Skip to main content
Glama
zsun4work

Fed Speech MCP

by zsun4work
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Fed Speech MCP

An MCP (Model Context Protocol) server that retrieves, parses, and analyzes speeches and testimonies from major Federal Reserve officers.

Features

  • 📡 RSS Feed Discovery - Automatically discovers new speeches from Fed RSS feeds

  • 🔍 Index Page Scanning - Backfill capability by scanning yearly index pages

  • 📝 Smart Parsing - Extracts metadata, speaker info, and clean text from Fed HTML pages

  • 📊 Feature Extraction - Detects topics (inflation, rates, labor market, etc.)

  • ⚖️ Importance Scoring - Rule-based scoring for market relevance

  • 💾 JSON Storage - Persistent storage with deduplication

  • 🔌 MCP Interface - Full MCP compatibility for AI assistants

Covered Content

Speakers

  • Chair of the Federal Reserve

  • Vice Chair of the Federal Reserve

  • Federal Reserve Governors

Content Types

  • Speeches

  • Congressional Testimony

  • Prepared Remarks

Installation

Prerequisites

  • Python 3.10 or higher

  • uv package manager

Install uv (if you don't have it):

# macOS / Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Windows powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Install the Project

cd fed-speech-mcp # Create virtual environment and install uv sync

That's it! The virtual environment is created and all dependencies are installed.

🚀 Quick Start: Test Locally in 5 Minutes

Want to see the MCP tools in action before connecting to an AI? Follow these steps:

Step 1: Install

cd fed-speech-mcp uv sync

Step 2: Fetch Speeches from Fed Website

uv run python scripts/test_local.py refresh --limit 5

You'll see output like:

🔄 REFRESHING SPEECHES FROM FED WEBSITE ================================================== 📡 Discovering documents via RSS feeds... Found 20 document(s) [1/5] Processing: Governor Bowman's Speech on Banking... ✅ Fetched 45231 bytes ✅ Parsed: Michelle W. Bowman - speech ✅ Normalized: fed-speech-a1b2c3d4e5f6 ✅ Saved (NEW) ... ✅ REFRESH COMPLETE Discovered: 5 New: 5 Total in storage: 5

Step 3: Explore the Data

View latest speeches:

uv run python scripts/test_local.py latest --limit 3

Output:

📰 LATEST SPEECHES ================================================== Found 3 speech(es): 1. Speech on Monetary Policy and the Economic Outlook 📅 2024-12-15 👤 Jerome H. Powell (Chair) 📄 speech | 2500 words ⭐ high (0.85) 🏷️ Topics: inflation, rates, labor 🔗 https://federalreserve.gov/... 🆔 fed-speech-abc123

Search by keyword:

uv run python scripts/test_local.py search --query "inflation"

Filter by speaker:

uv run python scripts/test_local.py speaker --name "Powell" uv run python scripts/test_local.py speaker --role "Chair"

Get full speech content:

uv run python scripts/test_local.py get --doc-id fed-speech-abc123

View statistics:

uv run python scripts/test_local.py stats

Step 4: Run All Tests

uv run python scripts/test_local.py all

This runs a complete test suite: refresh → stats → latest → search → filter → get.

Available Test Commands

Command

Description

Example

refresh

Fetch new speeches

--limit 5, --include-index

latest

Show latest speeches

--limit 10, --since 2024-01-01

search

Search by keyword

--query "inflation"

speaker

Filter by speaker

--name "Powell", --role "Chair"

type

Filter by doc type

--doc-type testimony

get

Get full speech

--doc-id fed-speech-xxx

stats

Show statistics

(no options)

all

Run all tests

(no options)

Using with AI Assistants

Platform Compatibility Overview

Platform

MCP Support

Setup Method

Claude Desktop

✅ Native

Direct MCP integration

Cursor IDE

✅ Native

Direct MCP integration

ChatGPT

⚠️ Via API

HTTP API wrapper + Custom GPT

Google Gemini

⚠️ Via API

HTTP API wrapper + Google AI Studio

Other AI Tools

⚠️ Via API

HTTP API wrapper

Step-by-Step: Claude Desktop

Claude Desktop has native MCP support. This is the easiest setup.

