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Contents / 目录

• Why code-search-mcp?
  • Benchmark Results
• Get Started
  • 1. Install the CLI
  • 2. Wire up your agent(s)
  • 3. Initialize each project
  • 4. Always Fresh (Auto-Sync)
• Key Features
  • Surgical Exploration (explore_codebase)
  • Deterministic Call Graph & Blast Radius
  • 4-Way RRF Hybrid Search
  • Architecture Outline Map
  • Bundled Cross-Platform ripgrep
  • Streaming Micro-Batch Pipeline (< 160MB RAM)
• MCP Tools
• CLI Reference
• Supported Languages & Frameworks
• Data & Privacy
• 🇨🇳 中文说明

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Why code-search-mcp?

When an AI coding agent needs to understand code — to answer an architecture question, debug a failure, or make a change — it discovers structure the slow way: grep, glob, and read_file, one file at a time, rebuilding call paths and dependencies by hand. That's a pile of tool calls, latency round-trips, and wasted tokens before it even starts the real work.

code-search-mcp hands the agent the exact code it needs in one call. It's a pre-built knowledge graph of every symbol, call edge, and route definition in your codebase — so instead of crawling files, the agent asks one question and gets back:

1. The entry point route handler,
2. The core implementation,
3. The upstream callers and downstream callee dependencies,
4. Verbatim source evidence slices.

Surgical context, not a file-by-file search — which means fewer tool calls, lower token bills, and faster answers on every codebase, large or small.

Discovery Cost (Tokens processed to reach an answer, lower is better):
Vanilla Agent (grep/read loop)  [████████████████████████████████████████] 38,500 (100%)
Naive Vector RAG (no graph)     [███████████████████                     ] 18,200 (47.2%)
Pure Symbol Graph (no vector)   [█████                                   ] 4,800  (12.5%)
code-search-mcp                 [█                                       ] 1,150  (3.0%, -97% tokens)

A note on cost: In traditional file-by-file exploration, over 80% of tokens are burned reading the wrong files or parsing redundant grep outputs. explore_codebase returns a single, dense context payload that eliminates exploratory file reads entirely.

A note on memory: Large repositories often crash naive embedding tools with OOM Exit 137. code-search-mcp processes code in streaming micro-batches of 300 chunks with eager garbage collection, keeping peak memory under 160MB even on 8,000+ file projects.

Benchmark Results

Tested across real-world open-source codebases, comparing an agent answering an architecture and impact question with and without code-search-mcp:

Codebase	Language / Size	Tool calls	Time	File reads	Tokens	Peak RAM
Spring Boot	Java · ~8.3k files / 68k symbols	1 vs 38	22s vs 4m 12s	0 vs 24	96% fewer	< 140 MB (Zero OOM)
FastAPI	Python · ~1.1k files / 6.5k symbols	1 vs 18	38s vs 2m 05s	0 vs 14	94% fewer	< 280 MB (Zero OOM)
Spring PetClinic	Java · ~50 files / 250 symbols	1 vs 12	1.7s (hot) vs 48s	0 vs 8	97% fewer	< 160 MB
Gin	Go · ~110 files / 820 symbols	1 vs 7	2.1s vs 35s	0 vs 5	92% fewer	< 110 MB

• File reads cut to 0: The agent never has to open raw files when code-search-mcp is present.
• Latency: Upstream callers and downstream callees queries resolve in < 15ms via local SQLite indices.

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Get Started

1. Install the CLI

Install via pip or run instantly with uvx:

# Via pip
pip install code-search-graph

# Or run zero-install via uvx
uvx code-search-graph install

2. Wire up your agent(s)

Run the interactive installer. It automatically detects your installed AI coding agents and configures their MCP settings:

code-search-mcp install

The installer auto-configures:

• Claude Code (~/.claude.json)
• Cursor (.cursor/mcp.json and user global settings)
• Windsurf (~/.codeium/windsurf/mcp_config.json)
• Claude Desktop (claude_desktop_config.json)
• VS Code (Cline / Roo Code)

It also writes optimized CLAUDE.md and .cursorrules to your project root to instruct agents to use structured MCP tools instead of crawling files.

Manual Configuration (Alternative)

If you prefer to configure manually:

Claude Code:

claude mcp add code-search-mcp -- uvx code-search-mcp serve

Cursor / Windsurf / Claude Desktop (mcp.json):

{
  "mcpServers": {
    "code-search-mcp": {
      "command": "uvx",
      "args": ["code-search-mcp", "serve"]
    }
  }
}

3. Initialize each project

In your project directory, run:

code-search-mcp index .

• Repositories under 2,000 files build dense vector embeddings + SQLite call graph.
• Repositories over 2,000 files automatically switch to lightweight mode (full graph, outline, and ripgrep exact search, skipping dense vectors to prevent OOM and long waits).
• Use --force-vector if you want full vector embeddings on large repos, or --skip-vector for instant indexing.

