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Glama

Opendatasoft

by io.github.pipeworx-io

Datasets

datasets
Read-onlyIdempotent

Search datasets on any Opendatasoft portal (default public.opendatasoft.com) by keyword, with optional sort and facet filters; returns dataset IDs, titles, and field summaries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNo
rowsNo
sortNo
facetNoComma-sep facets to include.
startNo
instanceNoDefault public.opendatasoft.com.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
facetsNoFacet aggregations if requested
resultsNoArray of dataset objects
total_countNoTotal count of matching datasets

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: +[
+  {
+    "q": "population",
+    "rows": 10
+  },
+  {
+    "facet": "theme,license",
+    "instance": "public.opendatasoft.com",
+    "q": "climate",
+    "sort": "-modified"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Search results for datasets",
+  "properties": {
+    "facets": {
+      "description": "Facet aggregations if requested",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "results": {
+      "description": "Array of dataset objects",
+      "items": {
+        "properties": {
+          "dataset_id": {
+            "description": "Unique dataset identifier",
+            "type": "string"
+          },
+          "description": {
+            "description": "Dataset description",
+            "type": "string"
+          },
+          "modified": {
+            "description": "Last modification date",
+            "type": "string"
+          },
+          "name": {
+            "description": "Dataset name",
+            "type": "string"
+          },
+          "records_count": {
+            "description": "Number of records in dataset",
+            "type": "number"
+          },
+          "theme": {
+            "description": "Dataset theme/category",
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_count": {
+      "description": "Total count of matching datasets",
+      "type": "number"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is well covered. The description adds valuable context about the default instance (public.opendatasoft.com) and the returned fields, which goes beyond the schema. It doesn't mention rate limits or pagination behavior, but given the strong annotations, this is adequate.

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?

The description is a single sentence that is front-loaded with the verb and resource, and it packs essential information (scope, filters, return values) without any unnecessary words.

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 tool has 6 params, no required params, a good annotation set, and an output schema. The description covers the main use case and return summary, but it doesn't explain sort/facet syntax or pagination (start/rows). Schema examples partially fill this gap, making it adequate for a search tool.

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

Parameters3/5

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

Schema description coverage is only 33% (2 of 6 params have descriptions). The description clarifies 'q' as keyword and mentions optional sort and facet filters, and confirms the default instance, but it does not explain 'rows' or 'start' parameters. This partially compensates for the low schema coverage but leaves gaps.

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

Purpose4/5

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

The description clearly states 'Search datasets on any Opendatasoft portal' with a specific verb, resource, and scope, and lists return values (dataset IDs, titles, field summaries). It does not explicitly contrast with the sibling 'dataset' tool, so it stops short of fully distinguishing from alternatives.

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 implies when to use the tool: for keyword-based dataset search across Opendatasoft portals, with optional sort and facet filters. It doesn't mention exclusions or name alternative tools like 'dataset' or 'records', but the context is sufficient for most selection scenarios.

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

All Opendatasoft toolsInspect
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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