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code-index-mcp

code-index-mcp

by Krat12
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_semantic

Find code locations by conceptual meaning, not exact matches. Use natural language queries to search across indexed codebases.

Instructions

Fuzzy meaning-based search (vector similarity via Qdrant).

Use for conceptual queries like "where do we validate refunds" when you don't know exact identifiers. service selects the index.

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": "search_semantic"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
limitNo
serviceNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so description carries full burden. Reveals it's vector similarity search, but omits behavioral traits like idempotence, return format, or error conditions. Adequate but incomplete.

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?

Two concise, front-loaded sentences. Every word adds value; no redundancy.

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?

Output schema exists, so return format may be documented elsewhere. Description covers purpose and one parameter, but misses details on limit behavior and edge cases. Reasonably complete given complexity.

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 description must compensate. It explains 'service' selects the index, but adds no detail for query or limit. Partial 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?

Description clearly states 'fuzzy meaning-based search (vector similarity via Qdrant)' with an example query, distinguishing it from siblings like search_text and search_symbol.

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?

Explicit advice: 'Use for conceptual queries... when you don't know exact identifiers.' Mentions service parameter selects index. Lacks explicit when-not-to-use or alternative names, but context is clear.

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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