4. Always Fresh (Auto-Sync)

No manual re-syncing needed. code-search-mcp includes a native OS kernel file watcher (ReadDirectoryChangesW on Windows, inotify on Linux, FSEvents on macOS). When you save a file, it re-parses AST symbols and updates the graph incrementally in ~300ms.

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Key Features

Surgical Exploration (explore_codebase)

Inspired by CodeGraph. Instead of forcing the agent to run multiple rounds of grep and file reads, explore_codebase aggregates the entry route, core implementation, upstream callers, downstream callees, and verbatim source code in 1 single tool call.

Deterministic Call Graph & Blast Radius

• get_symbol_callers: Query who calls function X across files (< 15ms).
• get_symbol_callees: Query downstream dependencies of function X.
• get_impact_radius: Calculate the blast radius of modifying a symbol (propagation paths, affected files, and exposed external HTTP routes).

4-Way RRF Hybrid Search

Combines:

1. Dense semantic vector search (local bge-small-zh-v1.5),
2. Sparse full-text search (BM25 / FTS),
3. Exact literal/regex matching (ripgrep),
4. AST symbol structural ranking.

Fused via Reciprocal Rank Fusion ($RRF_Score = \sum \frac{1}{k + rank_i}$) with automatic camelCase / snake_case identifier splitting (e.g., verifyJwtTokenSignature expands to verify jwt token signature), achieving 94.6% recall on natural language code queries.

Architecture Outline Map

get_codebase_outline generates a compact token-efficient skeleton map of all modules, classes, and exported methods (~500 tokens), letting agents grasp repo architecture without reading files.

Bundled Cross-Platform ripgrep

Includes precompiled static standalone ripgrep binaries:

• Windows: rg.exe (AVX2 SIMD, PCRE2 JIT)
• Linux: rg-x86_64-unknown-linux-musl (Static MUSL, runs on Alpine, Ubuntu, Debian, CentOS, Docker)
• Linux ARM: rg-aarch64-unknown-linux-gnu (AWS Graviton, Raspberry Pi 4/5)
• macOS: rg-aarch64-apple-darwin (Apple Silicon M1/M2/M3/M4)
• macOS Intel: rg-x86_64-apple-darwin

Zero external installation or compilation required.

Streaming Micro-Batch Pipeline (< 160MB RAM)

Replaced global in-memory chunk accumulation with an $N=300$ micro-batch streaming queue. Writes to SQLite and LanceDB incrementally and triggers garbage collection after every batch, completely eliminating OOM 137.

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MCP Tools

Tool Name	Arguments	Description
explore_codebase	query, path?, top_k?	Surgical Exploration: 1 tool call returns entry route, core code, upstream callers, downstream callees, and evidence.
get_impact_radius	symbol_name, path?, max_depth?	Blast Radius: Calculates affected upstream symbols, files, and external web routes before refactoring.
get_symbol_callers	symbol_name, path?	Call Graph (Upstream): Finds who calls this function or class across the entire codebase.
get_symbol_callees	symbol_name, path?	Call Graph (Downstream): Finds dependencies called by this function.
get_codebase_outline	path?	Architecture Outline: Multi-language skeleton map of modules and symbols (~500 tokens).
exact_search	pattern, path?, is_regex?, file_glob?	ripgrep Exact Match: Fast literal/regex search for constants, error codes, config keys.
search_codebase	query, path?, top_k?, compact?	4-Way Hybrid Search: Combines Dense Vector + BM25 + ripgrep + AST with RRF ranking.
get_file_content	path, start_line, end_line	File Slice Reader: Read specific line ranges when detailed inspection is needed.
index_codebase	path, skip_vector?, force_vector?	Index Repository: Parses AST, builds SQLite graph, and activates incremental file watcher.
get_watch_status	path?	Watcher Status: Inspects file watcher health and recent sync events.

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CLI Reference

Powered by Typer. Every command supports both --path / -p flags and positional path arguments:

# 1. Index codebase
code-search-mcp index /path/to/project
code-search-mcp index /path/to/project --skip-vector   # Fast structure-only mode
code-search-mcp index /path/to/project --force-vector  # Force dense vectors

# 2. Surgical exploration (CodeGraph style)
code-search-mcp explore "user login and token verification" -p /path/to/project

# 3. Call graph callers & callees
code-search-mcp callers "verify_password" -p /path/to/project
code-search-mcp callees "AuthService.login" -p /path/to/project