Step 1: Install the Package

cd fed-speech-mcp uv sync

Step 2: Locate Claude Desktop Config

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Step 3: Add MCP Server Configuration

Edit claude_desktop_config.json:

{ "mcpServers": { "fed-speech": { "command": "uv", "args": ["run", "fed-speech-mcp"], "cwd": "/path/to/fed-speech-mcp" } } }

Note: Replace /path/to/fed-speech-mcp with your actual installation path.

Step 4: Restart Claude Desktop

Close and reopen Claude Desktop. You should see the Fed Speech tools available.

Step 5: Start Using

Ask Claude things like:

  • "Refresh the Fed speeches and show me the latest ones"

  • "What did Chair Powell say about inflation recently?"

  • "Show me all testimony from 2024"

Step-by-Step: Cursor IDE

Cursor IDE also supports MCP natively.

Step 1: Install the Package

cd fed-speech-mcp uv sync

Step 2: Open Cursor Settings

  1. Open Cursor IDE

  2. Go to Settings (⌘+, on macOS, Ctrl+, on Windows)

  3. Search for "MCP" or navigate to Features → MCP Servers

Step 3: Add MCP Server

Click "Add MCP Server" and configure:

{ "name": "fed-speech", "command": "uv", "args": ["run", "fed-speech-mcp"], "cwd": "/path/to/fed-speech-mcp" }

Step 4: Enable and Use

Toggle the server on. You can now use Fed Speech tools in Cursor's AI chat.

Step-by-Step: ChatGPT (Custom GPT)

ChatGPT doesn't support MCP natively, but you can use the HTTP API wrapper.

Step 1: Install the Package

cd fed-speech-mcp uv sync

Step 2: Start the HTTP API Server

uv run fed-speech-http

The server runs at http://localhost:8000 by default.

Step 3: Expose to Internet (Required for ChatGPT)

Use a tunneling service like ngrok:

# Install ngrok: https://ngrok.com/download ngrok http 8000

Copy the public URL (e.g., https://abc123.ngrok.io).

Step 4: Create a Custom GPT

  1. Go to ChatGPT

  2. Click your profile → My GPTs → Create a GPT

  3. In the Configure tab:

    • Name: "Fed Speech Analyst"

    • Description: "Analyzes Federal Reserve speeches and testimonies"

    • Instructions:

      You are a Federal Reserve speech analyst. Use the available actions to: 1. Fetch latest speeches with get_latest_speeches 2. Search speeches by speaker, type, or keywords 3. Analyze speech content for market-relevant insights Always refresh speeches first if the user asks about recent content.

  4. Click Create new action and import the OpenAPI schema:

openapi: 3.0.0 info: title: Fed Speech API version: 1.0.0 servers: - url: https://your-ngrok-url.ngrok.io paths: /speeches/latest: get: operationId: getLatestSpeeches summary: Get latest Fed speeches parameters: - name: limit in: query schema: type: integer default: 10 - name: since_date in: query schema: type: string responses: '200': description: List of speeches /speeches/search: get: operationId: searchSpeeches summary: Search speeches by keyword parameters: - name: query in: query required: true schema: type: string - name: limit in: query schema: type: integer default: 10 responses: '200': description: Search results /speeches/{doc_id}: get: operationId: getSpeech summary: Get a specific speech parameters: - name: doc_id in: path required: true schema: type: string responses: '200': description: Speech details /speeches/refresh: post: operationId: refreshSpeeches summary: Fetch new speeches from Fed website responses: '200': description: Refresh result

Step 5: Save and Use

Save your Custom GPT. Now you can ask it questions about Fed speeches!

Step-by-Step: Google Gemini

Google Gemini can use the HTTP API via Google AI Studio or API.

