ctx++

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.

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Why ctx++

ctx++ is built around three principles:

1. Never time out. SQLite with indexed vector and FTS search means queries are fast regardless of codebase size. The MCP server loads in under 2 seconds and all tool calls complete within the MCP timeout window.
2. Return the right code, not just matching code. The call graph traversal finds everything involved in a feature by walking real call relationships -- the same way a senior engineer would read the code.
3. Minimal setup. A single Go binary plus Ollama for embeddings. No cloud services, no API keys, no Docker.

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Tools

Tool	Description
ctxpp_index	Index or reindex the codebase. Run once after install; incremental updates happen automatically.
ctxpp_search	Search by identifier name (keyword) or natural language (semantic). Returns symbol definitions with file paths and line numbers.
ctxpp_file_skeleton	Return all symbols in a file with signatures and line ranges, without reading the full body. Cheap way to understand a file's API surface.
ctxpp_feature_traverse	Given an exact symbol name, return related symbols by walking the call graph outward via BFS. The auto-generated feature hub.
ctxpp_blast_radius	Given a symbol, return every location in the codebase that references it. Answers "what breaks if I change this?"

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Supported Languages

Language	Extensions	Symbols Extracted
Go	.go	functions, methods, structs, interfaces, types, constants, variables
Java	.java	classes, interfaces, enums, methods, constructors, fields
Kotlin	.kt, .kts	functions, methods, classes, interfaces, properties, imports
JavaScript	.js, .mjs, .cjs, .jsx	functions, classes, methods, arrow functions
TypeScript	.ts, .tsx, .mts, .cts	functions, classes, interfaces, type aliases, enums
Rust	.rs	functions, structs, enums, traits, impl methods, type aliases
C#	.cs	classes, interfaces, methods, fields, imports
C	.c, .h	functions, structs, enums, typedefs, function-like macros
C++	.cpp, .cc, .cxx, .hpp, .hh, .hxx	functions, methods, classes, structs, enums, namespaces, templates
SQL	.sql	tables, views, indexes, functions, procedures, triggers
Markdown	.md, .mdx	headings (as sections)
HTML	.html, .htm	headings, script/style blocks
Shell	.sh, .bash, .zsh, .dash	functions
Protobuf	.proto	messages, services, RPCs, enums
HTTP	.http, .rest	named requests
Text/Config	.txt, .env, Makefile, Dockerfile, LICENSE, etc.	file-level document symbol

Want to add another language? See docs/ADDING-LANGUAGE-SUPPORT.md for a step-by-step implementation template and PR checklist.

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Prerequisites

• Go 1.24+ for building from source
• Ollama for semantic search embeddings

# 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.

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Install

go install github.com/cavenine/ctxpp@latest

Or build from source:

git clone https://github.com/cavenine/ctxpp
cd ctxpp
make build

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Quick Start

1. Index your project

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

2. Add to your MCP config

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"
      }
    }
  }
}

3. Use it

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

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Ollama Integration

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"
}

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AWS Bedrock Integration

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:

	Ollama (local GPU)	Bedrock
Per-query embed latency	~25ms	100-460ms
Index time (kubernetes, 318K symbols)	47m	~7.5h
GPU required	Yes	No
Cost	Free (local)	AWS API pricing
Horizontal scaling	Limited by GPU	High (100-200 concurrent)
Quality (kubernetes benchmark)	4.8/5	4.7/5

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.

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OpenAI-Compatible Embeddings Integration

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_URL
• CTXPP_OPENAI_MODEL
• CTXPP_OPENAI_DIMS
• CTXPP_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.

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Configuration

All configuration is via environment variables.

Variable	Default	Description
CTXPP_PROJECT	.	Path to the project root to index
CTXPP_OLLAMA_URL	http://localhost:11434	Ollama API endpoint
CTXPP_OLLAMA_MODEL	bge-m3	Ollama embedding model
CTXPP_EMBED_BACKEND	(auto-detect)	Embedding backend: auto, ollama, tei, openai, bedrock, or bundled
CTXPP_OPENAI_URL	https://api.openai.com	OpenAI-compatible embeddings API base URL
CTXPP_OPENAI_MODEL	(required with openai)	OpenAI-compatible embedding model
CTXPP_OPENAI_API_KEY	(optional)	Bearer token for OpenAI-compatible providers
CTXPP_OPENAI_DIMS	(required with openai)	Embedding dimensions for the selected OpenAI-compatible model
CTXPP_WORKERS	number of CPUs	Parallel workers for initial indexing
CTXPP_EMBED_CONCURRENCY	10	Max concurrent embedding requests (mainly Bedrock)

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CLI Reference

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

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Architecture

ctx++ is written in Go and built on:

• go-tree-sitter -- native C tree-sitter bindings for fast, accurate AST parsing across all supported languages
• SQLite via modernc.org/sqlite (pure Go, no CGO) with FTS5 for full-text search and brute-force cosine similarity for vector search
• Ollama for embedding generation (default model: bge-m3)
• MCP SDK for stdio-based MCP transport

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.

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How Feature Traversal Works

When you ask ctxpp_feature_traverse about a symbol (e.g. "HandleLogin"):

1. A keyword search finds symbols with that exact name in the index
2. The call graph is walked outward from each seed via BFS — what does this function call? What do those functions call?
3. Results are returned in BFS order (seed first, then direct callees, then transitive callees) up to the configured depth (default: 3 hops)

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?

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License

MIT