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GraphRAG TypeScript MCP Tools

GraphRAG TypeScript MCP Tools

by Akakinad
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TypeScript
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explainMovieData

Retrieve and interpret movie data by providing a title. This tool uses LLM sampling to generate a natural language explanation of key details about a film.

Instructions

Get a natural language explanation of movie data using LLM sampling

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": {
      "movieTitle": "<movieTitle>"
    },
    "name": "explainMovieData"
  }
}

Input Schema

NameRequiredDescriptionDefault
movieTitleYesThe title of the movie

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions 'using LLM sampling', which implies non-deterministic generative behavior, but omits details on authorization, rate limits, or potential side effects. The minimal context is acceptable for a read-like tool but lacks depth.

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?

The description is a single sentence of 10 words, front-loading the core functionality. Every word contributes meaning, and there is no redundant or irrelevant information.

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?

Given low complexity (1 required parameter, no output schema), the description adequately states the tool's purpose but fails to specify what aspects of movie data are explained (e.g., plot, cast, ratings) or the nature of the 'natural language explanation'. The output format is left entirely to inference.

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?

The input schema has 100% coverage with a single parameter 'movieTitle' described as 'The title of the movie'. The description adds no additional parameter semantics beyond the schema, so it meets the baseline for well-documented parameters without extra value.

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 verb 'Get', the resource 'movie data', and the method 'natural language explanation using LLM sampling'. It distinguishes itself from siblings like getMoviesByGenre and graphStatistics, which serve different purposes.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus its siblings. It does not mention when-not-to-use, prerequisites, or alternatives, leaving the agent to infer based solely on the tool name and sibling list.

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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