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MichelKerkmeester
MichelKerkmeesterCOMMUNITY
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mcp-coco-index

Route semantic code search queries to a local CocoIndex MCP server.

31|3|Updated Dec 23, 2025
Search Tags:#indexing#cli#mcp#semantic#cocoindex#code-search#fork
One-click install
npx skills add https://github.com/MichelKerkmeester/opencode--spec-kit-skilled-agent-orchestration --skill mcp-coco-index
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-coco-index
Source: https://github.com/MichelKerkmeester/opencode--spec-kit-skilled-agent-orchestration/tree/main/.opencode/skill/mcp-coco-index
Command: npx skills add https://github.com/MichelKerkmeester/opencode--spec-kit-skilled-agent-orchestration --skill mcp-coco-index

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Enables semantic code search by routing agent queries to a local CocoIndex Code MCP server, delivering concept-based results for codebases via a single MCP tool.

Core Features & Use Cases

  • Flexible routing to the CocoIndex Code MCP search tool for agents and automated workflows.
  • Supports incremental indexing, language and path filtering, and integration with local embeddings.
  • Real-time readiness and server health checks to ensure dependable agent interactions.

Quick Start

Install and run the local CocoIndex MCP server, then invoke the MCP search tool to query code.

Frequently Asked Questions about mcp-coco-index

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enable semantic code search for my AI agents?▼

You can filter code search results by applying language and path filters within the MCP search tool. The local CLI processes these constraints during routing to return concept-based matches for specific directories or file types.

What is the best way to index a local codebase for an AI agent?▼

Indexing a codebase for an AI agent involves using the local CocoIndex CLI to support incremental indexing and optional local embeddings. This process ensures the code is prepared for concept-based retrieval via the MCP search tool.

Does semantic code search work with incremental indexing and local embeddings?▼

Yes, semantic code search supports incremental indexing and integration with local embeddings. The local CLI and MCP tool route these features to keep your codebase indexed and ready for concept-based agent queries.

How do I check server health and readiness for MCP code search?▼

You can check server health and readiness using the built-in real-time telemetry and readiness tooling provided by the local CLI. This ensures dependable interactions by verifying the MCP server is operational before querying.

What telemetry data is tracked during code search and indexing?▼

Code search and indexing track patch-level telemetry including source_realpath, content_hash, path_class, dedup signals, and ranking telemetry. This data ensures accurate routing and deduplication of concept-based search results.

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