latlong-maps

endpoint

https://geocp.latlong.ai/mcp/

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is latlong-maps live?

Yes — it answered the hub's last check (checked 27m ago). It answered 99% of checks over the last 30 days.

Is latlong-maps free to use?

Not anonymously — it asks for a key or a login before it will serve. Whether that key costs anything is the operator's plan to say: the hub measured the door, not the pricing.

What tools does latlong-maps have?

8 tools: upload_file, get_capabilities, compute_column, geocode, pincode_lookup, reverse_geocode, territory_mapping, question_to_map.

Is latlong-maps safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

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.

• upload_file changes data auth-required never probed

  Upload a CSV or XLSX file for server-side ingestion under policy (auth, size cap, malware scan, injected-intent scan) and receive a file_key. This is the first step for ANY tool that works on the user's file: question_to_map, geocode, reverse_geocode, pincode_lookup and compute_column all take the returned file_key. When the user names a local CSV/XLSX and wants results added to it (pincodes, cities, coordinates, a computed column), upload it here rather than reading it yourself. Step 1: Call upload_file with file_name, file_size, and question. The response contains a signed upload_url and a curl command — run the curl command to POST the file bytes. The curl response contains file_key, columns, a row preview, and optionally 'intent' (geo-match service detected geo_column, geo_level, value_column, map_type, confidence) and 'guidance' (a ready-to-fire question_to_map action with all detected parameters pre-filled). Forward the intent fields into question_to_map to skip auto-detection and render immediately. For multi-sheet XLSX: the curl response is 'sheet_selection_required' with a file_key and sheets list. Pass file_key and sheet to question_to_map (or any other tool that accepts file_key) — do not call upload_file again. After upload, pass the returned file_key to question_to_map to render a map from the full dataset. The file_key is reusable — for additional questions on the same file, pass the same file_key again without re-uploading. When the upload response includes 'guidance', use its next_actions directly for a zero-round-trip render.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "file_name",
      "file_size"
    ],
    "properties": {
      "sheet": {
        "type": "string"
      },
      "file_key": {
        "type": "string"
      },
      "question": {
        "type": "string"
      },
      "file_name": {
        "type": "string"
      },
      "file_size": {
        "type": "integer"
      }
    },
    "additionalProperties": false
  }

  arguments 25 lines copy
• get_capabilities reads auth-required 7h ago

  Return the server's capability matrix: supported map types, geo types, output formats, classification methods, and the list of tools available in this process (which varies by configuration). Call this first to discover what the server can do.

  mcp-tool

  1234

  {
    "type": "object",
    "additionalProperties": false
  }

  arguments 4 lines copy
• compute_column auth-required never probed

  Add a column computed from arithmetic over an uploaded file's numeric columns, then map it. Pass file_key, expression and result_column, e.g. {"file_key": "...", "expression": "(revenue - cost) / population", "result_column": "profit_per_capita"}. Operators: + - * / % and parentheses, or a single comparison (> < >= <= ==) between two operands. Column names are matched case-insensitively and may contain spaces; quote a name that looks like a number with backticks, e.g. `2023` - `2020`. A row whose referenced cells are blank or a missing-data marker (-, NA, N/A), or that divides by zero, gets an empty value. The uploaded file is never changed: the reply carries a NEW file_key holding a copy with the column added. To map it, call question_to_map with that file_key and value_column = result_column. Use this instead of calculating values yourself. If it is refused, tell the user the error; do not calculate the values yourself or upload a rewritten file.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "file_key",
      "expression"
    ],
    "properties": {
      "sheet": {
        "type": "string"
      },
      "file_key": {
        "type": "string"
      },
      "expression": {
        "type": "string",
        "description": "arithmetic over column names, e.g. (revenue - cost) / population; operators + - * / % and parentheses, or one comparison > < >= <= =="
      },
      "result_column": {
        "type": "string"
      }
    },
    "additionalProperties": false
  }

