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Glama

Data Nantes

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Query records from a Nantes 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": "bike_stations",
+    "limit": 20
+  },
+  {
+    "dataset_id": "air_quality",
+    "group_by": "station_name",
+    "order_by": "avg_pm25 desc",
+    "select": "count(*) as measurements, avg(pm25) as avg_pm25",
+    "where": "date >= 2023-01-01"
+  }
+]
  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, idempotentHint, and non-destructive behavior. The description adds context about using ODSQL and the specific dataset, confirming the read-only nature. 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?

Single sentence with no wasted words. It front-loads the core action and then succinctly lists the operations in parentheses. Highly efficient.

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?

The description covers the key operations but omits information about return format or any limitations. Given there is no output schema, the agent might need more context about what the response contains (e.g., structure of records). Adequate but not fully 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 coverage is 100%, so baseline is 3. The description enhances understanding by grouping parameters into categories (filter, aggregate, sort, paginate), which adds semantic structure beyond the schema's individual descriptions.

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 tool queries records from a specific dataset (Nantes Métropole Open Data) using ODSQL, and lists filtering, aggregation, sorting, and pagination. This verb+resource combination distinguishes it from siblings like search_datasets and dataset_info.

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?

Usage context is implied by the purpose, but there is no explicit when-to-use, when-not-to-use, or mention of alternatives. The description does not guide the agent on when to prefer this over similar tools like search_within.

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

A4.1/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and Polymarket analysis tools (bet_research, polymarket_edges, polymarket_arbitrage). While descriptions are detailed, the similarity in function could confuse an agent trying to select the right one.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, ask_pipeworx, bet_research, compare_entities). No mixing of conventions (camelCase, PascalCase) is observed, making it predictable.

Tool Count3/5

With 34 tools, the server is on the heavy side. The majority are Pipeworx tools covering many domains, but the count exceeds the typical sweet spot of 3-15 tools. Some consolidation could reduce redundancy.

Completeness4/5

The tool surface covers a wide range of data access and analysis (Nantes open data, SEC filings, Polymarket, entity comparison, dependency scanning). Minor gaps exist, such as limited Nantes data operations (only query and info) and some tools requiring external accounts, but the overall scope is comprehensive.

Resources