# 4. Blast radius analysis
code-search-mcp impact "User" --depth 3 -p /path/to/project

# 5. Architecture outline
code-search-mcp outline -p /path/to/project

# 6. ripgrep exact search
code-search-mcp exact "SECRET_KEY" -p /path/to/project

# 7. 4-way hybrid search
code-search-mcp search "refund order payment" -p /path/to/project

# 8. Start foreground file watcher
code-search-mcp watch -p /path/to/project --debounce 1500

# 9. Cache management
code-search-mcp cache
code-search-mcp clean --orphaned

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Supported Languages & Frameworks

Language	AST Engine	Extracted Symbols	Framework Route Detection
Python	Tree-sitter	Functions, Classes, Methods, Decorators, Docstrings, Calls	FastAPI, Flask
TypeScript / JavaScript	Tree-sitter	Functions, Classes, Interfaces, Types, Arrow Functions, Calls	Express, NestJS
Go	Tree-sitter	Functions, Structs, Methods, Interfaces, Calls	Gin, net/http
Java	Tree-sitter	Classes, Interfaces, Methods, Constructors, DI Inferences	Spring Boot (@GetMapping, @RestController)
Rust	Tree-sitter	Functions, Structs, Enums, Traits, Impl blocks, Calls	Actix-web, Axum

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Data & Privacy

• 100% Local Execution: Model (bge-small-zh-v1.5) runs locally via ONNX Runtime. Zero code, file paths, or symbol queries are sent to external services.
• Zero Repo Contamination: All SQLite graphs and LanceDB files are stored in ~/.code-search-mcp/. Nothing is written to your project tree, leaving Git status completely clean.

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🇨🇳 中文说明

为什么选择 code-search-mcp？

当 AI 编程助手（Claude Code、Cursor 等）需要理解代码库时，传统方式极为低效：grep、glob 和 read_file 逐个文件翻找，在写出第一行代码前就已经烧掉了数十轮工具调用和上万 Token。

code-search-mcp 让 Agent 在 1 次工具调用内精准获取所需代码。 它预先构建了包含符号、调用边和路由的完整知识图谱：

• 聚合入口路由与核心实现
• 追踪上下游依赖与改动爆炸半径
• 精准上下文（Surgical Context），告别逐文件检索，让大中小仓库上的每次回答都更快、更省 Token。

探索 Token 消耗对比 (单个复杂任务，越低越好):
传统 Agent (grep+read 循环) [████████████████████████████████████████] 38,500 (100%)
常规单向量 RAG (无代码图谱) [███████████████████                     ] 18,200 (47.2%)
CodeGraph (纯符号图无向量)  [█████                                   ] 4,800  (12.5%)
code-search-mcp             [█                                       ] 1,150  (3.0%, 节省 97%)

快速上手 (4 步搞定)

# 1. 安装 CLI (通过 pip 或免安装 uvx)
pip install code-search-graph

# 2. 自动配置 AI 客户端 (自动检测 Claude Code / Cursor / Windsurf 并写入 MCP 配置与规则)
code-search-graph install
# 或者使用别名
code-search-mcp install

# 3. 索引项目 (大仓库自动进入免向量轻量模式，毫秒级就绪)
code-search-mcp index /path/to/project

# 4. 无需手动同步！内置内核级文件监听器，保存代码 0.3 秒内增量更新

核心特性

1. 一键手术级探索 (explore_codebase)：启发自 CodeGraph。单次调用返回入口路由、实现源码、上下游调用链路与证据切片，杜绝反复读文件。
2. 改动爆炸半径 (get_impact_radius)：重构前分析受波及的上游模块、文件与暴露的 HTTP API。
3. 4 路 RRF 倒数排名融合：Dense 向量 + FTS 全文 + 内置 ripgrep 确凿字面量 + AST 结构排名，自动对 verifyJwtTokenSignature 等长标识符做驼峰/下划线拆词，Top-3 召回率达 94.6%。
4. 架构大纲地图 (get_codebase_outline)：消耗数百 Token 快速理清多语言项目全貌与模块职责。
5. 内置全平台预编译 ripgrep：内置 Windows (rg.exe)、Linux (musl/arm64) 与 macOS (Apple Silicon/Intel) 静态二进制，零环境依赖。
6. 恒定微批次低显存流水线：300 块微批次流式写入 + 垃圾回收约束，常驻内存低于 160MB，彻底根除大仓库 OOM 137。

实测性能基准

测试项目	源码规模	提取符号	索引耗时	内存峰值	工具轮数 (With vs Without)
Spring Boot	8,373 文件	68,085	22 秒 (轻量模式)	< 140 MB	1 vs 38 (0 次翻文件)
FastAPI	1,171 文件	6,545	38 秒	< 280 MB	1 vs 18 (0 次翻文件)
Spring PetClinic	50 文件	250	1.7 秒 (热缓存)	< 160 MB	1 vs 12 (0 次翻文件)

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License

MIT License. See LICENSE for details.