  arguments 23 lines copy
• geocode reads auth-required never probed

  Geocode addresses from an uploaded CSV or XLSX file into latitude/longitude. Pass a file_key from upload_file. Single place, no file: pass only address (e.g. {"address": "India Gate, New Delhi"}). The reply carries latitude, longitude, address, pincode, city, district, state and landmark. status=not_found means no match; tell the user rather than answering yourself. Column selection: if you know it, pass address_column (or address_columns for several). Otherwise the server asks the geo-match service to detect it. When the answer is not unambiguous — several address columns, a weak match, or only structured columns like District/State/PIN that must be combined — the response is status=clarification_required with candidates and ready-to-fire guidance actions. Present the choice to the user and re-call with their answer; never invent a column name. The result overwrites the same file_key as a new version, plus a small preview, not the full rows. Pass that file_key to question_to_map to map the coordinates, or hand the user file_url (a signed download link, when present) to fetch the file directly.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "sheet": {
        "type": "string"
      },
      "job_id": {
        "type": "string"
      },
      "address": {
        "type": "string"
      },
      "file_key": {
        "type": "string"
      },
      "force_new": {
        "type": "boolean"
      },
      "address_column": {
        "type": "string"
      },
      "address_columns": {
        "type": [
          "null",
          "array"
        ],
        "items": {
          "type": "string"
        }
      },
      "compose_columns": {
        "type": [
          "null",
          "array"
        ],
        "items": {
          "type": "string"
        }
      }
    },
    "additionalProperties": false
  }

  arguments 42 lines copy
• pincode_lookup auth-required 10h ago

  Look up Indian pincodes: city, district and state. One pincode, no file: {"pincode": "560019"} also returns the post offices it covers with their coordinates. status=not_found means the pincode does not exist; tell the user rather than answering yourself. A file (including a local CSV the user names): upload it with upload_file first, then pass file_key (and pincode_column if you know it). Do not look the rows up one by one or write the result file yourself — this tool writes it. Every row gains city, district, state and pincode_status columns; the file is written back over file_key as a new version and file_url is a download link. Show the user the preview and offer the link. Each unique pincode costs one lookup. A clarification_required reply asks which column holds the pincodes. Large files return status=accepted with a job_id: poll {"job_id": "..."} until completed.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "sheet": {
        "type": "string"
      },
      "job_id": {
        "type": "string"
      },
      "pincode": {
        "description": "one 6-digit Indian pincode, e.g. \"560019\" (a number is accepted too)"
      },
      "file_key": {
        "type": "string"
      },
      "force_new": {
        "type": "boolean"
      },
      "pincode_column": {
        "type": "string"
      }
    },
    "additionalProperties": false
  }

  arguments 24 lines copy
• reverse_geocode reads auth-required never probed

  Turn latitude/longitude coordinates in an uploaded CSV or XLSX into addresses. Pass a file_key from upload_file. Single point, no file: pass only latitude and longitude in decimal degrees (e.g. {"latitude": 28.6129, "longitude": 77.2295}). The reply carries address, pincode, city, district, state and landmark. status=not_found means no match; tell the user rather than answering yourself. Column selection: pass latitude_column and longitude_column, or latlong_column for a single combined column, or coordinate_pairs for several sets. Otherwise the server detects them. When the answer is not unambiguous — several coordinate columns, a weak match, coordinates outside the supported region, or coarse whole-number values — the response is status=clarification_required. Present the choice to the user and re-call with their answer. Results are written back into the uploaded file: the response carries file_key, a signed file_url, and a small preview. Each pair adds address, pincode, landmark and status columns.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "sheet": {
        "type": "string"
      },
      "job_id": {
        "type": "string"
      },
      "file_key": {
        "type": "string"
      },
      "latitude": {
        "description": "one point's latitude in decimal degrees, e.g. 28.6129 (a string is accepted too)"
      },
      "force_new": {
        "type": "boolean"
      },
      "longitude": {
        "description": "one point's longitude in decimal degrees, e.g. 77.2295 (a string is accepted too)"
      },
      "latlong_column": {
        "type": "string"
      },
      "latitude_column": {
        "type": "string"
      },
      "coordinate_pairs": {
        "type": [
          "null",
          "array"
        ],
        "items": {
          "type": "object",
          "properties": {
            "latlong_column": {
              "type": "string"
            },
            "latitude_column": {
              "type": "string"
            },
            "longitude_column": {
              "type": "string"
            }
          },
          "additionalProperties": false
        }
      },
      "longitude_column": {
        "type": "string"
      },
      "accept_low_precision": {
        "type": "boolean"
      }
    },
    "additionalProperties": false
  }

