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local-policy-mcp

local-policy-mcp

by cbjworld
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

build_research_brief

Read-only

Generate a comparison brief from 2–4 researched local policy cases using exact quotes from source documents; missing fields stay unknown, and quote presence is checked.

Instructions

Build a comparison from 2–4 newly researched cases and exact quotes returned by read_open_document or read_official_source in this process. Missing fields stay unknown. Checks quote presence, NOT factual interpretation or attribution.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "cases": "<cases>"
    },
    "name": "build_research_brief"
  }
}

Input Schema

NameRequiredDescriptionDefault
casesYes
titleNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.7

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare read-only, non-destructive, and closed-world behavior. The description adds useful validation semantics: missing fields remain unknown and quote presence is checked, but factual interpretation and attribution are not validated. It still omits return format, but the annotations carry safety context.

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?

Three sentences are front-loaded with purpose and constraints. Every sentence adds a distinct constraint (source, missing fields, validation scope) with no filler.

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?

For a tool with a required 2–4 item array of case objects and no output schema, the description gives the high-level input contract but leaves key details implicit. It does not explain the nested case fields or what the built comparison returns, so an agent still needs to infer much from the schema.

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 coverage is 0%, so the description must carry parameter meaning. It explains that 'cases' are 2–4 newly researched cases with exact quotes from specific reader tools, but it does not explain the nested fields (budget, result, target, operation) or the optional 'title' parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Build a comparison') and constrains the inputs to 2–4 cases with exact quotes from read_open_document or read_official_source. It distinguishes the tool from the listed reader siblings by being a downstream composer, though it does not explicitly contrast with search/research alternatives.

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?

It gives prerequisite context: the cases must come from newly researched sources and quotes must be returned by read_open_document or read_official_source in this process. However, it does not say when to prefer this tool over research_policy or the search siblings, nor does it state when not to use it.

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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