_ registry / mcp http-sse · checked 51m ago

sigma-data-model

https://sigma-data-model-mcp.onrender.com

Registry code: ae0af460198c4820

api record

Converts data models from dbt, Snowflake, LookML, Tableau, and Power BI into Sigma Computing data model format.

from a public catalogue that lists it, not from the operator

endpoint
https://sigma-data-model-mcp.onrender.com/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
422ms

last good check

priced tools
0

of 26 tools

_ answered our checks, 90 days 2 checks · signed record
  • unknown → live
_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 26 tools
1 open 25 never probed 1 of 26 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • get_sigma_data_model_schema open 3h ago

    Return the Sigma Computing data model JSON schema reference. Shows element types, columns, metrics, relationships, controls, and more. For detailed specs on specific components, read the MCP resources: sigma://data-model-spec/data-model — top-level structure, ID rules sigma://data-model-spec/column — column formulas, prefix rules per source type sigma://data-model-spec/metric — metric formulas (use source column names, not spec IDs) sigma://data-model-spec/source — all 7 source kinds overview sigma://data-model-spec/join-source — join source with name field requirements sigma://data-model-spec/table — full table element field reference sigma://data-model-spec/filter, /folder, /grouping, /control, /relationship — supporting types

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • convert_powerbi_to_sigma unknown never probed

    Convert a Power BI model (TOM JSON / .bim) to Sigma Computing data model JSON. Accepts the JSON content of a Power BI model — either a .bim file, a DataModelSchema from a .pbit, or TOM JSON from SSAS/Power BI Service. Handles tables, columns, DAX measures → Sigma metrics, DAX calculated columns, relationships, display folders, measures-only tables, and M expression path extraction for warehouse table sources. Complex DAX patterns (CALCULATE+ALL, iterators, time intelligence, VAR/RETURN) generate warnings with links to equivalent Sigma patterns.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "model_json",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "model_json": {
      "type": "string",
      "description": "Power BI model JSON content (.bim file, DataModelSchema, or TOM JSON)"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID (from GET /v2/connections); pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • convert_tableau_to_sigma unknown never probed

    Convert a Tableau workbook or data source to Sigma Computing data model JSON. Accepts raw XML content from .twb (workbook), .tds (data source), or the extracted XML from .twbx/.tdsx packaged files. Parses data sources, joins/relationships, calculated fields with formula conversion, LOD FIXED expressions → child elements with groupings, parameters → controls, and cross-element column reference auto-fixing. Supports federated/Excel-backed sources: when the Tableau source is not a direct warehouse connection, pass database and schema to set the warehouse path. Table names are uppercased automatically (Orders → ORDERS). Use table_mapping to override specific names when they differ in the warehouse. Table calculations: RUNNING_*/WINDOW_*/RANK*/LOOKUP/INDEX are lowered to SQL OVER-clause helper elements when the worksheet context allows; otherwise the WINPROBE-validated chart-context Sigma formula (Cumulative*/Moving*/Rank/ RankDense/RankPercentile/PercentOfTotal/RowNumber/Lag/Lead) is returned in `workbookPatterns` — those only work in GROUPED workbook elements (window functions silently error in DM calc columns/metrics). Untranslatable table calcs (WINDOW_MEDIAN/PERCENTILE/CORR/COVAR, PREVIOUS_VALUE, SIZE) are flagged loudly, never silently dropped. Detected RLS calcs are reported in `security` (not injected).

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "xml_content",
    "connection_id",
    "database",
    "schema",
    "datasource_index"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "xml_content": {
      "type": "string",
      "description": "Tableau XML content (.twb or .tds file content)"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    },
    "table_mapping": {
      "type": "string",
      "description": "Optional JSON map of Tableau table names to warehouse table names, e.g. {\"Orders\":\"ORDERS\",\"People\":\"PEOPLE\"}. Required when the Tableau source is Excel/flat-file and warehouse table names differ from the Tableau sheet names."
    },
    "datasource_index": {
      "type": "number",
      "description": "Which data source to convert, 0-indexed (pass 0 for default)"
    }
  },
  "additionalProperties": false
}
    arguments 38 lines
  • convert_sql_to_sigma_formula unknown never probed

