qanat MCP Server
fidetolabs/qanat
[](https://agentrank-ai.com/tool/fidetolabs--qanat/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) An MCP server that turns filings and prices into valuation indicators, and tests each one inside its own industry. Connect it to your agent and ask.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
fidetolabs/qanat is a Python MCP server licensed under MIT. An MCP server that turns filings and prices into valuation indicators, and tests each one inside its own industry. Connect it to your agent and ask. Topics: alpha-research, backtesting, duckdb, mcp, quantitative-finance, trading-toolkit, mcp-server, model-context-protocol, quant.
Ranked #5163 out of 24846 indexed tools.
Ecosystem
Score Breakdown
305 stars → growing community
Last commit 1mo ago → recently updated
No issues filed → no history to score
1 contributor → solo project
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
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From the README
<p align="center"> </p> <h1 align="center">Qanat</h1> <p align="center"> <i>An MCP server for quant factor processing and backtesting.</i> </p> <p align="center"> <a href="https://github.com/fidetolabs/qanat/blob/main/LICENSE"></a> <a href="https://github.com/fidetolabs/qanat/blob/main/pyproject.toml"></a> <a href="#status"></a> <a href="https://discord.gg/JUmwATScS8"></a> </p> Qanat is a data platform for quant factors. Give it any source that has a timestamp. It processes the data, stores it, and serves it to your agent over MCP. You write one YAML file saying where the data comes from and what to do with it. Qanat runs that, replays it over history one date at a time, and prices what the portfolio held after fees. Connect it to your agent and ask. It is built for people who rebalance daily or weekly. You cannot beat a hedge fund on speed, and over that horizon you do not need to. ## Install Python 3.10 or newer. ```bash uv tool install qanat-fdtlRead full README on GitHub →
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