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Glama

Data Toulouse

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Query records from a Toulouse Métropole Open Data dataset with ODSQL. Filter (where), aggregate (group_by/select), sort (order_by), paginate (limit/offset).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax records (1-100, default 20).
queryNoFree-text keyword across all fields (optional).
whereNoODSQL filter, e.g. `year >= 2020 AND city = "Paris"` (overrides query).
offsetNoPagination offset (default 0).
selectNoODSQL select/aggregation, e.g. `count(*) as n, sum(amount)`.
group_byNoODSQL group_by field(s).
order_byNoSort, e.g. `date desc`.
dataset_idYesDataset id from search_datasets.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataset_id": "toulouse_traffic_sensors",
+    "limit": 20
+  },
+  {
+    "dataset_id": "toulouse_traffic_sensors",
+    "group_by": "sensor_location",
+    "limit": 50,
+    "order_by": "vehicle_count desc",
+    "select": "count(*) as vehicle_count",
+    "where": "date >= '2024-01-01'"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool uses ODSQL, which is a specific query language, providing useful behavioral context beyond annotations. It does not contradict 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?

Two sentences: first states purpose and resource, second lists capabilities. No fluff, front-loaded with essential information. Every word earns its place.

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?

No output schema, but the description covers the main operations and examples exist in the input schema. It lacks specification of the return format (e.g., JSON array), but for a query tool with rich schema, it is sufficiently complete.

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?

Schema description coverage is 100%, so baseline is 3. The description adds value by summarizing parameter groups (filter, aggregate, sort, paginate) and noting that `where` overrides `query`, which is not explicitly stated in the schema.

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 clearly states the verb 'Query' and the resource 'records from a Toulouse Métropole Open Data dataset with ODSQL'. It specifies operations (filter, aggregate, sort, paginate) and distinguishes from sibling tools like search_datasets by emphasizing the ODSQL query language.

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 explicitly says 'Query records with ODSQL', indicating when to use it. It does not explicitly exclude alternatives or state when not to use it, but the context is clear enough for agents to infer usage.

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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread). Users will struggle to choose the right tool without deep reading.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ai_visibility_check, bet_research), others camelCase (generate_llms_txt, list_subscriptions), and many are long phrases (scan_competitor_ai_presence, polymarket_kalshi_spread). No consistent pattern.

Tool Count3/5

34 tools is high but not extreme. The server covers diverse domains (company data, drugs, economics, prediction markets, open data, memory utilities), but many tools are very specific and could be consolidated, making the set feel bloated.

Completeness3/5

The server covers many data sources but has notable gaps: simple market listing tools are missing for Polymarket, and the Toulouse Open Data tools are limited to querying only (no create/update/delete). Memory and subscription tools seem ancillary to the core data mission.

Resources