    Convert a SQL expression to a Sigma Computing formula. Handles: CASE WHEN, DATEDIFF, DATEADD, ROUND, NULLIF, COALESCE, arithmetic, column refs (SNAKE_CASE → [Title Case]), IN lists, and more.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "sql"
  ],
  "properties": {
    "sql": {
      "type": "string",
      "description": "The SQL expression to convert"
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
  • convert_tableau_formula_to_sigma unknown never probed

    Convert a Tableau calculated field formula to a Sigma Computing formula. Handles: IF/ELSEIF/ELSE/END, CASE/WHEN, IIF, ZN, COUNTD, DATEPART, DATETRUNC, DATEADD, DATEDIFF. LOD expressions → comment placeholder. Table calcs use WINPROBE-validated mappings: RUNNING_* → Cumulative*; WINDOW_*(agg, -n, m) → Moving*; WINDOW_STDEV → MovingStdDev; agg/WINDOW_SUM(agg) → PercentOfTotal(agg, "grand_total"); RANK/RANK_DENSE/RANK_PERCENTILE → Rank/RankDense/RankPercentile(agg, "desc"); INDEX() → RowNumber(); LOOKUP(agg, ±n) → Lag/Lead(agg, n). CAVEAT (also emitted as a warning): these Sigma window functions only work in CHART/grouped-workbook-element context — they silently error in data-model element calc columns and workbook master calc columns. Never emits *Over functions. Untranslatable table calcs (WINDOW_MEDIAN/PERCENTILE/CORR/COVAR, PREVIOUS_VALUE, SIZE) return a loud warning naming the fragment.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "formula"
  ],
  "properties": {
    "formula": {
      "type": "string",
      "description": "The Tableau formula to convert"
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
  • format_sigma_display_name unknown never probed

    Convert a SNAKE_CASE identifier to Sigma display name format (Title Case).

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "identifier"
  ],
  "properties": {
    "identifier": {
      "type": "string",
      "description": "SNAKE_CASE identifier (e.g. \"ORDER_NUMBER\")"
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
  • convert_cube_to_sigma unknown never probed

    Convert Cube.dev schema files to Sigma Computing data model JSON. Accepts Cube schemas in YAML (.yml, .yaml) and JavaScript (.js) form. The JS parser supports cube(`name`, { ... }) / view(`name`, { ... }) call form, template literals (${CUBE}.col, ${OtherCube.dim}, ${measure_name}), and multi-line backtick SQL. Converts: - Cubes with sql_table → warehouse-table elements - Cubes with sql → Custom SQL elements (template refs are normalized) - Dimensions (string/number/time/boolean) → columns - Measures (count/sum/avg/min/max/count_distinct/number/percent) → metrics - Filtered measures (filters[].sql) → SumIf/CountIf/AvgIf wrappers - Joins (one_to_one/one_to_many/many_to_one) → relationships with FK/PK keys - Views (cubes[].join_path / includes / prefix) → derived elements Pre-aggregations and segments are skipped with informational warnings — Sigma uses warehouse-side caching and has no direct equivalent. Pass files as an array of {name, content} objects. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "files",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "files": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "content"
        ],
        "properties": {
          "name": {
            "type": "string",
            "description": "Filename (e.g. \"orders.yml\" or \"orders.js\"). Extension determines parser: .yml/.yaml → YAML, .js/.cjs/.mjs → JavaScript."
          },
          "content": {
            "type": "string",
            "description": "Full file content"
          }
        },
        "additionalProperties": false
      },
      "description": "Array of Cube schema files (YAML and/or JavaScript)"
    },
    "schema": {
      "type": "string",
      "description": "Override schema name (e.g. \"PUBLIC\"); pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name (e.g. \"ANALYTICS\"); pass empty string to omit"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID (from GET /v2/connections); pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 47 lines
  • convert_tableau_prep_to_sigma unknown never probed

