edgar-mcp-server

Get economic data series (FRED)

macro_get_series

Read-onlyIdempotent

Retrieve US and global economic time series from FRED: interest rates, inflation, GDP, employment, and more. Align up to five series by date and apply transforms like year-over-year percent change.

Instructions

Get US and global economic time series from FRED (Federal Reserve Bank of St. Louis, 800,000+ series): interest rates, inflation, GDP, jobs, money supply, credit spreads, FX, commodities, recession indicators. Pass 1-5 series IDs to line them up by date. Common IDs: DGS10 / DGS2 (Treasury yields), T10Y2Y (yield curve), FEDFUNDS, CPIAUCSL / CPILFESL (CPI / core), PCEPILFE (core PCE), UNRATE, PAYEMS, GDPC1 (real GDP), A191RL1Q225SBEA (GDP growth), M2SL, BAMLH0A0HYM2 (high-yield spread), VIXCLS, DCOILWTICO (oil), DEXUSEU / DEXMXUS (FX), USREC. Use transform 'pct_change_yoy' for inflation rates from price indexes, and frequency to aggregate daily data (e.g. monthly averages). Find other IDs with macro_search_series.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`end_date`	No	Last observation date. Default: latest (YYYY-MM-DD).
`frequency`	No	Aggregate to a lower frequency: 'weekly', 'monthly', 'quarterly', 'annual'.
`transform`	No	'level' (default), 'change', 'change_yoy', 'pct_change', 'pct_change_yoy', 'pct_change_annualized' or 'log'.	level
`max_points`	No	Rows in the table (default 30, evenly sampled; the latest value is always included).
`series_ids`	Yes	FRED series IDs, e.g. ['DGS10','DGS2'] or ['CPIAUCSL'].
`start_date`	No	First observation date. Default: 10 years ago (YYYY-MM-DD).
`aggregation`	No	How to aggregate when `frequency` is set: average (default), sum or end of period.	avg
`response_format`	No	Output format: 'markdown' (readable, default) or 'json' (structured).	markdown

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0Sep 24, 2026

TDQS

A5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context: series are aligned by date, max_points rows are evenly sampled with the latest value always included, and defaults for dates are explained (10 years back, latest end). This goes beyond the safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence adds value: it covers purpose, usage, examples, and alternative routing in a structured way. It is front-loaded with the primary action and then expands with necessary details, with no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 8 parameters, 4 enums, and no output schema, this description is remarkably complete. It covers what to pass, how to transform, how to aggregate, defaults, and references the sibling for discovery. An agent would have no trouble calling it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While the schema covers 100% of parameters, the description adds practical meaning: common series IDs (DGS10, CPIAUCSL, etc.), recommended transform for inflation, and how frequency/aggregation work together. This significantly enriches the bare schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves economic time series from FRED, specifies the domain (US/global), and lists common series IDs. It distinguishes itself from the sibling macro_search_series (which is for finding IDs) and other market tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells users to pass 1-5 series IDs, provides guidance on transforms (e.g., 'pct_change_yoy' for inflation) and frequency aggregation, and directs users to macro_search_series for finding other IDs. This clearly delineates when to use this tool versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Get economic data series (FRED)

Instructions

Input Schema

Schema Changelog

TDQS

Other Tools