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Data Issy

by io.github.pipeworx-io
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Query

query
Read-onlyIdempotent

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

Input 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.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataset_id": "issy_public_transport",
+    "limit": 20
+  },
+  {
+    "dataset_id": "issy_air_quality",
+    "limit": 50,
+    "order_by": "date desc",
+    "select": "count(*) as records",
+    "where": "year >= 2022"
+  }
+]
  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 readOnlyHint=true and destructiveHint=false. Description adds context: only works with Issy-les-Moulineaux Open Data datasets, which is useful beyond 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 verb and resource, no wasted words. Efficient and scannable.

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?

Covers main behavior and source constraint. Lacks mention of return format or behavior of 'query' vs 'where' parameters, but adequate for most use cases.

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% (baseline 3). Description groups parameters into ODSQL clauses (filter, aggregate, sort, paginate), adding clarity beyond individual parameter 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?

Description clearly states it queries records from Issy-les-Moulineaux Open Data using ODSQL, listing capabilities (filter, aggregate, sort, paginate). This 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?

Implied usage for querying dataset records, but no explicit guidance on when to use vs alternatives like search_within, and no exclusions or prerequisites mentioned.

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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