    Convert a Tableau Prep flow (.tfl/.tflx) to Sigma Computing data model JSON. Accepts the unzipped `flow` JSON document from a .tfl/.tflx archive (a .tfl is a ZIP containing a file named `flow` plus display/metadata files). Handles: - Inputs (LoadSql, LoadCsv, LoadExcel, LoadJson, LoadHyper, LoadGoogle, LoadSqlProxy) - Containers (.v1.Container) — recursively flattened into the parent graph - Transform actions (linear chain → one Sigma element): AddColumn → calculated column (via tableauFormulaToSigma) RemoveColumns / KeepOnlyColumns → drop columns RenameColumn → column display name ChangeColumnType → cast wrapper Remap → nested If() chain FilterOperation → calculated boolean column "Filter: <name>" - SuperJoin (one_to_one / one_to_many / many_to_one inferred from joinType) → Sigma relationship with FK/PK keys - SuperUnion → Sigma element with source.kind:'union' + sub-element refs - SuperAggregate → child element with groupings + metrics - Output nodes (WriteToHyper / WriteToCsv / PublishExtract) → ignored LoadSqlProxy (Tableau Server published datasource) is emitted as a Custom SQL placeholder element — no direct warehouse reachability. File-based inputs (CSV, Excel, JSON, Hyper) are mapped to warehouse tables by basename. Use `table_mapping` to override (e.g. {"Orders_Central":"ORDERS"}). Pivot, Script, RunCommand, and Prediction nodes are skipped with warnings.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "flow_json",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name (e.g. \"PUBLIC\"); pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name (e.g. \"ANALYTICS\"); pass empty string to omit"
    },
    "flow_json": {
      "type": "string",
      "description": "The unzipped Tableau Prep flow JSON content (the file named \"flow\" inside the .tfl ZIP)"
    },
    "tds_files": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "content"
        ],
        "properties": {
          "name": {
            "type": "string",
            "description": "Filename (e.g. \"Product Catalog.tds\")"
          },
          "content": {
            "type": "string",
            "description": "Tableau .tds/.tdsx XML content (unzipped if .tdsx)"
          }
        },
        "additionalProperties": false
      },
      "description": "Optional companion Tableau .tds/.tdsx files. When a .tds caption matches a LoadSqlProxy.datasourceName in the flow, the placeholder Custom SQL stub is replaced with the actual relation (warehouse-table for type=\"table\" relations, or Custom SQL with the real SELECT body for type=\"text\" relations)."
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    },
    "table_mapping": {
      "type": "string",
      "description": "Optional JSON map of Prep input names → warehouse table names, e.g. {\"Orders_Central\":\"ORDERS\"}. Required when CSV/Excel inputs are used and the warehouse table names differ from the Prep input names."
    }
  },
  "additionalProperties": false
}
    arguments 55 lines
  • convert_dbt_to_sigma unknown never probed

    Convert dbt semantic model YAML to Sigma Computing data model JSON. Accepts dbt semantic_models YAML (from semantic_manifest.json, YAML files, or dbt Cloud). Handles entities, dimensions, measures, metrics, and foreign entity cross-references → Sigma relationships. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "yaml_content",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name (e.g. DBT_PROD); pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name (e.g. ANALYTICS); pass empty string to omit"
    },
    "yaml_content": {
      "type": "string",
      "description": "The dbt semantic model YAML content"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID (from GET /v2/connections); pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • convert_snowflake_to_sigma unknown never probed

    Convert Snowflake Semantic View YAML to Sigma Computing data model JSON. Accepts Snowflake Cortex Analyst semantic view YAML definitions. Handles tables with dimensions, time_dimensions, facts, primary keys, inline relationships, and top-level relationships. Facts auto-generate Sum() metrics.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "yaml_content",
    "connection_id",
    "auto_metrics"
  ],
  "properties": {
    "auto_metrics": {
      "type": "boolean",
      "description": "Auto-generate Sum() metrics for fact columns (pass true unless you want to skip metrics)"
    },
    "yaml_content": {
      "type": "string",
      "description": "The Snowflake semantic view YAML content"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 24 lines
  • convert_lookml_to_sigma unknown never probed

