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"]