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MapleStats MCP

Plan Query

plan_query
Read-onlyIdempotent

Identify which Canadian data sources answer a multi-agency question and return the topics, ordered tools, local portals, and caveats to combine them.

Instructions

Plan which sources answer a question that may span several agencies.

Use for: the first call on a substantive question ("How have rents and interest rates moved in Calgary since 2020?"). Returns the topics it touches, the tools to call for each in order, local portals for any province or city named, and caveats on combining them. Then run the steps with call_tool. lang is accepted for consistency; the plan text is English. Keywords: plan, which data source, where to find, combine sources, cross-source, question, research, Canada data. Mots-clés : planifier, quelle source de données, où trouver, combiner des sources, question, recherche, données canadiennes, statistiques.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen
questionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
placesYes
topicsYesBest match first; at most 4.
guidanceYes
questionYes
provenanceYesWhere a result came from and how fresh/complete it is. Embedded in every module's response models (composition, not inheritance) so a tool can return `MyResult(provenance=..., ...)` without fighting Pydantic's model-inheritance rules for extra fields.
fallback_stepsYesOnly when no topic matched.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description adds genuine value beyond that: what the plan returns (topics, ordered tool calls, local portals, combination caveats) and the non-obvious note that "lang is accepted for consistency; the plan text is English" — a real behavioral quirk the schema alone does not reveal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core guidance is well front-loaded and earn its place, but the trailing keyword and Mots-clés blocks are length padding that restates the same concepts in two languages. Useful for search_tools discovery, but they dilute the definition's structure.

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?

An output schema exists, so return values needn't be enumerated, yet the description helpfully previews what the plan contains. Combined with the usage context and the call_tool handoff, an agent has nearly everything it needs; only the question parameter's expected format is left implicit.

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 0%, so the description must carry the load. It clarifies the lang parameter's actual effect (accepted for consistency, output stays English) and illustrates the question parameter with a sample, but never describes the expected form or scope of question beyond the example. Partial compensation only.

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 (plan) and resource (which sources answer a question), and explicitly scopes it to cross-agency questions. It distinguishes itself from siblings by naming call_tool as the follow-up executor, so an agent can tell it apart from search_tools and call_tool 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 Guidelines4/5

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

Explicitly says when to use it ("the first call on a substantive question") and gives a concrete example, plus tells the agent what to do next ("run the steps with call_tool"). It lacks an explicit when-not clause, e.g. routing simple single-source lookups away from this tool, so it falls short of a 5.

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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