    Convert LookML project files to Sigma Computing data model JSON. Provide one or more LookML files (views + model). Parses LookML, resolves explores/joins, converts dimensions → columns, measures → metrics, sql_on → relationships, and derived_table → Custom SQL elements. View-only input (no .model.lkml) is supported: each view converts to a standalone element (no relationships — joins live in the model file). {view.SQL_TABLE_NAME{'}'} substitution is fully resolved including N-hop alias chains and PDT-referencing-PDT patterns. Referenced derived views in the parse set are inlined as WITH CTEs (CTE-continuation fragments — derived SQL starting with ", name AS (" — are completed/promoted to a full WITH). References to views NOT in the input become LOOKER_SCRATCH.<VIEW> placeholder tables with a LOUD 🔶 warning naming the unresolved view. include: directives are parsed and listed in warnings — resolution is limited to the files provided. Persistence config is never silent: datagroup_trigger / sql_trigger_value / persist_for / increment_key emit warnings recommending Sigma scheduled materialization on the element; {% incrementcondition %} Liquid is replaced with 1=1 (full scan) plus the same materialization recommendation. Other warehouse hints (distribution, sortkeys, cluster_keys, …) warn informationally. Pass files as an array of {name, content} objects.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "files",
    "connection_id",
    "explore_name",
    "join_strategy"
  ],
  "properties": {
    "files": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "content"
        ],
        "properties": {
          "name": {
            "type": "string",
            "description": "Filename (e.g. \"sales.model.lkml\" or \"orders.view.lkml\")"
          },
          "content": {
            "type": "string",
            "description": "Full file content"
          }
        },
        "additionalProperties": false
      },
      "description": "Array of LookML files to parse"
    },
    "explore_name": {
      "type": "string",
      "description": "Name of the explore to convert; pass empty string to auto-detect (uses first explore found)"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    },
    "join_strategy": {
      "type": "string",
      "description": "How to handle joins: \"relationships\" (lazy), \"joins\" (eager physical), \"auto\", or \"\" for default"
    }
  },
  "additionalProperties": false
}
    arguments 47 lines
  • convert_cognos_to_sigma unknown never probed

    Convert an IBM Cognos Analytics **Data Module** (JSON) to a Sigma data model. Input is the Data Module JSON from `GET /bi/v1/metadata/modules/{id}` (CA 11.x+). Query subjects → table elements; query items → columns (facts with a regularAggregate → metrics); calculations → Sigma formulas via the Cognos expression DSL (total..for → *Over, if/then/else → If, _add_days/_days_between/extract → date fns, substitute → RegexpReplace, substr → Mid, cast/varchar/char → Text, || → &); relationships → DM relationships (join columns from link[].leftRef/rightRef, source = the many side). Flags (does not fake): runtime macros, running-total/rank/lag/lead, GetResourceString localization, and composite/non-equi joins.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "module_json",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override warehouse schema; empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override warehouse database; empty string to omit"
    },
    "module_json": {
      "type": "string",
      "description": "Cognos Data Module JSON (the metadata/modules/{id} response)"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • convert_cognos_report_to_sigma unknown never probed

    Convert an IBM Cognos **report specification** (XML) to a Sigma workbook. Input is the report XML from `GET /bi/v1/objects/{id}?fields=specification`. Each <list> → a Sigma table element sourced from the migrated data model (auto-aggregated lists → grouped tables); <singleton> → kpi-chart; <crosstab> → pivot-table; RAVE2 <vizControl> charts → Sigma chart elements (incl. tiledmap → region/point map); dataItems → columns (expressions translated, model refs resolved to [Subject/Col], dataFormats → Sigma column formats incl. scaled $K/$M); prompts → segmented controls (values/defaults recovered from <selectValue>/customControl); runtime macros (# prompt('p','token',…) #, e.g. swap-measure pickers) → segmented control + controlId-wired Switch when the value set is recoverable; detail filters → element filters (?prompt? → control + boolean match column). Pass data_model_id (from posting the converted Data Module) to wire the table sources. Flags (not converted): drill-through → actions, conditional render blocks, master-detail, summary (post-aggregation) filters, unrecoverable macros.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "report_xml",
    "data_model_id"
  ],
  "properties": {
    "report_xml": {
      "type": "string",
      "description": "Cognos report-spec XML (the specification string)"
    },
    "data_model_id": {
      "type": "string",
      "description": "Sigma dataModelId of the migrated Cognos Data Module; empty string leaves a placeholder"
    }
  },
  "additionalProperties": false
}
    arguments 19 lines
  • convert_omni_to_sigma unknown never probed

