by stefanoamorelli
Access FRED to retrieve economic time series data, including the consumer confidence index and composite leading indicat
Connects to the Federal Reserve Economic Data API to retrieve and analyze economic time series data like inflation rates, unemployment figures, and other financial indicators. Provides access to 800,000+ official economic datasets from the Fed.
FRED (Federal Reserve Economic Data) is a community-built MCP server published by stefanoamorelli that provides AI assistants with tools and capabilities via the Model Context Protocol. Access FRED to retrieve economic time series data, including the consumer confidence index and composite leading indicat It is categorized under finance, analytics data.
You can install FRED (Federal Reserve Economic Data) in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
AGPL-3.0
FRED (Federal Reserve Economic Data) is released under the AGPL-3.0 license.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
FRED (Federal Reserve Economic Data) is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
FRED (Federal Reserve Economic Data) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
FRED (Federal Reserve Economic Data) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: FRED (Federal Reserve Economic Data) is the kind of server we cite when onboarding engineers to host + tool permissions.
Strong directory entry: FRED (Federal Reserve Economic Data) surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend FRED (Federal Reserve Economic Data) for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, FRED (Federal Reserve Economic Data) benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: FRED (Federal Reserve Economic Data) is the kind of server we cite when onboarding engineers to host + tool permissions.
FRED (Federal Reserve Economic Data) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
I recommend FRED (Federal Reserve Economic Data) for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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[!IMPORTANT] Disclaimer: This open-source project is not affiliated with, sponsored by, or endorsed by the Federal Reserve or the Federal Reserve Bank of St. Louis. "FRED" is a registered trademark of the Federal Reserve Bank of St. Louis, used here for descriptive purposes only.
A Model Context Protocol (MCP) server providing universal access to all 800,000+ Federal Reserve Economic Data (FRED®) time series through three powerful tools.
https://github.com/user-attachments/assets/66c7f3ad-7b0e-4930-b1c5-a675a7eb1e09
[!TIP] If you use this project in your research or work, please cite it using the CITATION.cff file, or use the following citation:
APA Format:
Amorelli, S. (2025). Federal Reserve Economic Data MCP (Model Context Protocol) Server (Version 1.0.2) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.14536707
BibTeX:
@software{amorelli_2025_14536707,
author = {Amorelli, Stefano},
title = {{Federal Reserve Economic Data MCP (Model Context
Protocol) Server}},
month = jan,
year = 2025,
publisher = {Zenodo},
version = {1.0.2},
doi = {10.5281/zenodo.14536707},
url = {https://doi.org/10.5281/zenodo.14536707}
}
To install Federal Reserve Economic Data Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @stefanoamorelli/fred-mcp-server --client claude
git clone https://github.com/stefanoamorelli/fred-mcp-server.git
cd fred-mcp-server
pnpm install
pnpm build
This server requires a FRED® API key. You can obtain one from the FRED® website.
Install the server, for example, on Claude Desktop, modify the claude_desktop_config.json file and add the following configuration:
{
"mcpServers": {
"FRED MCP Server": {
"command": "/usr/bin/node",
"args": [
"<PATH_TO_YOUR_CLONED_REPO>/fred-mcp-server/build/index.js"
],
"env": {
"FRED_API_KEY": "<YOUR_API_KEY>"
}
}
}
}
You can also run the FRED MCP Server using Docker. Add this configuration to your claude_desktop_config.json:
{
"mcpServers": {
"fred-mcp": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"FRED_API_KEY=<your-key-here>",
"stefanoamorelli/fred-mcp-server:latest"
],
"env": {}
}
}
}
Replace <your-key-here> with your actual FRED API key.
For network deployments, you can run the server with Streamable HTTP transport instead of stdio:
# Using CLI flag
node build/index.js --http
# Or using environment variable
TRANSPORT=http node build/index.js
# Custom port (default is 3000)
PORT=8080 node build/index.js --http
The server will be available at http://localhost:3000/mcp (or your custom port).
