About filesift
FileSift is a local codebase search utility for AI coding agents, combining keyword and semantic search to locate relevant code by intent. It offers CLI, MCP server, and skill-based access while keeping code on the machine.
Key features
- 01Hybrid keyword and semantic code search
- 02Index projects locally
- 03Search related files by imports and similarity
- 04CLI, MCP server, and agent skill access
Use cases
- 01Locate authentication or database code by description
- 02Find files related to a given source file
- 03Let coding agents search a project without sending code elsewhere
MCP support
FileSift provides a lightweight STDIO MCP server with tools for code search, related-file discovery, indexing, and index status.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README
FileSift
A local, open-source utility that helps AI coding agents intelligently search and understand codebases.
FileSift lets your AI coding agent search across a codebase based on what code does, rather than what it looks like. Instead of sifting through entire files after a grep, your agent can jump straight to the most relevant code using natural language queries like "authentication middleware" or "database connection pooling". Everything runs locally on your machine — your code never leaves your environment.
Key benefits:
- Smarter search — hybrid keyword + semantic search finds code by intent, not just string matching
- Less context wasted — agents get pointed to the right files immediately, saving token budget on exploration
Installation
pip install filesift
Usage
There are three ways to use FileSift, depending on your workflow:
1. CLI
The most straightforward approach. Good for testing queries, managing indexes, and configuring settings.
# Index a project
filesift index /path/to/your/project
# Search for files by what they do
filesift find "authentication and session handling"
# Search in a specific directory
filesift find "retry logic for API calls" --path /path/to/project
2. MCP Server
Installing FileSift also provides a filesift-mcp command — a lightweight MCP server that exposes indexing and search as tools over STDIO. This works with most popular coding agents including Claude Code, Cursor, Copilot, and more.
Add it to your agent's MCP configuration:
{
"mcpServers": {
"filesift": {
"command": "filesift-mcp"
}
}
}
The MCP server exposes four tools:
filesift_search— search an indexed codebase by natural language queryfilesift_find_related— find files related to a given file via imports and semantic similarityfilesift_index— index a directory to enable searchingfilesift_status— check indexing status of a directory
3. Skills
FileSift ships with a search-codebase skill that can be installed directly into your coding agent's skill directory. This lets the agent interact with the FileSift CLI through bash, without requiring MCP support.
# Install for Claude Code (default)
filesift skill install
# Install for other agents
filesift skill install --agent cursor
filesift skill install --agent copilot
filesift skill install --agent codex
Supported agents: claude, codex, cursor, copilot, gemini, roo, windsurf.
How It Works
FileSift uses a daemonized embedding model to keep searches fast. At its core, it generates embeddings from code descriptions and performs searches against small vector stores called indexes.
-
Indexing —
filesift indexfirst builds a fast keyword/structural index (completes in seconds), then triggers background semantic indexing that generates embeddings for each file. -
Daemon — A background daemon loads indexes into memory and automatically shuts down after a configurable period of inactivity. After the first cold-start search, subsequent searches are near-instant.
-
Search — Queries are matched using both keyword (BM25) and semantic (FAISS) search, then combined via Reciprocal Rank Fusion for the best of both approaches.
Indexes are stored in a .filesift directory within each indexed project.
Configuration
FileSift uses a TOML configuration file, manageable via the CLI:
# View all settings
filesift config list --all
# Set a value
filesift config set search.MAX_RESULTS 20
filesift config set daemon.INACTIVITY_TIMEOUT 600
# Manage ignore patterns
filesift config add-ignore "node_modules" ".venv"
filesift config list-ignore
Configuration sections: search, indexing, daemon, models, paths.
Contributing
Contributions are welcome! To get started:
git clone https://github.com/roshunsunder/filesift.git
cd filesift
pip install -e .
- Fork the repository
- Create a feature branch (
git checkout -b feature/my-feature) - Commit your changes and open a pull request
License
Apache 2.0 — see LICENSE for details.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.