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circlfi-mcp

circlfi-mcp

Remote
circlfi.comFinance0Updated 15w ago

About circlfi-mcp

circlfi-mcp provides CirclFi equity valuation data to AI clients, covering US stocks with daily recalculated models and company-quality and value-trap scores. It offers a hosted endpoint and a local stdio option, with a free tier and premium tools.

Key features

  1. 01Stock intrinsic valuation profiles
  2. 02Quality and value-trap scores
  3. 03Screening across US stocks
  4. 04Three models in free tier
  5. 05Hosted HTTP and local stdio options

Use cases

  1. 01Compare ticker valuations against fair value
  2. 02Screen stocks by quality and risk scores
  3. 03Export screened results as CSV
  4. 04Review valuations from multiple models

MCP support

The server provides stock valuation and market screening tools to MCP clients. Clients can connect to its hosted Streamable HTTP endpoint or run the local npm-based stdio server.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://circlfi.com/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Packages
npmcirclfi-mcp1.1.0

Tools

Details

From the project README

CirclFi MCP Server

npm version MCP License: ISC

The official Model Context Protocol (MCP) server for CirclFi — the institutional-grade equity valuation platform.

Give any AI assistant direct access to 13 valuation models across 5,900+ US stocks, recalculated daily from SEC EDGAR, FRED, and GDELT data. Intrinsic values, Quality of Company scores (32 fundamental signals), and Value Trap risk scores — as native AI tools.

Two ways to connect

Option A — Remote server (no install, works everywhere)

Point any MCP-compatible client at the hosted endpoint:

https://circlfi.com/mcp
  • Transport: Streamable HTTP (stateless), current MCP spec
  • Auth: none needed for the free tier. Premium subscribers send their Gumroad purchase email as a bearer token: Authorization: Bearer [email protected]
  • Works with ChatGPT connectors (developer mode), Claude custom connectors (web/desktop), agent frameworks, and any cloud client that can't run local processes.
Option B — Local stdio server (npm)
npx -y circlfi-mcp

(Installing from GitHub also works: npx -y github:negm17111995/Circlfi-MCP)

Install in your client

Claude Desktop

Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json, Windows: %APPDATA%\Claude\claude_desktop_config.json):

{
  "mcpServers": {
    "circlfi": {
      "command": "npx",
      "args": ["-y", "circlfi-mcp"],
      "env": { "CIRCLFI_EMAIL": "" }
    }
  }
}

Restart Claude Desktop and look for the tools icon. Premium subscribers: put your Gumroad purchase email in CIRCLFI_EMAIL.

Claude Code
claude mcp add circlfi -- npx -y circlfi-mcp

Or with premium access:

claude mcp add circlfi -e [email protected] -- npx -y circlfi-mcp
Cursor

One-click install →

Or add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "circlfi": { "command": "npx", "args": ["-y", "circlfi-mcp"] }
  }
}
VS Code (Copilot / MCP)
code --add-mcp '{"name":"circlfi","command":"npx","args":["-y","circlfi-mcp"]}'
Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "circlfi": { "command": "npx", "args": ["-y", "circlfi-mcp"] }
  }
}
ChatGPT (connectors / developer mode)

ChatGPT connects to remote MCP servers. In Settings → Connectors → Advanced → Developer mode, add a custom connector with the URL:

https://circlfi.com/mcp
Any other MCP client
  • Local: command npx, args ["-y", "circlfi-mcp"], optional env CIRCLFI_EMAIL
  • Remote: Streamable HTTP at https://circlfi.com/mcp, optional header Authorization: Bearer <gumroad-email>

Available tools

get_stock_valuation

Complete intrinsic valuation profile for any US ticker: fair value, implied upside, and statistical confidence per model, plus the Quality of Company (QOC) score and Value Trap risk score.

Free tier — 3 models, no signup: Bayesian DCF (10,000-run Monte Carlo with jump-diffusion), Earnings Power Value (Greenwald), CUCE Ensemble (meta-model).

Premium — all 13 models: the above plus EROIC Spread, First Chicago, Markov DDM, ML-RIV, Dynamic NAV, PWERM, Regime Cross-Sectional, Sentiment SOTP, FTNN Topology, and RCMH-DCF. Full methodology: circlfi.com/methodology

get_market_screener (Premium)

Screen all 5,900+ US stocks in one call. Filter by minimum QOC, maximum Value Trap score, or industry; return JSON for analysis or CSV for download.

Example prompts

  • "What is the intrinsic value of AAPL according to CirclFi?"
  • "Screen for Software stocks with QOC above 8 and Value Trap below 10, output as CSV."
  • "Compare the Bayesian DCF valuations of MSFT and GOOGL and tell me which trades further below fair value."

How it works (security & architecture)

This repository is a thin client — no proprietary algorithms, no API keys. Tool calls are forwarded to the CirclFi API (https://circlfi.com/api/mcp), which verifies premium subscriptions with Gumroad in real time and returns model output. Free-tier requests need no account at all. The hosted remote endpoint (https://circlfi.com/mcp) exposes the identical tools directly over Streamable HTTP.

All output is quantitative model data for educational purposes — not financial advice (disclaimer).

Pricing

  • Free: 3 models per stock, unlimited tickers
  • Premium ($39/mo or $299/yr): all 13 models + market screener — subscribe via Gumroad, then use your purchase email as CIRCLFI_EMAIL (local) or bearer token (remote)

Support

Open a GitHub Issue for server problems. For valuation methodology or subscription questions, see circlfi.com.

Comments

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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.