    Convert Omni Analytics model files to Sigma Computing data model JSON. Accepts .view.yaml files (dimensions + measures) and .model.yaml files (explores + joins). Pass multiple files together — views and explores are merged and resolved automatically. Converts: - Views → Sigma elements with warehouse table paths - Dimensions → Sigma columns; ${TABLE}.col and ${field} refs translated - type: time dimensions → expanded per timeframe using DateTrunc() - Measures → Sigma metrics (sum, avg, min, max, count_distinct, count) - Explores/joins (sql_on + foreign_key) → Sigma relationships with FK/PK keys - CASE WHEN, IN (...), SQL functions → Sigma formula syntax The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "files",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "files": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "content"
        ],
        "properties": {
          "name": {
            "type": "string",
            "description": "Filename (e.g. \"orders.view.yaml\" or \"retail_analytics.model.yaml\")"
          },
          "content": {
            "type": "string",
            "description": "Full file content"
          }
        },
        "additionalProperties": false
      },
      "description": "Array of Omni YAML files (.view.yaml and/or .model.yaml)"
    },
    "schema": {
      "type": "string",
      "description": "Override schema name (e.g. \"PUBLIC\"); pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name (e.g. \"ANALYTICS\"); pass empty string to omit"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID (from GET /v2/connections); pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 47 lines
  • convert_sql_to_sigma unknown never probed

    Convert SQL SELECT statements to Sigma Computing data model JSON. Parses SQL SELECT statements (including explicit JOINs and aggregate functions) and generates a Sigma data model with warehouse elements, relationships, and a derived view element that surfaces all SELECT columns. Supports: - Explicit JOIN ON / JOIN USING → Sigma relationships with FK/PK column IDs - SUM/COUNT/AVG/MIN/MAX → Sigma metrics - CTEs (uses the last top-level SELECT, ignores CTE bodies) - Dot-qualified columns (t.col) → attributed to the correct warehouse element - DISTINCT / ALL SELECT modifiers - Multi-table models (pass multiple statements) Complex queries (subqueries in FROM, implicit cross-joins) fall back to a Custom SQL element with inferred column names. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "statements",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name (e.g. \"PUBLIC\"); pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name (e.g. \"ANALYTICS\"); pass empty string to omit"
    },
    "statements": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "sql"
        ],
        "properties": {
          "sql": {
            "type": "string",
            "description": "The SQL SELECT statement to convert"
          },
          "name": {
            "type": "string",
            "description": "Human-readable name for this query (used as derived element name)"
          }
        },
        "additionalProperties": false
      },
      "description": "Array of named SQL SELECT statements to convert"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID (from GET /v2/connections); pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 47 lines
  • parse_lookml unknown never probed

    Parse a LookML file and return its structured representation as JSON. Useful for inspecting views, explores, dimensions, measures, joins before conversion.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "content"
  ],
  "properties": {
    "content": {
      "type": "string",
      "description": "LookML file content to parse"
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
  • diagnose_sigma_save_error unknown never probed

    Identify which SQL element in a Sigma data model caused a save error. When Sigma returns a syntax error like "syntax error line 49 at position 24 unexpected '('" it only provides a connection ID, not which element failed. This tool parses the error, scans all Custom SQL elements in the model JSON, and pinpoints the likely culprit by matching line number and error token position. Returns: - A list of candidate elements (those whose SQL has >= errorLine lines) - The exact line content at the error line for each candidate - A "likely culprit" flag on the element where the error token appears at errorPosition

