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ReyemTech

mcp-canada

by ReyemTech

plan_query

Plan multi-step queries across Canadian government data APIs by breaking natural language questions into structured execution plans with relevant tools.

Instructions

Plan a multi-step query across Canadian government data APIs.

Returns a structured execution plan with the most relevant tool names for the given natural language question. Use execute_batch to run the plan.

Use for: orchestrating queries that span multiple data sources, finding which tools to use for a complex question, multi-API planning, cross-module queries, batch query preparation.

Keywords: plan, query, multi-step, orchestrate, batch, cross-module, execution plan, tool selection, NL query, natural language, discover, which tools, what tools, how to query, planning, workflow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
langNoen

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It transparently states the tool is for planning only and directs to 'execute_batch' for execution. It doesn't cover limitations or error behavior, but the planning nature is well communicated.

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

Conciseness4/5

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

The description is front-loaded with core purpose but includes a lengthy keyword list that adds redundancy. It is mostly concise but could be tightened for efficiency.

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 tool's role and relationship to 'execute_batch', but lacks examples, parameter guidance, and constraints. With an output schema present, some gaps are acceptable, but parameter semantics are missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, yet the description adds no meaning for parameters 'query', 'top_k', or 'lang'. It fails to describe input semantics beyond schema defaults and enums, requiring the agent to infer.

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 plans multi-step queries across Canadian government data APIs and returns a structured execution plan. It uses specific verbs like 'plan' and 'orchestrate', and is easily distinguishable from siblings like 'call_tool' and 'execute_batch'.

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?

The description explicitly lists use cases (e.g., multi-API planning, cross-module queries) and advises using 'execute_batch' for execution. This provides clear when-to-use guidance and references an alternative sibling.

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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