Codebase Intelligence MCP Server

Codebase Intelligence MCP Server

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

Category
访问服务器

README

#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.

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

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选