Example client request:
# Initialize session
curl -X POST http://localhost:3000/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"my-client","version":"1.0.0"}}}'
# Use the mcp-session-id from the response header for subsequent requests
curl -X POST http://localhost:3000/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "mcp-session-id: <session-id-from-init>" \
-d '{"jsonrpc":"2.0","id":2,"method":"tools/list"}'
This MCP server provides three comprehensive tools to access all 800,000+ FRED® economic data series:
fred_browseDescription: Browse FRED's complete catalog through categories, releases, or sources.
Parameters:
browse_type (required): Type of browsing - "categories", "releases", "sources", "category_series", "release_series"category_id (optional): Category ID for browsing subcategories or series within a categoryrelease_id (optional): Release ID for browsing series within a releaselimit (optional): Maximum number of results (default: 50)offset (optional): Number of results to skip for paginationorder_by (optional): Field to order results bysort_order (optional): "asc" or "desc"fred_searchDescription: Search for FRED economic data series by keywords, tags, or filters.
Parameters:
search_text (optional): Text to search for in series titles and descriptionssearch_type (optional): "full_text" or "series_id"tag_names (optional): Comma-separated list of tag names to filter byexclude_tag_names (optional): Comma-separated list of tag names to excludelimit (optional): Maximum number of results (default: 25)offset (optional): Number of results to skip for paginationorder_by (optional): Field to order by (e.g., "popularity", "last_updated")sort_order (optional): "asc" or "desc"filter_variable (optional): Filter by "frequency", "units", or "seasonal_adjustment"filter_value (optional): Value to filter the variable byfred_get_seriesDescription: Retrieve data for any FRED series by its ID with support for transformations and date ranges.
Parameters:
series_id (required): The FRED series ID (e.g., "GDP", "UNRATE", "CPIAUCSL")observation_start (optional): Start date in YYYY-MM-DD formatobservation_end (optional): End date in YYYY-MM-DD formatlimit (optional): Maximum number of observationsoffset (optional): Number of observations to skipsort_order (optional): "asc" or "desc"units (optional): Data transformation:
frequency (optional): Frequency aggregation ("d", "w", "m", "q", "a")aggregation_method (optional): "avg" (average), "sum", or "eop" (end of period)With these three tools, you can:
[!NOTE] Want to be featured? Tag Stefano Amorelli on LinkedIn or @stefanoamorelli on X in your post about using FRED MCP Server, or submit a PR to add your shoutout!
We're grateful for the community support! Here are some mentions from amazing people:
<details open> <summary><b>Scott G</b> - "One of my breakthrough moments for 'getting' what is possible with Claude was this fred-mcp-server project..."</summary> <br> <a href="https://www.linkedin.com/posts/sgoley_as-many-of-us-continue-to-use-llms-more-and-activity-7372401049669885952-ha6M"> <img src="assets/social/linkedin-sgoley.jpg" alt="LinkedIn post by Scott G - Fintech & Data Analytics Professional" width="600"> </a> <br> <i>Scott G - Fintech & Data Analytics Professional</i> | <a href="https://www.linkedin.com/in/sgoley/">LinkedIn Profile</a> </details> <details open> <summary><b>John Shelburne</b> - "The FRED MCP Server is a game-changer for financial analysis..."</summary> <br> <a href="https://www.linkedin.com/posts/shelburne_ai-finance-innovation-activity-7341141860880478210-JQe4"> <img src="assets/social/linkedin-john-shelburne.jpg" alt="LinkedIn post by John Shelburne" width="600"> </a> <br> <i>John Shelburne - Fixed Income Fintech Leader with 20+ Years of Experience | Machine Learning & Cloud Computing Specialist</i> | <a href="https://www.linkedin.com/in/shelburne/">LinkedIn Profile</a> </details> <!-- Add more social media posts here using the format above -->See TESTING.md for more details.
# Run all tests
pnpm test
# Run specific tests
pnpm test:registry
This open-source project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). This means:
For commercial licensing options or other licensing inquiries, please contact [email protected].
© 2025 [Stefano Amorelli](https://
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.