Skip to main content
Glama

Opendatasoft

by io.github.pipeworx-io

Records

records
Read-onlyIdempotent

Query records from an Opendatasoft dataset with optional keyword search, ODSQL WHERE/SELECT/GROUP BY/ORDER BY clauses, pagination, and projection; returns matching rows as JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNo
limitNo
whereNo
offsetNo
selectNo
group_byNo
instanceNo
order_byNo
dataset_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsNoArray of record objects
total_countNoTotal count of matching records

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataset_id": "world-cities",
+    "limit": 20
+  },
+  {
+    "dataset_id": "us-census-data",
+    "limit": 50,
+    "offset": 0,
+    "order_by": "population DESC",
+    "select": "name,population",
+    "where": "population > 100000"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Records from a dataset",
+  "properties": {
+    "results": {
+      "description": "Array of record objects",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_count": {
+      "description": "Total count of matching records",
+      "type": "number"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds meaningful context by specifying the return format (JSON), pagination support, and ODSQL capabilities. This goes beyond what annotations provide without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, well-structured sentence that front-loads the core purpose and uses every phrase to add value. No fluff or repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers key behavioral aspects (keywords, ODSQL, pagination, projection, return format) for a moderately complex tool with 9 parameters. An output schema exists, so return details are not needed. Minor gaps like default pagination limits are omitted, but the description is complete enough for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates by mapping most parameters: q (keyword search), where/select/group_by/order_by (ODSQL clauses), limit/offset (pagination), and projection (select). It misses dataset_id and instance, but these are self-evident or niche; overall it gives significant semantic meaning to 7 of 9 params.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Query') and resource ('records from an Opendatasoft dataset'), clearly distinguishing it from siblings like 'dataset' and 'datasets' which likely handle dataset metadata. It lists concrete capabilities (ODSQL clauses, pagination, projection) beyond a generic statement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly indicates the tool is for querying records from datasets, with explicit references to ODSQL, pagination, and projection. It does not provide explicit exclusions or name alternatives, but the context makes it obvious when to use this tool versus sibling metadata/facet tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Opendatasoft toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The tool set is a kitchen sink of unrelated utilities (Opendatasoft catalog, Pipeworx data search, prediction markets, npm scanning, memory, etc.). The 'ask_pipeworx' family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are very similar and could easily be confused. The wide variety of purposes with overlapping names makes it hard for an agent to disambiguate.

Naming Consistency1/5

Naming is wildly inconsistent: snake_case (ai_visibility_check, ask_pipeworx), concatenated (pipeworx_trending, polymarket_arbitrage), verb phrases (compare_entities, suggest_questions), and simple nouns (dataset, records). No consistent pattern exists, making it hard to predict tool names.

Tool Count2/5

At 36 tools, the server is overloaded with a scattershot collection of capabilities unrelated to its name (Opendatasoft). Only 5 tools directly relate to Opendatasoft, while the rest cover diverse third-party services. This indicates poor scope focus.

Completeness2/5

The server lacks completeness for any single purpose. For Opendatasoft, it has only read-oriented tools with no create/update/delete. For Pipeworx, many query tools exist but no data ingestion. Prediction market tools are extensive but not part of the core mission. Overall, the surface has significant gaps.

Resources