Chisle
The coding-agent plugin that bills 52% of a bare model across 20 live tasks. Terse persona + tool-output compressor + context diet.
翻訳待ち
MCP server + TypeScript SDK for 40+ U.S. government data APIs — 250+ tools. Treasury, FRED, Congress, FDA, CDC, FEC, lobbying, and many more. Works with VS Code Copilot, Claude Desktop, Cursor.
翻訳待ち
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Read the full README ↗MCP Server + TypeScript SDK for 40+ U.S. Government APIs
300+ tools covering economic, fiscal, health, education, energy, environment, lobbying, housing, patents, safety, banking, consumer protection, workplace safety, transportation, seismic, clinical trials, pharma payments, research funding, procurement, and legislative data.
20+ APIs require no key · The rest use free keys that take under a minute to get
Getting Started · API Reference · Documentation
--modules fred,treasury,congress or --domains economy,health--tool-mode discovery lists 4 tools instead of 350; the model searches for the one it needsmeta.sources lists the upstream API requests behind each answerRequires Node.js 22 or later.
npx us-gov-open-data-mcp
Add to .vscode/mcp.json for VS Code / Copilot:
{
"servers": {
"us-gov-open-data": {
"command": "npx",
"args": ["-y", "us-gov-open-data-mcp"],
"env": {
"FRED_API_KEY": "your_key",
"DATA_GOV_API_KEY": "your_key"
}
}
}
}
Add to claude_desktop_config.json for Claude Desktop:
{
"mcpServers": {
"us-gov-open-data": {
"command": "npx",
"args": ["-y", "us-gov-open-data-mcp"],
"env": {
"FRED_API_KEY": "your_key",
"DATA_GOV_API_KEY": "your_key"
}
}
}
}
Once connected, ask your AI assistant natural language questions:
Economic: "What's the current state of the U.S. economy? Show me GDP, unemployment, inflation, and interest rates."
Health: "Show me the adverse event profile for Ozempic including clinical trials, FDA reports, and pharma payments to doctors."
Legislative: "What happened with the Inflation Reduction Act? Who sponsored it, how did the vote break down by party?"
Follow the money: "Which banking PACs gave money to members of the Senate Banking Committee, and how did those members vote on banking deregulation?"
Cross-reference: "How has federal spending on healthcare changed over the last 5 years, and what health outcomes has it produced?"
npm install us-gov-open-data-mcp
import { getObservations } from "us-gov-open-data-mcp/sdk/fred";
import { searchBills } from "us-gov-open-data-mcp/sdk/congress";
const gdp = await getObservations("GDP", { sort: "desc", limit: 5 });
No MCP server required. All functions include caching, retry, and rate limiting.
Full documentation at lzinga.github.io/us-gov-open-data-mcp
| Getting Started | MCP setup, SDK install, client configs |
| API Keys | Which APIs need keys, where to get them |
| Data Sources | All 40+ APIs grouped by category |
| API Reference | Auto-generated from TypeScript — every function and type |
| Examples | SDK code, MCP prompts, analysis showcases |
| Architecture | How the system works |
| Adding Modules | Add a new API — just create a folder |
| Category | APIs |
|---|---|
| Economic | Treasury, FRED, BLS, BEA, EIA |
| Legislative | Congress.gov, Federal Register, GovInfo, Regulations.gov |
| Financial | FEC, Senate Lobbying, SEC, FDIC, CFPB |
| Spending & Procurement | USAspending, Open Payments, GSA CALC+ |
| Health & Safety | CDC, FDA, CMS, ClinicalTrials.gov, NIH, NHTSA, DOL |
| Environment & Weather | EPA, EPA AQS, NOAA, NWS, NREL, USGS |
| Justice | FBI Crime Data, DOJ News |
| Education & Research | NAEP, College Scorecard, USPTO |
| Demographics | Census, HUD, FEMA |
| Other | BTS, USDA NASS, USDA FoodData, World Bank |
This project integrates a significant number of government APIs, many of which have large, complex, or inconsistently documented schemas. AI is used as a tool throughout this project to help parse API documentation, generate type definitions, and scaffold tool implementations — making it possible to cover this much surface area and get people access to government data faster than would otherwise be feasible. While every effort has been made to ensure accuracy, some endpoints may return unexpected results, have incomplete parameter coverage, or behave differently than documented.
This is a community-driven effort — if you find something that's broken or could be improved, please open an issue or submit a PR. Contributions that fix edge cases, improve schema accuracy, or expand coverage are especially welcome. The goal is to make U.S. government data as accessible and reliable as possible, together.
All data is sourced from official U.S. government and international APIs — the server does not generate, modify, or editorialize any data.
MIT