Serveur MCP Context+

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.

https://github.com/user-attachments/assets/a97a451f-c9b4-468d-b036-15b65fc13e79

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.

Complementary server: pmll-memory-mcp (npx pmll-memory-mcp) is a separate MCP server by @drQedwards that adapts Context+'s long-term memory graph and adds short-term KV context memory, Q-promise deduplication, and a solution engine on top. See drQedwards/PPM for details.

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).

Including paths excluded by the workspace .gitignore

If your workspace .gitignore excludes a sub-directory that you still want indexed (common in monorepos where sub-projects under repos/, packages/, or vendor/ are gitignored at the top level), use --include or CONTEXTPLUS_EXTRA_ROOTS to add the paths back.

CLI form (repeatable):

bunx contextplus /path/to/workspace \
  --include repos/lacuna \
  --include repos/graphrag-core

Environment variable (fallback when no --include flag is set; uses the system path separator — : on Unix, ; on Windows):

CONTEXTPLUS_EXTRA_ROOTS=repos/lacuna:repos/graphrag-core \
  bunx contextplus /path/to/workspace

In .mcp.json the env form is usually more ergonomic:

{
  "mcpServers": {
    "contextplus": {
      "command": "bunx",
      "args": ["contextplus", "/path/to/workspace"],
      "env": {
        "CONTEXTPLUS_EXTRA_ROOTS": "repos/lacuna:repos/graphrag-core"
      }
    }
  }
}

Each path listed is walked independently of the workspace root, with a fresh ignore scope. Each path's own .gitignore is respected. Paths are validated at startup; invalid entries (non-existent, not a directory, outside the workspace) emit a stderr warning and are skipped.

Nested .gitignore files inside the workspace and inside each extra root are loaded and merged with inherited rules, matching git and ripgrep behavior.

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