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source-rag-mcp

source-rag-mcp

by hanhy06
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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rag_search

Find relevant source code chunks from decompiled Minecraft using lightweight lexical RAG scoring, with optional version filtering.

Instructions

Search source chunks with lightweight lexical RAG scoring.

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": {
      "query": "<query>"
    },
    "name": "rag_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYes
versionNoMinecraft version. Omit to search every indexed version.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions 'lightweight lexical RAG scoring' but does not specify whether the operation is read-only, any required authentication, rate limits, or what happens to the state. This is insufficient for a search tool.

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 description is a single sentence, which makes it concise but overly brief. It lacks structure and does not present information in a scannable way. While front-loaded, it omits crucial details.

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

Completeness2/5

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

Given the tool has 3 parameters (one required), no output schema, and no annotations, the description is insufficient. It does not explain the output format, scoring details, or how to interpret results, leaving the agent with many unknowns.

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 only 33% (only 'version' has a description). The description adds no information about the parameters 'query' or 'limit', failing to compensate for the low coverage. The agent would need to infer their meanings.

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 'Search', the resource 'source chunks', and the method 'lightweight lexical RAG scoring', distinguishing it from sibling tools like search_symbol and search_text which likely use different algorithms.

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 alternatives such as search_symbol or search_text. There is no mention of use cases, limitations, or criteria for selecting this tool.

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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