MapleStats MCP

Search tools

search_tools

Read-onlyIdempotent

Find the right Canadian open-data tool for a specific need using a plain-language query in English or French; returns ranked tool definitions and input schemas from MapleStats MCP.

Instructions

Find the right data tool for one specific need by plain-language search.

Use for: locating a tool when you know roughly what data you want (a StatCan table, Bank of Canada series, a city's open-data portal). Returns the best-matching tool definitions, ranked by relevance, each with its name, description and full input schema, in the same format as list_tools. Read-only; it searches this server's catalogue only and calls no external API. Use plan_query instead when a question may span several sources or you do not know where to start; use call_tool to run a tool once you have its name. Queries in English or French both work. An empty result means no tool matched: rephrase with topic words (e.g. 'housing starts' rather than 'CMHC table').

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	What you need, in plain English or French, e.g. 'Calgary rent index' or 'taux de chômage Québec'. Topic words work better than tool names.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.1.1Oct 1, 2026

changedInput schema / properties / query / description

Previous value: -"Natural language query to search for tools"New value: +"What you need, in plain English or French, e.g. 'Calgary rent index' or 'taux de chômage Québec'. Topic words work better than tool names."

First observedv0.1.0Sep 27, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations cover the safety profile (readOnly/openWorld=false/idempotent), and the description adds genuinely new behavior: it searches only this server's catalogue, calls no external API, returns ranked best-matching tool definitions in list_tools format, and defines the empty-result case with recovery advice. These are traits the annotations cannot convey.

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?

Purpose is front-loaded in the first sentence, followed by use-for, boundaries, alternatives, and an edge-case recovery line. Every sentence carries distinct operational information with no filler or repetition.

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

Completeness5/5

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

For a one-parameter search tool with an output schema and full annotation coverage, nothing an agent needs is missing: trigger, scope, return shape, ranking behavior, language support, and failure recovery are all stated.

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% and the single query parameter is already well documented, so the baseline is 3. The description adds meaningful strategy beyond the schema: queries work in English or French, and topic words beat tool names, which shapes how an agent should phrase the argument.

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?

States a specific verb (find/search) and resource (data tool) plus the scoping constraint that it searches this server's catalogue only. It explicitly names what it is not (plan_query, call_tool), so an agent can distinguish it from siblings without opening any schema.

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

Usage Guidelines5/5

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

Gives an explicit 'Use for' trigger, then hands off with conditions: 'use plan_query instead when a question may span several sources or you do not know where to start' and 'use call_tool to run a tool once you have its name.' That is the full when/when-not/alternative set.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server