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "error_message",
    "model_json"
  ],
  "properties": {
    "model_json": {
      "type": "string",
      "description": "The full Sigma data model JSON string (the output from a converter or GET /v2/dataModels/{id}/spec)"
    },
    "error_message": {
      "type": "string",
      "description": "The full Sigma error message, e.g. \"syntax error line 49 at position 24 unexpected '('\""
    }
  },
  "additionalProperties": false
}
    arguments 19 lines
  • validate_dm_columns unknown never probed

    Fetch the columns of a saved Sigma data model and surface any with type.type === "error". Use this immediately after a successful POST /v2/dataModels/spec to detect column-level errors that the POST itself reports as success but that fail at query time. Common causes: - Unknown DM formula function (e.g. SumOver, LagOver, RankOver — these are not Sigma DM functions) - Arity mismatch (e.g. CumulativeSum([x], [y], [z]) — only takes 1 arg) - Reference to a column that doesn't exist in the element Returns the list of errored columns with their element ID, column name, formula, and Sigma's error message (when available). Required environment: SIGMA_BASE_URL (e.g. https://aws-api.sigmacomputing.com) and a Bearer token; pass the token explicitly via the access_token parameter.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "data_model_id",
    "access_token"
  ],
  "properties": {
    "base_url": {
      "type": "string",
      "description": "Sigma API base URL. Defaults to https://aws-api.sigmacomputing.com"
    },
    "access_token": {
      "type": "string",
      "description": "A Bearer access token (obtained via POST /v2/auth/token grant_type=client_credentials)"
    },
    "data_model_id": {
      "type": "string",
      "description": "The data model ID (UUID) returned by POST /v2/dataModels/spec"
    }
  },
  "additionalProperties": false
}
    arguments 23 lines
  • convert_thoughtspot_to_sigma unknown never probed

    Convert a ThoughtSpot TML (YAML) worksheet or model to Sigma Computing data model JSON. Accepts ThoughtSpot Table/Worksheet TML YAML — the format exported from ThoughtSpot's Develop > TML interface. Handles table_paths (physical table aliases), worksheet_columns with "ALIAS::column" separator, formula columns, and joins with SQL ON clause parsing. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "tml_yaml",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "tml_yaml": {
      "type": "string",
      "description": "ThoughtSpot TML YAML content (worksheet or table format)"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID (from GET /v2/connections); pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • convert_qlik_to_sigma unknown never probed

    Convert a Qlik Sense app metadata JSON to Sigma Computing data model JSON. Accepts Qlik Engine API "qtr" format or REST API "tables" format. Handles tables with fields → warehouse elements, shared field names → relationships, master measures → metrics, master dimensions → calculated columns. Qlik Set Analysis is translated to conditional aggregation where possible (alternate states / $-expansions / P()/E() degrade with warnings). Inter-record expressions translate as: Rank/Above/Below/Previous/Peek → Sigma Rank/Lag/Lead formulas returned in `workbookPatterns` (they only work in GROUPED workbook elements — window functions silently error in DM calc columns/metrics); FirstSortedValue → a SQL QUALIFY helper element (or the Rank=n-filter pattern); HRank and other pivot column-axis functions are flagged untranslatable. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "model_json",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "model_json": {
      "type": "string",
      "description": "Qlik app metadata JSON (qtr format or REST tables array)"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • convert_qlikview_prj_to_sigma unknown never probed

