# FRED (Federal Reserve Economic Data)

> Use this tool when you need to retrieve economic data series observations with customizable parameters for analysis, research, or visualization. It solves problems related to accessing official U.S. economic indicators and historical data, providing options for date ranges, frequency aggregation, and data transformations. The tool takes in parameters such as date ranges and output formats, and returns economic data series observations in a customizable format.

Canonical page: https://skillsregistry.net/skills/jaldekoa-fredapi  
JSON: https://api.skillsregistry.net/v1/skills/jaldekoa-fredapi

## Description

MCP-FREDAPI provides a bridge to the Federal Reserve Economic Data (FRED) API, enabling AI assistants to retrieve economic data series observations with customizable parameters. Built with Python using FastMCP, this implementation by Jon Aldekoa allows querying time series data with options for date ranges, frequency aggregation, data transformations, and output formatting. The server handles API key management through environment variables and offers robust error handling for failed requests. It's particularly useful for economic analysis, financial research, and data visualization applications that require access to official U.S. economic indicators and historical data.

## Trust

- **Trust score (0–1):** 0.99
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/jaldekoa-fredapi)
- **Repository:** <https://github.com/jaldekoa/mcp-fredapi>

## 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": "jaldekoa-fredapi"
    }
  }
}
```

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

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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