LMFDB

endpoint

https://mcp.lmfdb.org/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing

karma

0 · newcomer

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.

• count_rows unknown never probed

  Count rows in a table, optionally with a WHERE clause. Args: table_name: The table to count (e.g. "ec_curvedata"). where: Optional WHERE clause without the WHERE keyword (e.g. "rank >= 2 AND conductor < 1000"). Returns: JSON with the count.

  mcp-tool

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  {
    "type": "object",
    "title": "count_rowsArguments",
    "required": [
      "table_name"
    ],
    "properties": {
      "where": {
        "type": "string",
        "title": "Where",
        "default": ""
      },
      "table_name": {
        "type": "string",
        "title": "Table Name"
      }
    }
  }

  arguments 18 lines copy
• overview unknown never probed

  Curated map of the LMFDB (L-functions and Modular Forms Database) by mathematical area — ALWAYS CALL THIS FIRST for any task involving number theory data. Covers these mathematical areas: elliptic curves over Q and over number fields, classical modular forms, Maass forms, Hilbert modular forms, Bianchi modular forms, genus 2 curves, higher genus families, L-functions, number fields, p-adic fields, Dirichlet characters, Artin representations, Galois groups, Sato-Tate groups, abstract groups, abelian varieties over finite fields, Belyi maps. Returns a sectioned JSON map of production tables grouped by mathematical area, with for each table: - the hub table of each section (the table to start joins from) - grain of each table (per-curve, per-isogeny-class, etc.) - canonical join columns (often the key to avoiding wrong joins) - format quirks where present Use this BEFORE search_knowls / describe_table / list_tables — it usually shortcuts the whole discovery workflow. Follow up with: - search_knowls(keywords): find the column storing a specific mathematical quantity when overview doesn't already tell you - describe_table(name): see the full schema of a specific table - run_sql(query): execute a SELECT query - count_rows, sample_rows, table_stats: convenience wrappers for common operations without writing SQL Takes no arguments. Returns: JSON object with shape: { "sections": [ { "section": "Elliptic curves over Q", "tables": [ {"name": "ec_curvedata", "notes": "Hub table ..."}, ... ] }, ... ] }

  mcp-tool

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  {
    "type": "object",
    "title": "overviewArguments",
    "properties": {}
  }

  arguments 5 lines copy
• list_tables unknown never probed

  List LMFDB tables with metadata (section, notes) when available. If you're starting a new task, prefer overview() — it gives the same metadata grouped by mathematical area and is usually more useful. Use list_tables when you need a flat list or when filtering by a prefix you already know. By default returns only production tables flagged visible through the MCP (status='production', mcp_visible=true in the lmfdb_tables metadata). Pass status='all' to also include tables that are not curated (test tables, beta tables, obsolete variants, etc.) — useful for developers and researchers who want access to work-in-progress data. Auxiliary tables ending in _counts or _stats (psycodict machinery) are always filtered out. Args: prefix: Optional table-name prefix. Common prefixes: ec (elliptic curves over Q), ec_nf (elliptic curves over number fields), nf (number fields), lf (p-adic fields), mf (classical modular forms), hmf (Hilbert modular forms), bmf (Bianchi modular forms), maass (Maass forms), g2c (genus 2 curves), hgcwa (higher genus families), av_fq (abelian varieties over Fq), belyi (Belyi maps), lfunc (L-functions), char (Dirichlet characters), artin (Artin representations), gps (groups — abstract, Galois, Sato-Tate). status: 'production' (default) returns only production-visible tables. 'all' returns every matching table including uncurated ones. Other values ('beta', 'alpha', 'obsolete') filter by that exact status value in lmfdb_tables. Returns: JSON array of {table_name, section, row_estimate, notes} objects. section and notes are null for tables not present in lmfdb_tables.

  mcp-tool

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  {
    "type": "object",
    "title": "list_tablesArguments",
    "properties": {
      "prefix": {
        "type": "string",
        "title": "Prefix",
        "default": ""
      },
      "status": {
        "type": "string",
        "title": "Status",
        "default": "production"
      }
    }
  }

  arguments 16 lines copy
• search_knowls unknown never probed

  Search LMFDB documentation for tables and columns matching the given keywords. Use this to discover which table or column contains a mathematical quantity you're looking for. This is typically the first tool to call when you know what mathematical data you want but don't know where it's stored. For example: - search_knowls("frobenius traces") finds columns storing a_p values - search_knowls("isogeny class") finds tables about isogeny classes - search_knowls("rational points") finds columns with rational point data - search_knowls("analytic rank") finds the relevant column(s) Args: query: One or more keywords (e.g. "frobenius traces", "conductor elliptic curve", "sato-tate"). limit: Maximum results to return (default 20, max 100). Returns: JSON array of {id, title, content, match_count} objects, ranked by how many query keywords match. IDs have the form "tables.<table_name>" or "columns.<table_name>.<column_name>".

  mcp-tool

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  {
    "type": "object",
    "title": "search_knowlsArguments",
    "required": [
      "query"
    ],
    "properties": {
      "limit": {
        "type": "integer",
        "title": "Limit",
        "default": 20
      },
      "query": {
        "type": "string",
        "title": "Query"
      }
    }
  }

  arguments 18 lines copy
• describe_table unknown never probed

  Show the column names, types, and (where available) documentation for a given LMFDB table. The output includes: - table_name - description: the contents of the knowl "tables.<table_name>", if any, describing what the table represents - columns: a list of {column_name, data_type, is_nullable, description, array_length} objects. "description" comes from the knowl "columns.<table_name>.<column_name>", if any. "array_length" is present for array-typed columns and reports the typical length sampled from a few rows (useful for columns that store a fixed number of values, e.g. aplist has length 25 for a_p with p < 100). Knowl content may contain LaTeX, markdown, and nested knowl references. Args: table_name: The table to describe (e.g. "ec_curvedata", "g2c_curves"). Returns: JSON object with keys: table_name, description, columns.

