# FRED (Federal Reserve Economic Data)

> Use this tool when you need to access and analyze economic data series from the Federal Reserve, solving problems in financial research, macroeconomic trend identification, and real-time data analysis. It provides a secure proxy interface for searching, retrieving, and comparing economic indicators, with features like rate limiting and caching. Through its Python-based implementation, FRED offers tools for calculating statistics and detecting trends in time series data, making it valuable for applications requiring in-depth economic analysis.

Canonical page: https://skillsregistry.net/skills/fred  
JSON: https://api.skillsregistry.net/v1/skills/fred

## Description

FRED MCP Server provides AI assistants with access to Federal Reserve Economic Data through a secure proxy interface. Built with Python using the mcp-server library, it offers tools for searching, retrieving, and analyzing economic data series with features like rate limiting and caching. The implementation includes specialized tools for comparing multiple indicators, calculating statistics, and detecting trends in time series data. It also provides prompt templates to guide users in effectively utilizing FRED data for economic analysis. Particularly valuable for applications requiring real-time economic data analysis, financial research, and macroeconomic trend identification.

## Trust

- **Trust score (0–1):** 0.92
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/fred)
- **Repository:** <https://github.com/milind-kulshrestha/fred_mcp_server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "fred"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/fred` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/fred/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt