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epicsagas/alcove

by Various

Alcove is an MCP server that gives AI coding agents on-demand access to your private project docs — BM25 + vector hybrid search for precision retrieval, tree-sitter code indexing s

E

MCP

epicsagas/alcove

Added 1 June 2026

#ai-agent #claude-code #documentation #knowledge-base #mcp #mcp-server #privacy #rust

Overview

Alcove is an MCP server that gives AI coding agents on-demand access to private project documentation. It combines BM25 and vector hybrid search for precise retrieval and uses tree-sitter to index codebase structure. Policy enforcement helps maintain documentation consistency.

Best for

Best for
Teams using MCP-based AI agents who need secure, structured access to private project documentation

Use cases

  • Retrieving relevant project docs for AI agents during code generation or review
  • Indexing private codebases so agents understand project structure and naming conventions
  • Enforcing documentation policies across shared project repositories

Notes

Alcove is an MCP server that gives AI coding agents on-demand access to private project documentation. It combines BM25 and vector hybrid search for precise retrieval and uses tree-sitter to index codebase structure. Policy enforcement helps maintain documentation consistency.

9 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

  • Retrieving relevant project docs for AI agents during code generation or review
  • Indexing private codebases so agents understand project structure and naming conventions
  • Enforcing documentation policies across shared project repositories

Pros

  • Hybrid BM25+vector search improves retrieval accuracy over pure keyword or vector methods
  • Tree-sitter indexing enables agents to interpret code structure rather than just strings
  • Policy enforcement helps keep documentation aligned with project standards

Cons

  • Very early-stage project with only 9 stars, indicating limited community and support
  • Requires an MCP-compatible AI agent ecosystem to function
  • Setup and maintenance may demand familiarity with Rust tooling

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Hybrid BM25+vector search improves retrieval accuracy over pure keyword or vector methods
  • Tree-sitter indexing enables agents to interpret code structure rather than just strings
  • Policy enforcement helps keep documentation aligned with project standards

Cons

  • Very early-stage project with only 9 stars, indicating limited community and support
  • Requires an MCP-compatible AI agent ecosystem to function
  • Setup and maintenance may demand familiarity with Rust tooling

Pairs with

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