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Glama

Data Paris

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Query records from a Paris 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": "arbres-remarquables-paris",
+    "limit": 20
+  },
+  {
+    "dataset_id": "stations-velib-disponibilites-en-temps-reel",
+    "limit": 10,
+    "order_by": "available_bikes desc",
+    "where": "available_bikes > 5"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint as false. The description adds valuable context about the ODSQL syntax and supported clauses, which is critical for correct invocation but does not disclose potential side effects beyond the 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?

Single sentence, front-loaded with the core purpose, followed by a parenthetical enumeration of key capabilities. No redundant information; every word serves a purpose.

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

Completeness3/5

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

Given 8 parameters and no output schema, the description provides a high-level overview but lacks details on ODSQL syntax (e.g., clause order, combining filters with aggregation) and does not mention return format or error conditions. Examples in the schema partially compensate.

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 coverage is 100% with individual parameter descriptions. The description adds semantic grouping by mapping operations to parameters (Filter (where), aggregate (group_by/select), etc.), which helps the agent understand parameter roles beyond the schema definitions.

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' with a concrete resource 'records from a Paris Open Data dataset' and clearly lists supported ODSQL operations (filter, aggregate, sort, paginate). It distinguishes itself from sibling tools like search_datasets by naming the query language and data source.

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

Usage Guidelines3/5

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

The description implies usage for ODSQL queries but does not explicitly state when to use this tool versus alternatives (e.g., search_within for simpler searches). No when-not or exclusion guidance is provided.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct function or data domain, from prediction markets (bet_research, polymarket_*) to company research (entity_profile, compare_entities) to data queries (query, dataset_info). No two tools have overlapping purposes; meta-tools like ask_pipeworx and deep_research are clearly differentiated.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive verbs (e.g., search_datasets, validate_claim, remember). No mix of conventions like camelCase or inconsistent verb choices.

Tool Count4/5

At 33 tools, the server covers a broad range of capabilities (data retrieval, AI visibility, prediction markets, company profiles, subscriptions, etc.). While slightly above the ideal range, the count is justified by the breadth of functionality and no tool feels redundant.

Completeness4/5

The tool set provides comprehensive coverage for data discovery, retrieval, and analysis across multiple domains (SEC, FDA, FRED, Paris Open Data, prediction markets, etc.). Minor gaps exist (e.g., no update/delete for Paris Open Data), but the primary focus on reading and analysis is well-served.

Resources