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🧠 KacheDB MCP Server

PyPI License Python Languages

High-Performance Model Context Protocol (MCP) server for KacheDB — exposing sub-millisecond in-memory caching, Tree-Sitter AST symbol intelligence, and SIMD semantic vector memory to Antigravity IDE, Claude Desktop, Cursor, and AI coding agents.


⚡ Why KacheDB for AI Agents?

AI coding assistants repeatedly re-read large codebases, parse ASTs, and crawl directories, wasting tens of thousands of tokens and adding hundreds of milliseconds of latency per turn.

kachedb-mcp connects your AI assistant directly to KacheDB's Megaslab pure-RAM SwissTable:

  • 🚀 Sub-50 Nanosecond Retrieval: Instant symbol inspection and exact KV hits in memory.
  • 🌳 Tree-Sitter AST Code Intelligence: Multi-language symbol graphs (functions, classes, structs, signatures, docstrings) across Python, Rust, Go, TypeScript, and JavaScript.
  • 🔍 Upward Anchor Discovery: Zero-config project root detection from any touched file — no arbitrary $HOME scans or guessing folder names.
  • 🔄 Incremental Git Watermarks: Sub-millisecond sync verification using commit SHA watermarks (kache:meta:<ws>:commit). Up-to-date projects are skipped with zero overhead.
  • 🧠 SIMD Semantic Vector Memory: Natural language concept and code recall powered by ARM NEON & AVX2/FMA cosine similarity kernels.
  • 🪙 Live Financial Telemetry: Tracks avoided latency, cumulative tokens saved, and real-time USD cost savings.

🛠️ MCP Tools Exposed (11 Tools)

🌳 Codebase Intelligence Tools

Tool Type Description
kache_explore_symbol ⚡ Exact AST Instant (< 50 ns) symbol inspection from SwissTable. Returns signatures, docstrings, line numbers, and file paths in a surgical < 300 token payload.
kache_index_workspace 🚀 Indexer Discovers projects, parses AST symbols across supported languages, and persists SwissTable keys and vector embeddings.
kache_workspace_status 📊 Status Inspects indexing status, total indexed symbols, language breakdown, commit watermark, and cache freshness.

🧠 Semantic Memory Tools

Tool Type Description
kache_semantic_search 🧠 Vector Natural language semantic search over cached codebases, PR reviews, and past decisions (top_k, threshold).
kache_save_context 🧠 Vector Save architectural patterns, bug solutions, or file digests with hierarchical chunking and SIMD vector embeddings.
kache_get_parent_document 📄 Context Retrieve the full unabridged parent document for chunked semantic matches.

⚡ Exact Key-Value Tools

Tool Type Description
kache_get ⚡ Exact Sub-millisecond exact key retrieval for code chunks, ASTs, and tool outputs.
kache_set ⚡ Exact Store string content in memory with optional TTL expiration.
kache_delete ⚡ Exact Remove a key or vector from cache and reclaim slab memory.

📊 Telemetry & Health Tools

Tool Type Description
kache_stats 📊 Health Real-time connection health, active vector counts, and RAM footprint.
kache_telemetry 📊 Telemetry Live cumulative tokens saved, latency saved (seconds), hit ratios, and estimated USD saved.

🚀 Quickstart

1. Start the KacheDB Server Daemon

Ensure your KacheDB daemon is running locally:

kachedb-server --port 6380

2. Configure in Antigravity IDE / Claude Desktop / Cursor

Add kachedb to your MCP configuration file (mcp_config.json or claude_desktop_config.json):

No hardcoded paths needed. When an agent opens or mentions any file, Upward Anchor Discovery automatically resolves the enclosing project root and indexes it on-demand:

{
  "mcpServers": {
    "kachedb": {
      "command": "uvx",
      "args": ["kachedb-mcp"],
      "env": {
        "KACHEDB_PORT": "6380"
      }
    }
  }
}

Pre-Configured Multi-Project Roots

To automatically discover and index projects on startup, set KACHEDB_WORKSPACE_ROOTS:

{
  "mcpServers": {
    "kachedb": {
      "command": "uvx",
      "args": ["kachedb-mcp"],
      "env": {
        "KACHEDB_PORT": "6380",
        "KACHEDB_WORKSPACE_ROOTS": "/path/to/projects:/path/to/work"
      }
    }
  }
}

Or install via Python pip:

pip install kachedb-mcp
{
  "mcpServers": {
    "kachedb": {
      "command": "kachedb-mcp",
      "env": {
        "KACHEDB_PORT": "6380"
      }
    }
  }
}

🌐 Multi-Language AST Support

Tree-Sitter parsers extract rich symbol graphs with zero external compiler dependencies:

Language Extensions Extracted Symbols
Python .py, .pyi Classes, Methods, Functions, Signatures, Docstrings
Rust .rs Structs, Enums, Traits, Impl Methods, Functions, Doc comments
Go .go Structs, Interfaces, Methods, Functions, Package comments
TypeScript .ts, .tsx Classes, Interfaces, Types, Methods, Functions, JSDoc
JavaScript .js, .jsx, .mjs Classes, Methods, Functions, JSDoc

⚙️ Environment Configuration

Variable Default Description
KACHEDB_HOST 127.0.0.1 KacheDB daemon hostname or IP
KACHEDB_PORT 6380 KacheDB daemon TCP port
KACHEDB_WORKSPACE_ROOTS (empty) Optional directory list (separated by : on Unix or ; on Windows) for multi-project discovery
KACHEDB_AUTO_INDEX 1 Enable autonomous lazy workspace indexing (1 or 0)
KACHEDB_INDEX agent_semantic_memory Target vector index name for semantic memory
KACHEDB_THRESHOLD 0.80 Minimum cosine similarity (0.0 – 1.0) for semantic hits
KACHEDB_TOKEN_COST_PER_MILLION 3.0 Input token cost in USD per 1M tokens for telemetry accounting
KACHEDB_DEFAULT_TTL 86400 Default cache lifetime in seconds (24h)
KACHEDB_EMBEDDER auto Embedding provider (auto, fastembed, transformers, openai, mock)
OPENAI_API_KEY (optional) API key if using KACHEDB_EMBEDDER=openai

📄 License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.

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