Context+

Semantic Intelligence for Large-Scale Engineering.

Context+ is an MCP server designed for developers who demand 99% accuracy. By combining RAG, Tree-sitter AST, Spectral Clustering, and Obsidian-style linking, Context+ turns a massive codebase into a searchable, hierarchical feature graph.

Video.Project.5.mp4

Tools

Discovery

Tool	Description
`get_context_tree`	Structural AST tree of a project with file headers and symbol ranges (line numbers for functions/classes/methods). Dynamic pruning shrinks output automatically.
`get_file_skeleton`	Function signatures, class methods, and type definitions with line ranges, without reading full bodies. Shows the API surface.
`semantic_code_search`	Search by meaning, not exact text. Uses embeddings over file headers/symbols and returns matched symbol definition lines.
`semantic_identifier_search`	Identifier-level semantic retrieval for functions/classes/variables with ranked call sites and line numbers.
`semantic_navigate`	Browse codebase by meaning using spectral clustering. Groups semantically related files into labeled clusters.

Analysis

Tool	Description
`get_blast_radius`	Trace every file and line where a symbol is imported or used. Prevents orphaned references.
`run_static_analysis`	Run native linters and compilers to find unused variables, dead code, and type errors. Supports TypeScript, Python, Rust, Go.

Code Ops

Tool	Description
`propose_commit`	The only way to write code. Validates against strict rules before saving. Creates a shadow restore point before writing.
`get_feature_hub`	Obsidian-style feature hub navigator. Hubs are `.md` files with `[[wikilinks]]` that map features to code files.

Version Control

Tool	Description
`list_restore_points`	List all shadow restore points created by `propose_commit`. Each captures file state before AI changes.
`undo_change`	Restore files to their state before a specific AI change. Uses shadow restore points. Does not affect git.

Memory & RAG

Tool	Description
`upsert_memory_node`	Create or update a memory node (concept, file, symbol, note) with auto-generated embeddings.
`create_relation`	Create typed edges between nodes (relates_to, depends_on, implements, references, similar_to, contains).
`search_memory_graph`	Semantic search with graph traversal — finds direct matches then walks 1st/2nd-degree neighbors.
`prune_stale_links`	Remove decayed edges (e^(-λt) below threshold) and orphan nodes with low access counts.
`add_interlinked_context`	Bulk-add nodes with auto-similarity linking (cosine ≥ 0.72 creates edges automatically).
`retrieve_with_traversal`	Start from a node and walk outward — returns all reachable neighbors scored by decay and depth.

Setup

Quick Start (npx / bunx)

No installation needed. Add Context+ to your IDE MCP config.

For Claude Code, Cursor, and Windsurf, use mcpServers:

{
  "mcpServers": {
    "contextplus": {
      "command": "bunx",
      "args": ["contextplus"],
      "env": {
        "OLLAMA_EMBED_MODEL": "nomic-embed-text",
        "OLLAMA_CHAT_MODEL": "gemma2:27b",
        "OLLAMA_API_KEY": "YOUR_OLLAMA_API_KEY"
      }
    }
  }
}

For VS Code (.vscode/mcp.json), use servers and inputs:

{
  "servers": {
    "contextplus": {
      "type": "stdio",
      "command": "bunx",
      "args": ["contextplus"],
      "env": {
        "OLLAMA_EMBED_MODEL": "nomic-embed-text",
        "OLLAMA_CHAT_MODEL": "gemma2:27b",
        "OLLAMA_API_KEY": "YOUR_OLLAMA_API_KEY"
      }
    }
  },
  "inputs": []
}

If you prefer npx, use:

"command": "npx"
"args": ["-y", "contextplus"]

Or generate the MCP config file directly in your current directory:

npx -y contextplus init claude
bunx contextplus init cursor
npx -y contextplus init opencode

Supported coding agent names: claude, cursor, vscode, windsurf, opencode.

Config file locations:

IDE	Config File
Claude Code	`.mcp.json`
Cursor	`.cursor/mcp.json`
VS Code	`.vscode/mcp.json`
Windsurf	`.windsurf/mcp.json`
OpenCode	`opencode.json`

CLI Subcommands

init [target] - Generate MCP configuration (targets: claude, cursor, vscode, windsurf, opencode).
skeleton [path] or tree [path] - (New) View the structural tree of a project with file headers and symbol definitions directly in your terminal.
[path] - Start the MCP server (stdio) for the specified path (defaults to current directory).

From Source

npm install
npm run build

Embedding Providers

Context+ supports two embedding backends controlled by CONTEXTPLUS_EMBED_PROVIDER:

Provider	Value	Requires	Best For
Ollama (default)	`ollama`	Local Ollama server	Free, offline, private
OpenAI-compatible	`openai`	API key	Gemini (free tier), OpenAI, Groq, vLLM

Ollama (Default)

No extra configuration needed. Just run Ollama with an embedding model:

ollama pull nomic-embed-text
ollama serve

Google Gemini (Free Tier)

Full Claude Code .mcp.json example:

{
  "mcpServers": {
    "contextplus": {
      "command": "npx",
      "args": ["-y", "contextplus"],
      "env": {
        "CONTEXTPLUS_EMBED_PROVIDER": "openai",
        "CONTEXTPLUS_OPENAI_API_KEY": "YOUR_GEMINI_API_KEY",
        "CONTEXTPLUS_OPENAI_BASE_URL": "https://generativelanguage.googleapis.com/v1beta/openai",
        "CONTEXTPLUS_OPENAI_EMBED_MODEL": "text-embedding-004"
      }
    }
  }
}

Get a free API key at Google AI Studio.

OpenAI

{
  "mcpServers": {
    "contextplus": {
      "command": "npx",
      "args": ["-y", "contextplus"],
      "env": {
        "CONTEXTPLUS_EMBED_PROVIDER": "openai",
        "OPENAI_API_KEY": "sk-...",
        "OPENAI_EMBED_MODEL": "text-embedding-3-small"
      }
    }
  }
}

Other OpenAI-compatible APIs (Groq, vLLM, LiteLLM)

Any endpoint implementing the OpenAI Embeddings API works:

{
  "mcpServers": {
    "contextplus": {
      "command": "npx",
      "args": ["-y", "contextplus"],
      "env": {
        "CONTEXTPLUS_EMBED_PROVIDER": "openai",
        "CONTEXTPLUS_OPENAI_API_KEY": "YOUR_KEY",
        "CONTEXTPLUS_OPENAI_BASE_URL": "https://your-proxy.example.com/v1",
        "CONTEXTPLUS_OPENAI_EMBED_MODEL": "your-model-name"
      }
    }
  }
}

Note: The semantic_navigate tool also uses a chat model for cluster labeling. When using the openai provider, set CONTEXTPLUS_OPENAI_CHAT_MODEL (default: gpt-4o-mini).

For VS Code, Cursor, or OpenCode, use the same env block inside your IDE's MCP config format (see Config file locations table above).

Architecture

Three layers built with TypeScript over stdio using the Model Context Protocol SDK:

Core (src/core/) - Multi-language AST parsing (tree-sitter, 43 extensions), gitignore-aware traversal, Ollama vector embeddings with disk cache, wikilink hub graph, in-memory property graph with decay scoring.

Tools (src/tools/) - 17 MCP tools exposing structural, semantic, operational, and memory graph capabilities.

Git (src/git/) - Shadow restore point system for undo without touching git history.

Runtime Cache (.mcp_data/) - created on server startup; stores reusable file, identifier, and call-site embeddings to avoid repeated GPU/CPU embedding work. A realtime tracker refreshes changed files/functions incrementally.

Config

Variable	Type	Default	Description
`CONTEXTPLUS_EMBED_PROVIDER`	string	`ollama`	Embedding backend: `ollama` or `openai`
`OLLAMA_EMBED_MODEL`	string	`nomic-embed-text`	Ollama embedding model
`OLLAMA_API_KEY`	string	-	Ollama Cloud API key
`OLLAMA_CHAT_MODEL`	string	`llama3.2`	Ollama chat model for cluster labeling
`CONTEXTPLUS_OPENAI_API_KEY`	string	-	API key for OpenAI-compatible provider (alias: `OPENAI_API_KEY`)
`CONTEXTPLUS_OPENAI_BASE_URL`	string	`https://api.openai.com/v1`	OpenAI-compatible endpoint URL (alias: `OPENAI_BASE_URL`)
`CONTEXTPLUS_OPENAI_EMBED_MODEL`	string	`text-embedding-3-small`	OpenAI-compatible embedding model (alias: `OPENAI_EMBED_MODEL`)
`CONTEXTPLUS_OPENAI_CHAT_MODEL`	string	`gpt-4o-mini`	OpenAI-compatible chat model for labeling (alias: `OPENAI_CHAT_MODEL`)
`CONTEXTPLUS_EMBED_BATCH_SIZE`	string (parsed as number)	`8`	Embedding batch size per GPU call, clamped to 5-10
`CONTEXTPLUS_EMBED_CHUNK_CHARS`	string (parsed as number)	`2000`	Per-chunk chars before merge, clamped to 256-8000
`CONTEXTPLUS_MAX_EMBED_FILE_SIZE`	string (parsed as number)	`51200`	Skip non-code text files larger than this many bytes
`CONTEXTPLUS_EMBED_NUM_GPU`	string (parsed as number)	-	Optional Ollama embed runtime `num_gpu` override
`CONTEXTPLUS_EMBED_MAIN_GPU`	string (parsed as number)	-	Optional Ollama embed runtime `main_gpu` override
`CONTEXTPLUS_EMBED_NUM_THREAD`	string (parsed as number)	-	Optional Ollama embed runtime `num_thread` override
`CONTEXTPLUS_EMBED_NUM_BATCH`	string (parsed as number)	-	Optional Ollama embed runtime `num_batch` override
`CONTEXTPLUS_EMBED_NUM_CTX`	string (parsed as number)	-	Optional Ollama embed runtime `num_ctx` override
`CONTEXTPLUS_EMBED_LOW_VRAM`	string (parsed as boolean)	-	Optional Ollama embed runtime `low_vram` override
`CONTEXTPLUS_EMBED_TRACKER`	string (parsed as boolean)	`true`	Enable realtime embedding refresh on file changes
`CONTEXTPLUS_EMBED_TRACKER_MAX_FILES`	string (parsed as number)	`8`	Max changed files processed per tracker tick, clamped to 5-10
`CONTEXTPLUS_EMBED_TRACKER_DEBOUNCE_MS`	string (parsed as number)	`700`	Debounce window before tracker refresh

Test

npm test
npm run test:demo
npm run test:all

Provide feedback

Saved searches

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Context+

Tools

Discovery

Analysis

Code Ops

Version Control

Memory & RAG

Setup

Quick Start (npx / bunx)

CLI Subcommands

From Source

Embedding Providers

Ollama (Default)

Google Gemini (Free Tier)

OpenAI

Other OpenAI-compatible APIs (Groq, vLLM, LiteLLM)

Architecture

Config

Test

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

Context+

Tools

Discovery

Analysis

Code Ops

Version Control

Memory & RAG

Setup

Quick Start (npx / bunx)

CLI Subcommands

From Source

Embedding Providers

Ollama (Default)

Google Gemini (Free Tier)

OpenAI

Other OpenAI-compatible APIs (Groq, vLLM, LiteLLM)

Architecture

Config

Test