Step 1: Start the HTTP API Server

cd fed-speech-mcp uv sync uv run fed-speech-http

Step 2: Expose to Internet

ngrok http 8000

Step 3: Use with Google AI Studio

  1. Go to Google AI Studio

  2. Create a new prompt or chat

  3. In your system instructions, include:

You have access to a Fed Speech API at https://your-ngrok-url.ngrok.io Available endpoints: - GET /speeches/latest?limit=10 - Get latest speeches - GET /speeches/search?query=inflation - Search speeches - GET /speeches/{doc_id} - Get specific speech - POST /speeches/refresh - Fetch new speeches - GET /speeches/by-speaker?name=Powell - Filter by speaker - GET /speeches/by-type?doc_type=testimony - Filter by type When users ask about Fed speeches, use these endpoints to fetch data. Format responses clearly with speaker names, dates, and key points.

Step 4: Use Function Calling (Advanced)

For programmatic use with Gemini API:

import google.generativeai as genai import requests # Configure Gemini genai.configure(api_key="YOUR_API_KEY") # Define tools for Gemini tools = [ { "function_declarations": [ { "name": "get_latest_speeches", "description": "Get the latest Federal Reserve speeches", "parameters": { "type": "object", "properties": { "limit": {"type": "integer", "description": "Max speeches to return"} } } }, { "name": "search_speeches", "description": "Search Fed speeches by keyword", "parameters": { "type": "object", "properties": { "query": {"type": "string", "description": "Search query"} }, "required": ["query"] } } ] } ] model = genai.GenerativeModel('gemini-pro', tools=tools) # Handle function calls def handle_function_call(fn_name, args): base_url = "http://localhost:8000" if fn_name == "get_latest_speeches": resp = requests.get(f"{base_url}/speeches/latest", params=args) elif fn_name == "search_speeches": resp = requests.get(f"{base_url}/speeches/search", params=args) return resp.json() # Chat with function calling chat = model.start_chat() response = chat.send_message("What are the latest Fed speeches about inflation?") # Process function calls in response for part in response.parts: if hasattr(part, 'function_call'): fn = part.function_call result = handle_function_call(fn.name, dict(fn.args)) # Send result back to model response = chat.send_message(str(result))

HTTP API Reference

When using the HTTP API wrapper, these endpoints are available:

Endpoint

Method

Description

/speeches/latest

GET

Get latest speeches

/speeches/search

GET

Search by keyword

/speeches/{doc_id}

GET

Get specific speech

/speeches/by-speaker

GET

Filter by speaker

/speeches/by-type

GET

Filter by doc type

/speeches/refresh

POST

Fetch new speeches

/speeches/stats

GET

Get statistics

Query Parameters

  • limit - Max results (default: 10, max: 50)

  • since_date - ISO 8601 date filter

  • start_date / end_date - Date range

  • name - Speaker name (partial match)

  • role - "Chair", "Vice Chair", or "Governor"

  • doc_type - "speech", "testimony", or "prepared_remarks"

  • query - Search keywords

Running the MCP Server Directly

For native MCP clients:

# Run the MCP server uv run fed-speech-mcp # Run the HTTP API server uv run fed-speech-http

Available Tools

get_latest_speeches

Get the latest Federal Reserve speeches, sorted by publication date.

Parameters:

  • limit (optional): Maximum number of speeches (default: 10, max: 50)

  • since_date (optional): Only return speeches after this date (ISO 8601)

get_speeches_by_speaker

Filter speeches by speaker name and/or role.

Parameters:

  • name (optional): Speaker name (partial match, e.g., "Powell")

  • role (optional): "Chair", "Vice Chair", or "Governor"

  • start_date (optional): Start date filter

  • end_date (optional): End date filter

get_speeches_by_type

Get speeches by document type.

Parameters:

  • doc_type (required): "speech", "testimony", or "prepared_remarks"

  • start_date (optional): Start date filter

  • end_date (optional): End date filter

get_speech

Get full content and metadata for a specific speech.