  arguments 57 lines copy
• territory_mapping auth-required never probed

  Run the territory-management pipeline on a branch file uploaded via upload_file. Hybrid async with three modes: pass file_key to START a job (returns a job_id immediately); pass only job_id on subsequent calls to POLL progress; pass job_id AND catchment_type together to RESUME a job that paused for clarification. The pipeline validates that all branches are in one state, computes each branch's isochrone at catchment_distance_km, finds villages (catchment_type='village') or pincodes (catchment_type='pincode') that overlap the isochrone by ≥50%. A village/pincode inside several catchments is kept under EVERY branch (never deduplicated), and the last column closest_branch names the nearest branch by driving distance for those shared units (blank otherwise). Each catchment is split into 5 wedges of 72° (group_number 1-5); each wedge is split into bunches of 1-5 neighbouring units (bunch_number 1..N); each bunch is sequenced via the Trips API (trip_number 1-5); s_no numbers each branch's rows 1..N in that order. Returns a signed URL to an XLSX (download_url) with a Territory sheet of rows and a Maps sheet, and maps: one SVG per branch (map_url) with the branch at the centre, its units shaded by group_number and labelled by s_no, overlaps hatched green (this branch is closest) or red (another branch is closer); render_map=false skips the maps. Optional at start: style_request (the user's words for the map colours, e.g. 'groups in pink shades'; only the 5 group colours and the near/far hatch colours change, applied exactly as asked, pincode maps only) and map_title (replaces the map title). If requested colours make the hatching hard to see, warnings carry a suggestion to pass on to the user. catchment_type is OPTIONAL at start: if omitted, the geo-match service detects the branch file's place columns (a pincode column means pincode, a village column means village), and a catchment_type / area_type / mapping_type column also counts (must be uniform across rows). If the file is unclear (several such columns) or neither the caller nor the file specifies it — or they disagree — the job pauses in phase=awaiting_catchment_type and the poll response carries clarification_needed=true with a clarification_message instead of an error; answer by calling again with that same job_id plus the chosen catchment_type to resume. A file with mixed village/pincode values in its own column fails outright with AMBIGUOUS_CATCHMENT_TYPE. While a job runs, each response carries poll_after_seconds=10: wait 10 seconds, then poll again. Expect phase transitions: validating → isochroning → overlapping → grouping → bunching → routing → distancing → rendering_map → writing_output → complete (or awaiting_catchment_type, from which a resume call re-enters at validating). Flow: upload_file → upload bytes → territory_mapping{file_key,…} → territory_mapping{job_id} until phase=complete or phase=awaiting_catchment_type → if awaiting, territory_mapping{job_id, catchment_type} to resume → territory_mapping{job_id} until phase=complete.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "job_id": {
        "type": "string"
      },
      "file_key": {
        "type": "string"
      },
      "map_title": {
        "type": "string"
      },
      "render_map": {
        "type": [
          "null",
          "boolean"
        ]
      },
      "style_request": {
        "type": "string"
      },
      "catchment_type": {
        "type": "string"
      },
      "catchment_distance_km": {
        "type": "number"
      }
    },
    "additionalProperties": false
  }

