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Fast, accurate codebase intelligence for AI coding agents.
ctx++ is an MCP (Model Context Protocol) server that gives AI agents precise, structured understanding of large codebases. It extracts symbols using native tree-sitter parsing, indexes them in SQLite with both full-text and vector search, and traces call graphs to automatically map how features are assembled across files -- no hand-maintained documentation required.
A local MCP server that gives AI agents fast symbol search, call-graph traversal, and blast-radius analysis over your codebase.
ctx++ is built around three principles:
Want to add another language? See docs/ADDING-LANGUAGE-SUPPORT.md for a step-by-step implementation template and PR checklist.
# Install Ollama, then pull the default embedding model:
ollama pull bge-m3
Without Ollama, ctx++ still works but provides keyword search only. Semantic search and feature traversal quality depend on embeddings.
go install github.com/cavenine/ctxpp@latest
Or build from source:
git clone https://github.com/cavenine/ctxpp
cd ctxpp
make build
ctxpp index --path /path/to/your/project
This creates .ctxpp/index.db in the project root. Add it to .gitignore:
.ctxpp/
Subsequent runs only re-process changed files. Branch switches self-heal automatically via the file watcher.
If parser logic changes but your source files do not, force a full reparse of supported files:
ctxpp index --path /path/to/your/project --force
The examples below use Ollama with bge-m3 (the default). If Ollama is not running, omit CTXPP_OLLAMA_* — ctx++ will fall back to keyword search only.
OpenCode (opencode.json in project root):
{
"mcp": {
"ctxpp": {
"type": "local",
"command": ["ctxpp", "mcp"],
"enabled": true,
"environment": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_OLLAMA_URL": "http://localhost:11434",
"CTXPP_OLLAMA_MODEL": "bge-m3"
}
}
}
}
Claude Code (.mcp.json):
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_OLLAMA_URL": "http://localhost:11434",
"CTXPP_OLLAMA_MODEL": "bge-m3"
}
}
}
}
Cursor / Windsurf (.cursor/mcp.json or .windsurf/mcp.json):
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_OLLAMA_URL": "http://localhost:11434",
"CTXPP_OLLAMA_MODEL": "bge-m3"
}
}
}
}
Ask your AI agent anything about the codebase:
use ctxpp to show me everything involved in account authentication
use ctxpp to find where FetchAccount is defined and what calls it
use ctxpp_blast_radius to tell me what breaks if I change the Account struct
ctx++ uses Ollama for embedding-based semantic search. The default model is bge-m3 (BAAI's BGE-M3, 1024 dimensions), which was selected through head-to-head quality benchmarks against multiple models on real codebases.
ollama pull bge-m3
ctx++ auto-detects Ollama on localhost:11434 at startup. If Ollama is not running, ctx++ falls back to keyword search only and prints a warning.
To use a different embedding model (e.g., all-minilm for faster indexing at the cost of some search quality):
"environment": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_OLLAMA_MODEL": "all-minilm"
}
For environments without a local GPU, ctx++ can use Amazon Titan Text Embeddings V2 via AWS Bedrock. Quality is comparable to the Ollama/bge-m3 default (4.7/5 vs 4.8/5 on the kubernetes benchmark).
Prerequisites: AWS credentials configured via ~/.aws/credentials, AWS_PROFILE, or IAM role. The identity needs bedrock:InvokeModel permission on amazon.titan-embed-text-v2:0.