Parameters:

  • doc_id (required): The unique document identifier

refresh_speeches

Fetch new speeches from the Federal Reserve website.

Parameters:

  • include_index (optional): Also scan index pages (slower but thorough)

  • years (optional): Years to scan for index pages

search_speeches

Search speeches by keyword.

Parameters:

  • query (required): Search query

  • limit (optional): Maximum results (default: 10)

get_speech_stats

Get statistics about stored speeches.

Output Format

Each speech document contains:

{ "doc_id": "fed-speech-abc123def456", "source": { "publisher": "Board of Governors of the Federal Reserve System", "collection": "speeches", "url": "https://www.federalreserve.gov/...", "retrieved_at": "2024-01-15T10:30:00Z" }, "published_at": "2024-01-15T00:00:00Z", "title": "Speech Title", "speaker": { "name": "Jerome H. Powell", "role": "Chair", "organization": "Board of Governors of the Federal Reserve System" }, "doc_type": "speech", "event": { "name": "Economic Club of New York", "location": "New York, NY" }, "text": { "raw": "...", "clean": "..." }, "features": { "word_count": 2500, "language": "en", "has_qa": false, "topics": { "inflation": true, "labor_market": true, "rates": true, "balance_sheet": false, "growth": true, "financial_stability": false } }, "importance": { "tier": "high", "score": 0.85, "reasons": [ "Speaker is Chair (Jerome H. Powell)", "Discusses rates in context of inflation" ] } }

Importance Scoring

The importance score is calculated using these rules:

Factor

Adjustment

Chair or Vice Chair speaker

Base: High

Governor speaker

Base: Medium

Testimony

+1 tier

Contains Q&A

+1 tier

Discusses rates + (inflation or labor market)

+1 tier

Word count < 300

-1 tier

Topic Detection

Topics are detected by keyword matching:

  • Inflation: inflation, prices, CPI, PCE, price stability

  • Labor Market: employment, unemployment, wages, jobs

  • Rates: interest rate, fed funds, hike, cut, monetary policy

  • Balance Sheet: QE, QT, runoff, asset purchases

  • Growth: GDP, demand, recession, economic activity

  • Financial Stability: banking, liquidity, stress, systemic risk

Environment Variables

Variable

Description

Default

FED_SPEECH_DATA_DIR

Data storage directory

./data

FED_SPEECH_HTTP_TIMEOUT

HTTP request timeout (seconds)

30

FED_SPEECH_MAX_RETRIES

Max retry attempts

3

FED_SPEECH_HTTP_PORT

HTTP API server port

8000

Data Storage

data/ ├── speeches/ # Processed JSON documents │ ├── fed-speech-xxx.json │ └── ... └── raw/ # Raw HTML content (for traceability) ├── 20240115_abc123.html └── ...

Development

Running Unit Tests

# Install with dev dependencies uv sync --dev # Run all tests uv run pytest # Run with verbose output uv run pytest -v # Run specific test file uv run pytest tests/test_models.py # Run with coverage uv run pytest --cov=fed_speech_mcp

Running Local Integration Tests

# Test all MCP functionality end-to-end uv run python scripts/test_local.py all # Test specific functionality uv run python scripts/test_local.py refresh --limit 3 uv run python scripts/test_local.py search --query "rates"

Project Structure

fed-speech-mcp/ ├── src/fed_speech_mcp/ │ ├── __init__.py │ ├── server.py # MCP server entry point │ ├── http_server.py # HTTP API wrapper │ ├── config.py # Configuration │ ├── models/ # Pydantic data models │ ├── ingestion/ # RSS/index discovery & fetching │ ├── parsing/ # HTML parsing & normalization │ ├── features/ # Feature extraction & scoring │ └── storage/ # JSON storage layer ├── data/ # Data directory ├── pyproject.toml └── README.md

License

MIT

Acknowledgments

Data sourced from the Federal Reserve Board.

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

Resources

Tools

New MCP Servers

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/zsun4work/fed-speech-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server