    Convert a QlikView ".qvw" app to a Sigma Computing DATA MODEL JSON via its "-prj" project folder. QlikView .qvw binaries have no parser; the migration path is the developer-opt-in <name>-prj/ folder (QlikView Desktop "Create project folder"). Pass the folder's files as a JSON array of { name, content }. This builds the data model from LoadScript.txt only: - LoadScript.txt → tables + (post-rename) fields → warehouse elements + relationships (a lib://.../TABLE.qvd load resolves to warehouse table TABLE). Relationships are inferred from shared field names only (a -prj folder carries no row counts) — review join directions. POST the output to /v2/dataModels/spec. NOTE: chart expressions/measures and the workbook/sheet layout (CH*.xml, QlikViewProject.xml) are NOT parsed here — the qlik-to-sigma migration skill's qlik-prj-discover.py handles those for a full data-model + workbook build.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "prj_files_json",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    },
    "prj_files_json": {
      "type": "string",
      "description": "JSON array of the -prj folder files: [{ \"name\": \"LoadScript.txt\", \"content\": \"...\" }, { \"name\": \"CH01.xml\", \"content\": \"...\" }, ...]"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • convert_atlan_to_sigma unknown never probed

    Convert an Atlan Data Contract (YAML or JSON) to Sigma Computing data model JSON. Accepts an Atlan data contract document. Reads the "models" object to create one Sigma element per model, auto-generates Sum() metrics for numeric columns, and builds relationships from "field.references" in "model.column" format. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "contract_text",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    },
    "contract_text": {
      "type": "string",
      "description": "Atlan data contract content (YAML or JSON string)"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • convert_alteryx_to_sigma unknown never probed

    Convert an Alteryx Designer workflow (.yxmd XML) to Sigma Computing data model JSON. Parses the Alteryx workflow XML. DbFileInput tools → warehouse elements, Join tools → relationships (traced back to source inputs via connection graph), Formula tools → calculated columns, Summarize tools → metrics. Cross-element column references are dropped with warnings since Sigma metrics must reference columns within the same element. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "xml_content",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "xml_content": {
      "type": "string",
      "description": "Alteryx workflow XML content (.yxmd file content)"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • convert_quicksight_to_sigma unknown never probed

    Convert AWS QuickSight asset exports to Sigma Computing data model JSON. Accepts one or more JSON files, auto-detected by content: - DescribeAnalysisDefinition response (analysis JSON: paste output of `aws quicksight describe-analysis-definition --account-id X --analysis-id Y`) - DescribeDataSet response (dataset JSON: paste output of `aws quicksight describe-data-set --account-id X --data-set-id Z`) For an analysis: pass the analysis JSON plus one DescribeDataSet JSON per referenced dataset (the analysis's DataSetIdentifierDeclarations list points at dataset ARNs by reference; without the matching dataset JSONs the converter emits a Custom-SQL stub element so calc fields have a home). For an asset-bundle .qs export: unzip first and pass each `analyses/*.json` and `datasets/*.json` file individually. Translation: - PhysicalTable.RelationalTable → warehouse-table element - PhysicalTable.CustomSql → Custom SQL element - PhysicalTable.S3Source / SaaSTable → Custom SQL placeholder (no Sigma equivalent) - LogicalTable.JoinInstruction → Sigma relationship on the left-side element - LogicalTable.DataTransforms: CreateColumns → calc col, RenameColumn → display name, CastColumnType → cast wrapper, FilterOperation → boolean calc col, ProjectOperation → column order, TagColumn → warning - AnalysisDefinition.CalculatedFields → calc cols on the bound element - ParameterDeclarations → Sigma controls - Window/table-calc QS functions (sumOver, runningSum, lag, lead, rank, etc.) → comment placeholder + warning (Sigma DM cols silently error on windows) Pass files as an array of {name, content} objects.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "files",
    "connection_id",
    "database",
    "schema"
  ],
  "properties": {
    "files": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "content"
        ],
        "properties": {
          "name": {
            "type": "string",
            "description": "Filename (e.g. \"my-analysis.json\" or \"orders-dataset.json\"). The converter classifies by content, not extension."
          },
          "content": {
            "type": "string",
            "description": "Full JSON content (DescribeAnalysisDefinition or DescribeDataSet response)"
          }
        },
        "additionalProperties": false
      },
      "description": "Array of QuickSight asset JSON files"
    },
    "schema": {
      "type": "string",
      "description": "Override schema name (used when RelationalTable.Schema is missing); pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name (used when RelationalTable.Catalog is missing); pass empty string to omit"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 47 lines
  • convert_oac_to_sigma unknown never probed

