ContextCore: An MCP server for Claude (or any AI tool) that enables massive token saving through hybrid search (BM25 + Embeddings)
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"contextcore": {
"cwd": "C:\\Users\\USER\\Documents\\SDKSearchImplementation\\SearchEmbedSDK",
"env": {
"CONTEXTCORE_API_BASE_URL": "http://127.0.0.1:8000",
"CONTEXTCORE_MCP_TIMEOUT_SECONDS": "120"
},
"args": [
"C:\\Users\\USER\\Documents\\SDKSearchImplementation\\SearchEmbedSDK\\mcp_server.py"
],
"command": "C:\\Users\\USER\\Documents\\SDKSearchImplementation\\SearchEmbedSDK\\.venv\\Scripts\\python.exe"
}
}
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One MCP server for all your local files. Search everything, send only what matters to AI.
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One MCP server for all your local files. Search everything, send only what matters to AI.
Stop pasting entire files into Claude. ContextCore indexes your notes, code, documents, images, audio, and video locally — then exposes a single MCP server that any AI tool can query. Instead of bloating your context window, Claude searches first and retrieves only the relevant chunks.
57% fewer tokens. Same answers. No cloud.
| Benchmark Setup | Baseline Context | ContextCore Context | Reduction |
|---|---|---|---|
| SciFact (top-5 retrieved docs vs chunked context) | 1,723.5 tokens/query | 733.4 tokens/query | 57.45% |
Works with: Claude Desktop · Claude Code · Cursor · Cline · OpenCode · any MCP-compatible tool
Most developers working across large codebases or document collections hit the same wall: pasting everything into context is expensive, slow, and hits limits. RAG pipelines require infrastructure. Other memory tools are cloud-only or single-format.
ContextCore is a local-first MCP server that does hybrid search (BM25 + embeddings) across every file type you care about — and registers itself with your AI tools automatically. One install. One server. All your data.
It is not Supermemory. It does not sync to a cloud. Your files stay on your machine. What it gives you is supercharged retrieval across every local file format, surfaced directly inside Claude and other tools via MCP.
This is the one MCP to rule them all design: instead of managing separate MCP servers for different file types, you have one local server with a consistent search API across text, code, images, audio, and video.
contextcore init — indexes your chosen folders (text, code, images, audio, video)search tool callThe hybrid search combines BM25 (keyword) and embeddings (semantic) so it handles both exact lookups ("find the function called parse_config") and fuzzy concept searches ("where did I write about the retry logic?").

Install from PyPI:
python -m pip install contextcore
Optional source install (for contributors):
git clone https://github.com/lucifer-ux/SearchEmbedSDK.git
cd SearchEmbedSDK
python -m pip install -e .
Then run the setup wizard:
contextcore init

Gif is sped up to skip the installation parts.
That's it. ContextCore indexes your files, registers with your AI tools, and runs in the background. No config files to edit.
ContextCore is local-first by default. If you want text_search_implementation_v2 to store and fetch its text index from Turso instead of the local SQLite file, configure Turso with environment variables or a .env file.
This currently applies only to text search storage. Image, audio, video, and code indexes still use their existing storage paths.
Create a .env file in the ContextCore repo root, your current working directory, or ~/.contextcore/.env:
TURSO_DATABASE_URL="libsql://..."
TURSO_AUTH_TOKEN="..."
CONTEXTCORE_TEXT_STORAGE_BACKEND="turso"
You can also point ContextCore at a specific env file:
export CONTEXTCORE_ENV_FILE="/path/to/contextcore.env"
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