  arguments 30 lines copy
• question_to_map reads auth-required never probed

  Render a geographic map from your data. Call upload_file first to ingest a CSV/XLSX file, then pass the returned file_key: {"question": "show total by pincode", "file_key": "mcp-uploads/<user-id>/2026/01/15/<uuid>.csv"}. The file_key is reusable — for additional questions on the same file, pass the same file_key again without re-uploading. Alternatively, pass rows directly as 'data' (e.g. from the upload_file preview): {"question": "show total by pincode", "data": [<rows>]} geo_column and value_column are preferred if easily identifiable from the column names, but not required — the server auto-detects them using a geo-match service that matches your data against known Indian geographies (states, districts, ACs, PCs, pincodes, RTOs, cities). Hints: geo_column usually contains place names (state, district, city, pincode, constituency) or numeric codes (pincode=560049); value_column usually contains the metric to plot (sales, count, total, percentage) or a category (party affiliation, status, type). If value_column is 'total' and doesn't exist in data, all numeric columns are summed automatically. String value columns render as categorical maps (each distinct value gets its own color, e.g. party affiliation). If the server cannot determine which columns to use, it returns a 'clarification_required' response with suggested columns and guidance — pick a geo_column and value_column from the suggestions and re-call. Supported geo types: state, district, pincode, rto, city, ac, pc. Points mode: if your file has latitude/longitude columns (e.g. from a prior geocoding step, or coordinates you already had) and your question asks to plot/pin/mark locations ("plot my stores", "show me the outlet locations", "mark these points"), the server detects the coordinate columns automatically and renders markers instead of shaded regions — no geo_column/value_column needed. The boundary lines drawn beneath the markers and how far the map zooms are chosen automatically from where the points actually fall (tightens to a state or district for a local cluster, falls back to all-India for a nationwide spread); you can also name a level explicitly ("plot by assembly constituency", "rto wise") to force it. A response may report points that fall outside every boundary (e.g. offshore or swapped lat/lon) — check for that in the warnings.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "question"
    ],
    "properties": {
      "data": {
        "type": [
          "null",
          "array"
        ],
        "items": {
          "type": "object",
          "additionalProperties": true
        }
      },
      "sheet": {
        "type": "string"
      },
      "style": {
        "type": "object",
        "additionalProperties": true
      },
      "table": {
        "type": "string"
      },
      "file_key": {
        "type": "string"
      },
      "map_type": {
        "type": "string"
      },
      "question": {
        "type": "string"
      },
      "geo_level": {
        "type": "string"
      },
      "geo_column": {
        "type": "string"
      },
      "state_hint": {
        "type": [
          "null",
          "array"
        ],
        "items": {
          "type": "string"
        }
      },
      "enrich_with": {
        "type": [
          "null",
          "object"
        ],
        "required": [
          "table",
          "columns"
        ],
        "properties": {
          "table": {
            "type": "string"
          },
          "columns": {
            "type": [
              "null",
              "array"
            ],
            "items": {
              "type": "string"
            }
          },
          "join_on": {
            "type": "string"
          }
        },
        "additionalProperties": false
      },
      "detected_geo": {
        "type": [
          "null",
          "object"
        ],
        "required": [
          "column",
          "geo_type",
          "confidence",
          "matched_count",
          "total_count"
        ],
        "properties": {
          "column": {
            "type": "string"
          },
          "parent": {
            "type": "string"
          },
          "geo_type": {
            "type": "string"
          },
          "confidence": {
            "type": "number"
          },
          "total_count": {
            "type": "integer"
          },
          "matched_count": {
            "type": "integer"
          }
        },
        "additionalProperties": false
      },
      "value_column": {
        "type": "string"
      },
      "output_format": {
        "type": "string"
      },
      "render_params": {
        "type": "object",
        "additionalProperties": true
      }
    },
    "additionalProperties": false
  }

  arguments 125 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.