    Convert Oracle Analytics Cloud (OAC) logical tables JSON to Sigma Computing data model JSON. Accepts an array of OAC logical table objects (SMML export format). Each table has logicalColumns and logicalTableSources. Handles physical vs derived column mappings, aggregation rules → metrics, and logical joins → relationships. Pass an optional physicalMap for DB/schema metadata when the OAC model does not include full path information. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "tables_json",
    "connection_id",
    "database",
    "schema",
    "physical_map_json"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "tables_json": {
      "type": "string",
      "description": "JSON array of OAC logical table objects (SMML export)"
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    },
    "physical_map_json": {
      "type": "string",
      "description": "Optional JSON object mapping table name → {database, schema}; pass empty string to omit"
    }
  },
  "additionalProperties": false
}
    arguments 34 lines
  • convert_bobj_to_sigma unknown never probed

    Convert a SAP BusinessObjects Universe to Sigma Computing data model JSON. Accepts EITHER (auto-detected — a leading "<" means XML): • BI RESTful Web Service (RWS) universe JSON — GET /biprws/sl/v1/universes/{id} on an on-prem BO 4.x server. This is the business OUTLINE only: object names, datatypes, folders. The REST endpoint does NOT return object SELECT/WHERE expressions or the data foundation (physical tables/columns/joins). • Semantic Layer SDK / Information Design Tool XML export of the data foundation + business layer — REQUIRED to migrate the actual warehouse columns and calculations, since RWS JSON lacks them. (See the businessobjects-to-sigma skill's scripts/extract-universe-sdk.groovy extractor recipe.) The ingest is tolerant of the common RWS JSON shape variants (nested outline/items, class/objects, or a flat objects[] array) and of the SDK/IDT XML tag/attribute variants. Mapping: physical tables → warehouse-table elements; dimensions/details → columns (business names preserved); measures → metrics (Sum/Count/Avg/...); object expressions → calculated columns; joins → relationships. Predefined filters and universe @-functions (@Prompt/@Select/@Variable/@Aggregate_Aware) are surfaced as warnings. If the warehouse was restructured vs. the universe (renamed or consolidated tables — e.g. a "platinum" layer), pass table_map / column_map to repoint the output at the new physical names. Unmatched map keys are surfaced as warnings. The output JSON can be POSTed to the Sigma API (POST /v2/dataModels/spec) to create a data model, or PUT to /v2/dataModels/{id}/spec to update one.

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "universe_json",
    "connection_id",
    "database",
    "schema",
    "table_map",
    "column_map"
  ],
  "properties": {
    "schema": {
      "type": "string",
      "description": "Override schema name; pass empty string to omit"
    },
    "database": {
      "type": "string",
      "description": "Override database name; pass empty string to omit"
    },
    "table_map": {
      "type": "string",
      "description": "Optional JSON object remapping universe tables to your restructured/platinum-layer tables, e.g. {\"CUSTOMER_DIM_DE\":\"DIM_CUSTOMER\",\"CUSTOMER_DIM_AT\":{\"table\":\"DIM_CUSTOMER\",\"schema\":\"PLATINUM\"}}. Many old tables may map to one. Pass empty string to omit."
    },
    "column_map": {
      "type": "string",
      "description": "Optional JSON object remapping physical columns, keyed \"OLD_TABLE.OLD_COL\" or \"*.OLD_COL\" (any table) → new column name, e.g. {\"CUSTOMER_DIM_DE.CUST_NAME\":\"CUSTOMER_NAME\"}. Pass empty string to omit."
    },
    "connection_id": {
      "type": "string",
      "description": "Sigma connection UUID; pass empty string to omit"
    },
    "universe_json": {
      "type": "string",
      "description": "BusinessObjects universe — RWS JSON (/sl/v1/universes/{id} format) OR a Semantic-Layer-SDK / IDT XML export (data foundation + business layer). Auto-detected by a leading \"<\"."
    }
  },
  "additionalProperties": false
}
    arguments 39 lines
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