semhood MCP server

<!-- mcp-name: io.github.AhmeedGamil/semhood -->

semhood

Stop grepping. Find the exact code your AI agent needs by intent, not keywords. semhood is an AST-aware semantic code search engine that retrieves code by what it does, complete with call-graph context and optional LLM enrichment.

Runs fully offline with zero API keys — or plug in cloud embeddings — Voyage's code-specialized models, or OpenAI's strong general-purpose (natural-language) embeddings — for higher-quality retrieval. Optional LLM enrichment adds a logic summary and developer queries to each chunk that you commit once and share — and every result ships with its call graph (what it calls + what calls it).

Why semhood · Install · Quickstart · MCP Setup · Architecture · Config · Troubleshooting

<!-- DEMO (highest-impact addition): record a ~15s clip showing semhood index . → semhood search "..." → a result with its call graph. Capture with vhs (github.com/charmbracelet/vhs), terminalizer, or asciinema+svg-term. Save it as assets/demo.gif, then uncomment the line below: --> <!-- -->

┌──────────────┐    parse + embed     ┌──────────────┐    LLM enrich    ┌──────────────┐
   │  source tree │ ───────────────────► │ code  vector │ ──────────────► │  description │
   │              │                      │ + BM25 sparse│                  │  + queries   │
   └──────────────┘                      └──────────────┘                  └──────────────┘

                                Search via CLI, HTTP, or MCP.

semhood indexes your codebase the way a developer thinks about it: every function, method, and class becomes a chunk, with its call graph, docstring, and signature attached. You search by intent ("how do we retry transient payment failures?") and get back the few chunks that actually answer the question.

Two stages: structural (always, free, ~seconds) and enrichment (optional, LLM or code agent, drains a pending queue). The index is queryable after stage 1; stage 2 just makes natural-language matches sharper.

The package ships an MCP server so Claude Desktop, Cursor, Cline, Continue, Kiro, Zed, and any other MCP-aware client can call semhood as a tool — your AI agent gets search, findsymbol, and getchunkcontext next to its built-in readfile and grep.

What makes semhood different

Most code search embeds your raw source and hopes a natural-language query lands near it. semhood adds two things plain vector search and grep can't:

🧠 Enrichment that thinks like a developer

An LLM reads each function and class and writes two artifacts, each stored as its own search vector:

Shortened. The full README is on GitHub.