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S-naruka

Codebase Intelligence MCP Server

by S-naruka
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Python

#WIP (Multiple known issues)

Codebase Intelligence MCP Server

An stdio MCP server that indexes a Python codebase into a local SQLite graph. It offers hybrid code search, fast file context, lazy explanations, dependency traces, and version-aware symbol history.

It is a local, single-user tool—not a language server, hosted service, background crawler, or full type-inference engine.

Status

Phases 1–9 are implemented: schema and parser, graph traversal, search, lazy LLM summaries, versioning, seven MCP tools, CLI indexing, tests, and documentation.

Related MCP server: Satori

Prerequisites

  • Python 3.11+

  • uv

  • Git (for commit-aware version tracking)

  • Ollama for indexing/search and local summaries

  • Node.js for MCP Inspector

Pull the configured local models:

ollama pull qwen3-embedding:0.6b
ollama pull qwen2.5-coder:7b

Setup and first index

cd "d:\Projects\MCP Project\codebase-intelligence-mcp"
uv sync
copy .env.example .env
# Set REPO_PATH in .env, if desired.
uv run codebase-intel index "d:\path\to\python-repo"

To change embedding models safely:

uv run codebase-intel reindex-embeddings "d:\path\to\python-repo"

Start the MCP server after setting REPO_PATH and ensuring Ollama is running:

uv run python src/server.py

MCP tools

Tool

Example

Purpose

search_codebase

{"query":"parse Python files", "top_k":5}

Hybrid vector + BM25 retrieval with centrality reranking.

get_file_context

{"file_path":"src/parser/ast_parser.py"}

Instant template summary, imports, and signatures.

explain_function

{"symbol_name":"parse_file"}

Cached behavioural summary plus live callers/callees.

trace_dependencies

{"symbol_name":"parse_file", "depth":2}

Breadth-first caller/callee traversal.

detect_changes

{"since_version":"last"}

Git-backed changed-file parsing and version diffs.

get_version_history

{"symbol_name":"parse_file"}

Added/removed/renamed/modified history, including rename lineage.

get_codebase_health

{}

Index counts, lazy-summary coverage, tombstones, and renames.

Configuration

See .env.example. Key settings are OLLAMA_HOST, SUMMARY_MODEL, EMBEDDING_MODEL, USE_REMOTE_SUMMARIES, GEMINI_API_KEY, DATABASE_PATH, and REPO_PATH.

Known limitations

  • Call-graph heuristic: dynamic dispatch is not resolved. Only unambiguous same-file names become call edges.

  • Rename heuristic: renames require an exact matching content hash; an edit plus rename appears as removed and added.

  • Concurrency: SQLite WAL plus a 5-second busy timeout is suitable for local use, not heavy simultaneous multi-process writes.

  • sqlite-vec scaling: brute-force vector search is comfortable to roughly 50K–100K symbols; it has no ANN index.

Testing

uv run pytest tests/ -v
npx @modelcontextprotocol/inspector uv run python src/server.py

The repository also contains phase exit scripts under scripts/. The CLI and server require local Ollama models; the automated unit tests mock provider calls.

Future work

  • Background crawler and cross-process coordination

  • LSP-assisted call resolution and refactor-aware renames

  • PageRank or impact-scoped centrality recomputation

  • Token-budget-aware response packing and staleness detection

  • More languages and ANN vector indexing

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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