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Glama

Opendatasoft

by io.github.pipeworx-io

Dataset

dataset
Read-onlyIdempotent

Fetch schema and metadata for a single Opendatasoft dataset by dataset_id; returns field definitions, record count, and dataset description from the specified portal instance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instanceNo
dataset_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoDataset name
themeNoDataset theme/category
fieldsNoList of fields/columns
modifiedNoLast modification date
dataset_idNoUnique dataset identifier
descriptionNoDataset description
records_countNoTotal number of 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"
+  },
+  {
+    "dataset_id": "us-census-data",
+    "instance": "public.opendatasoft.com"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Dataset metadata and details",
+  "properties": {
+    "dataset_id": {
+      "description": "Unique dataset identifier",
+      "type": "string"
+    },
+    "description": {
+      "description": "Dataset description",
+      "type": "string"
+    },
+    "fields": {
+      "description": "List of fields/columns",
+      "items": {
+        "properties": {
+          "description": {
+            "description": "Field description",
+            "type": "string"
+          },
+          "name": {
+            "description": "Field name",
+            "type": "string"
+          },
+          "type": {
+            "description": "Field data type",
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "modified": {
+      "description": "Last modification date",
+      "type": "string"
+    },
+    "name": {
+      "description": "Dataset name",
+      "type": "string"
+    },
+    "records_count": {
+      "description": "Total number of records",
+      "type": "number"
+    },
+    "theme": {
+      "description": "Dataset theme/category",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds behavioral detail by specifying return contents (field definitions, record count, dataset description) and the role of the portal instance, enriching understanding without contradicting annotations.

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, front-loaded with the action and resource, then lists return values. Every word contributes, with no redundancy or fluff.

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?

For a low-complexity metadata fetch with an output schema present, the description covers the core purpose, inputs, and return values well. Minor gap: it does not explain instance default behavior, but this is not critical given the simplicity.

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 0%, so the description must compensate. It references dataset_id explicitly and implies instance via 'specified portal instance', but it does not explain whether instance is optional or what happens if omitted, leaving a semantic gap for the 2-parameter tool.

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 'Fetch' and clearly identifies the resource: 'schema and metadata for a single Opendatasoft dataset by dataset_id'. It distinguishes from sibling 'datasets' (plural) by emphasizing 'single', making the scope explicit.

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 implies when to use this tool (when needing schema/metadata for one dataset) and contrasts with plural 'datasets' sibling, but it does not explicitly name alternatives or state when not to use it. This provides clear context without formal exclusions.

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

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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