A blazingly fast codebase graphRAG implementation in 100% Rust
If you already use Codegraph, the codegraph mcp server is the piece that lets your assistant work with it directly. A blazingly fast codebase graphRAG implementation in 100% Rust.
CodeGraph transforms your entire codebase into a semantically searchable knowledge graph that AI agents can actually reason about—not just grep through.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
agentic_context — Gathers the context you need—searches code, builds comprehensive context, answers semantic questionsagentic_impact — Maps change impact—dependency chains, call flows, what breaks if you touch somethingagentic_architecture — The big picture—system structure, API surfaces, architectural patternsagentic_quality — Risk assessment—complexity hotspots, coupling metrics, refactoring prioritiesEmbeddings — Use any model with dimensions 384-4096: - Local: Ollama, LM Studio, ONNX Runtime - Cloud: OpenAI, Jina AIDatabase — The Database tool exposed by this serverExperimen — The Experimen tool exposed by this serverConfiguration is passed through the environment: CODEGRAPH_PROJECT_ID. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Among the database access options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Codegraph's toolset — agentic_context, agentic_impact, agentic_architecture and 4 more — is a fair guide to whether it matches your workflow.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| agentic_context | Gathers the context you need—searches code, builds comprehensive context, answers semantic questions |
| agentic_impact | Maps change impact—dependency chains, call flows, what breaks if you touch something |
| agentic_architecture | The big picture—system structure, API surfaces, architectural patterns |
| agentic_quality | Risk assessment—complexity hotspots, coupling metrics, refactoring priorities |
| Embeddings | Use any model with dimensions 384-4096: - **Local:** Ollama, LM Studio, ONNX Runtime - **Cloud:** OpenAI, Jina AI |
| Database | The Database tool exposed by this server. |
| Experimen | The Experimen tool exposed by this server. |
{
"mcpServers": {
"codegraph": {
"command": "/full/path/to/codegraph",
"args": ["start", "stdio", "--watch"]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| CODEGRAPH_PROJECT_ID | Configuration value read at startup. | Optional |
Read-only SQL access to Postgres — let your assistant inspect schemas and answer questions from real data.
Manage your whole Supabase project in conversation — database, auth, storage, Edge Functions and branches.
Query, modify and analyse local SQLite databases in conversation — the fastest way to chat with a data file.
Metabase ships its own MCP endpoint — search your BI content, build and run queries, and save questions and dashboards without leaving the chat.
Official MongoDB server covering data, schemas and Atlas management — from find queries to spinning up clusters.
Serverless Postgres with database branching — point your assistant at Neon and let it work on disposable copies.