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Glama

Data Iledefrance

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Query records from a Île-de-France 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": "transports-ratp",
+    "limit": 20
+  },
+  {
+    "dataset_id": "education-schools",
+    "group_by": "type",
+    "limit": 50,
+    "order_by": "school_count desc",
+    "select": "count(*) as school_count",
+    "where": "arrondissement = '75001'"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, indicating safe, read-only, idempotent behavior. The description adds behavioral details such as the use of ODSQL and the 'where' parameter overriding 'query'. Since annotations are rich, the description provides moderate additional context but no contradictions.

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 immediately conveys the purpose and key capabilities. It is front-loaded with essential information—action, resource, language, and operations—without extraneous detail. Every part 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?

The tool has 8 parameters, no output schema, and rich annotations. The description covers the core idea and main operations, but does not mention return format, error handling, or pagination behavior beyond mentioning limit/offset. Given the complexity, it is adequate but could be slightly more complete.

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 100%, so the baseline is 3. The description groups parameters by operation (filter, aggregate, etc.) and introduces ODSQL context, but does not add significant meaning beyond the schema's parameter descriptions. The schema already documents each parameter well (e.g., limit range, where syntax).

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 action: 'Query records from a Île-de-France Open Data dataset with ODSQL.' It specifies the resource (records from a dataset), the query language (ODSQL), and lists key operations (filter, aggregate, sort, paginate). This is specific and distinguishes from sibling tools, none of which appear to perform similar querying.

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 lists the operations available (filter, aggregate, sort, paginate), which implies when to use the tool—when you need to query data with ODSQL. However, it does not explicitly state when not to use it or suggest alternative tools. Sibling tools like 'search_datasets' and 'validate_claim' are different, but no comparison is made.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., multiple ask/tools for querying (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple company analysis tools (entity_profile, compare_entities, recent_changes). The prediction market tools (bet_research, polymarket_arbitrage, etc.) further blur distinctions. Agents would frequently select the wrong tool.

Naming Consistency2/5

Naming conventions are inconsistent: snake_case (ask_pipeworx, dataset_info), camelCase (ai_visibility_check, scan_competitor_ai_presence), and phrases (pipeworx_feedback, polymarket_arbitrage). No pattern emerges, making tool discovery harder.

Tool Count2/5

33 tools is excessive for a server ostensibly about Île-de-France Open Data. Only 3 tools (dataset_info, query, search_datasets) relate to that domain, while the rest are a general-purpose data agent with many disjoint capabilities. The count feels bloated and unfocused.

Completeness2/5

For the declared purpose (Île-de-France data), the tool set is missing CRUD operations (no create/update/delete). For the actual general data use, there are gaps like missing person entity resolution, data visualization, and file handling. The deep_research tool requires an account, creating a barrier.

Resources