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
MCP
epicsagas/alcove
Added 1 June 2026
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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