Set CTXPP_EMBED_BACKEND=bedrock and the following env vars:
OpenCode (opencode.json in project root):
{
"mcp": {
"ctxpp": {
"type": "local",
"command": ["ctxpp", "mcp"],
"enabled": true,
"environment": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_EMBED_BACKEND": "bedrock",
"CTXPP_BEDROCK_REGION": "us-east-1",
"CTXPP_BEDROCK_MODEL": "amazon.titan-embed-text-v2:0",
"CTXPP_BEDROCK_DIMS": "1024",
"CTXPP_EMBED_CONCURRENCY": "100"
}
}
}
}
Claude Code (.mcp.json):
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_EMBED_BACKEND": "bedrock",
"CTXPP_BEDROCK_REGION": "us-east-1",
"CTXPP_BEDROCK_MODEL": "amazon.titan-embed-text-v2:0",
"CTXPP_BEDROCK_DIMS": "1024",
"CTXPP_EMBED_CONCURRENCY": "100"
}
}
}
}
Cursor / Windsurf (.cursor/mcp.json or .windsurf/mcp.json):
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_EMBED_BACKEND": "bedrock",
"CTXPP_BEDROCK_REGION": "us-east-1",
"CTXPP_BEDROCK_MODEL": "amazon.titan-embed-text-v2:0",
"CTXPP_BEDROCK_DIMS": "1024",
"CTXPP_EMBED_CONCURRENCY": "100"
}
}
}
}
Or for initial indexing from the command line:
export CTXPP_EMBED_BACKEND=bedrock
export CTXPP_BEDROCK_REGION=us-east-1
export CTXPP_BEDROCK_MODEL=amazon.titan-embed-text-v2:0
export CTXPP_BEDROCK_DIMS=1024
export CTXPP_EMBED_CONCURRENCY=100 # increase to 200 for large repos
ctxpp index --path /path/to/your/project
Trade-offs vs Ollama:
Bedrock is the right choice for CI/CD pipelines, cloud-hosted agents, or developer machines without a GPU. For interactive development with a GPU available, Ollama is faster.
ctx++ can also use any provider that exposes the OpenAI POST /v1/embeddings API. This includes OpenAI, OpenAI-compatible proxies, vLLM, LiteLLM, LocalAI, and Ollama's OpenAI-compatible endpoint.
Set CTXPP_EMBED_BACKEND=openai and configure:
CTXPP_OPENAI_URLCTXPP_OPENAI_MODELCTXPP_OPENAI_DIMSCTXPP_OPENAI_API_KEY (optional for local unauthenticated servers)Example with OpenAI hosted embeddings:
{
"mcpServers": {
"ctxpp": {
"command": "ctxpp",
"args": ["mcp"],
"env": {
"CTXPP_PROJECT": "/path/to/your/project",
"CTXPP_EMBED_BACKEND": "openai",
"CTXPP_OPENAI_URL": "https://api.openai.com",
"CTXPP_OPENAI_MODEL": "text-embedding-3-small",
"CTXPP_OPENAI_DIMS": "1536",
"CTXPP_OPENAI_API_KEY": "${OPENAI_API_KEY}"
}
}
}
}
Example with Ollama's OpenAI-compatible endpoint:
export CTXPP_EMBED_BACKEND=openai
export CTXPP_OPENAI_URL=http://localhost:11434
export CTXPP_OPENAI_MODEL=bge-m3
export CTXPP_OPENAI_DIMS=1024
ctxpp index --path /path/to/your/project
This backend is opt-in only. Auto-detection still prefers TEI, then Ollama, then bundled fallback.
All configuration is via environment variables.
ctxpp index [--path/-p <path>] [--force] Index or reindex a project (default: $CTXPP_PROJECT or current directory)
ctxpp backfill [--path/-p <path>] Re-embed symbols missing embedding vectors
ctxpp mcp Start the MCP server over stdio
ctxpp version Print version
ctx++ is written in Go and built on:
modernc.org/sqlite (pure Go, no CGO) with FTS5 for full-text search and brute-force cosine similarity for vector searchbge-m3)The index lives in a single .ctxpp/index.db file per project. The schema tracks files, symbols, embeddings, call edges, and import edges. All queries hit indexed columns -- no full-table scans, no loading the entire index into memory.
See PRD.md for full architecture and design decisions.
When you ask ctxpp_feature_traverse about a symbol (e.g. "HandleLogin"):
This gives you the full call tree rooted at a symbol — useful for understanding what a function orchestrates without reading every file manually. Use ctxpp_blast_radius for the reverse direction: what calls this function?
MIT
A local MCP server that gives AI agents fast symbol search, call-graph traversal, and blast-radius analysis over your codebase.
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FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
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Ctxpp ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-05 A local MCP server that gives AI agents fast symbol search, call-graph traversal, and blast-radius analysis over your codebase.