  mcp-tool

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  {
    "type": "object",
    "title": "describe_tableArguments",
    "required": [
      "table_name"
    ],
    "properties": {
      "table_name": {
        "type": "string",
        "title": "Table Name"
      }
    }
  }

  arguments 13 lines copy
• sample_rows unknown never probed

  Return a random sample of rows from a table to understand its contents. Args: table_name: The table to sample (e.g. "ec_curvedata"). n: Number of rows to return (default 5, max 50). Returns: JSON with columns and sample rows.

  mcp-tool

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  {
    "type": "object",
    "title": "sample_rowsArguments",
    "required": [
      "table_name"
    ],
    "properties": {
      "n": {
        "type": "integer",
        "title": "N",
        "default": 5
      },
      "table_name": {
        "type": "string",
        "title": "Table Name"
      }
    }
  }

  arguments 18 lines copy
• run_sql unknown never probed

  Run a read-only SQL SELECT query against the LMFDB database. This is the most flexible tool — use it for filtering, joining, aggregating, or computing statistics across LMFDB tables. Args: sql: A SELECT query. Only SELECT/WITH/EXPLAIN statements are allowed. Examples: - SELECT label, rank, conductor FROM ec_curvedata WHERE rank >= 3 LIMIT 20 - SELECT st_group, COUNT(*) FROM g2c_curves GROUP BY st_group - SELECT AVG(rank) FROM ec_curvedata WHERE torsion_order = 5 limit: Maximum rows to return (default 100, max 100000). Returns: JSON with 'columns', 'row_count', and 'rows'.

  mcp-tool

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  {
    "type": "object",
    "title": "run_sqlArguments",
    "required": [
      "sql"
    ],
    "properties": {
      "sql": {
        "type": "string",
        "title": "Sql"
      },
      "limit": {
        "type": "integer",
        "title": "Limit",
        "default": 100
      }
    }
  }

  arguments 18 lines copy
• table_stats unknown never probed

  Compute basic statistics for a numeric column in a table. For non-numeric columns, returns count and distinct count only. Args: table_name: The table (e.g. "ec_curvedata"). column: The column to analyze (e.g. "rank", "conductor"). where: Optional WHERE clause without the keyword. Returns: JSON with count, distinct_count, and (for numeric columns) min, max, avg, stddev.

  mcp-tool

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  {
    "type": "object",
    "title": "table_statsArguments",
    "required": [
      "table_name",
      "column"
    ],
    "properties": {
      "where": {
        "type": "string",
        "title": "Where",
        "default": ""
      },
      "column": {
        "type": "string",
        "title": "Column"
      },
      "table_name": {
        "type": "string",
        "title": "Table Name"
      }
    }
  }

  arguments 23 lines copy
• export_query unknown never probed

  Prepare a bulk download of a SELECT query's results. Returns a short- lived URL that streams the full result set as CSV or JSONL — the data does NOT go through the MCP conversation, so this is the right tool for anything destined for client-side analysis (pandas, numpy, plots, machine learning, etc.) rather than direct inspection. Typical use: ask the LLM to write a query joining several tables and selecting the columns you want, call export_query, then in the LLM's code-execution sandbox: import pandas as pd df = pd.read_csv("<returned url>") # or for JSONL: # df = pd.read_json("<returned url>", lines=True) ... analysis ... Compared to run_sql: - run_sql returns rows inline (capped at 100,000, meant for direct inspection; everything flows through the LLM's context window). - export_query returns a URL (default cap 100,000 rows, hard cap 1,000,000; data bypasses the LLM's context entirely). The download URL is valid for about 10 minutes and can be fetched multiple times within that window. Only SELECT / WITH / EXPLAIN queries are allowed (same safety rules as run_sql). Args: sql: A SELECT query. Same rules as run_sql. format: 'csv' (default, universal, types coerced to strings) or 'jsonl' (JSON Lines; preserves types; pandas reads via pd.read_json(url, lines=True)). max_rows: Maximum rows to export (default 100,000, hard cap 1,000,000). If the query has its own LIMIT, the smaller of the two is used. Returns: JSON object: { "url": "https://.../download/<token>", "format": "csv", "estimated_rows": 84321, # see note below; may be null "max_rows": 100000, # cap actually applied "estimate_exceeds_cap": false, # only present when true "expires_in_seconds": 600, "sandbox_hint": "import pandas as pd; df = pd.read_csv(url)" } Or {"error": "..."} on failure. Note on estimated_rows: this comes from Postgres's query planner (EXPLAIN) and reflects the estimated number of rows your WHERE/JOIN clauses match, ignoring any LIMIT. It assumes WHERE predicates are statistically independent, so for queries with correlated predicates — notably (rank, conductor) on ec_curvedata — it can be off by 10×– 1000× in either direction. Treat it as orientation only; the actual row count in the downloaded file is ground truth. When the estimate exceeds the applied max_rows cap, "estimate_exceeds_cap": true is included so you can decide whether to raise max_rows or tighten the query. estimated_rows is null only when EXPLAIN returns an unparseable plan.

  mcp-tool

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  {
    "type": "object",
    "title": "export_queryArguments",
    "required": [
      "sql"
    ],
    "properties": {
      "sql": {
        "type": "string",
        "title": "Sql"
      },
      "format": {
        "type": "string",
        "title": "Format",
        "default": "csv"
      },
      "max_rows": {
        "type": "integer",
        "title": "Max Rows",
        "default": 100000
      }
    }
  }

  arguments 23 lines copy

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