🧠 KacheDB MCP Server
High-Speed Model Context Protocol (MCP) server for KacheDB — exposing sub-millisecond in-memory caching and SIMD semantic vector memory to Antigravity IDE, Claude Desktop, Cursor, and AI coding agents.
⚡ Why KacheDB for AI Agents?
AI coding assistants (like Antigravity IDE and Claude Desktop) repeatedly re-read large codebases, AST parses, and architecture plans, wasting 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 cache:
- 🚀 Sub-50 Microsecond Retrieval: $< 50\ \mu\text{s}$ cache hits in memory.
- 🧠 SIMD Semantic Vector Memory: Natural language concept and code recall powered by ARM NEON & AVX2/FMA cosine similarity kernels.
- 🪙 Massive Token & Cost Savings: Saves up to 80% of repetitive prompt tokens and tracks cumulative financial savings in real time.
🛠️ MCP Tools Exposed
| 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 an architectural pattern, bug solution, or file digest with SIMD vector embeddings. |
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. |
kache_stats |
📊 Metrics | Real-time connection health, active vector counts, and RAM footprint. |
kache_telemetry |
📊 Metrics | Live cumulative tokens saved, latency saved (seconds), and hit ratios. |
🚀 Quickstart
1. Start the KacheDB Server Daemon
Ensure your KacheDB daemon is running locally:
kachedb-server --port 6379
2. Configure in Antigravity IDE / Claude Desktop / Cursor
Add kachedb to your MCP configuration file (mcp_config.json or claude_desktop_config.json):
{
"mcpServers": {
"kachedb": {
"command": "uvx",
"args": ["kachedb-mcp"],
"env": {
"KACHEDB_HOST": "127.0.0.1",
"KACHEDB_PORT": "6379",
"KACHEDB_INDEX": "agent_semantic_memory",
"KACHEDB_THRESHOLD": "0.80"
}
}
}
}
Or run via Python pip:
pip install kachedb-mcp
{
"mcpServers": {
"kachedb": {
"command": "kachedb-mcp"
}
}
}
⚙️ Environment Configuration
| Variable | Default | Description |
|---|---|---|
KACHEDB_HOST |
127.0.0.1 |
KacheDB daemon hostname or IP |
KACHEDB_PORT |
6379 |
KacheDB daemon TCP port |
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_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.
Release files for kachedb-mcp 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| kachedb_mcp-0.1.1.tar.gz | 16.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| kachedb_mcp-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 30.1 kB
Release files / kachedb_mcp-0.1.1.tar.gz
| Download URL | kachedb_mcp-0.1.1.tar.gz |
|---|---|
| Size | 16.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Download URL | kachedb_mcp-0.1.1-py3-none-any.whl |
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| Size | 14.0 kB |
| Tags | Python 3 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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