FRED API MCP Server

get_fred_series_observations

Retrieve economic time series observations for a specified FRED series ID. Apply date ranges, frequency, units, and transformations to get the exact data points needed.

Instructions

Get series observations from the Fred API.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of observations to return. Defaults to 10.
`units`	No	Data value transformation. Options: 'lin', 'chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log'. Defaults to 'lin'.	lin
`offset`	No	Number of observations to offset from first. Defaults to 0.
`frequency`	No	Frequency of observations. Options: 'd', 'w', 'bw', 'm', 'q', 'sa', 'a', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem'. Defaults to no value for no frequency aggregation.
`series_id`	Yes	The id for a series.
`sort_order`	No	Sort order of observations. Options: 'asc' or 'desc'. Defaults to 'asc'.	asc
`output_type`	No	Output type of observations. Options: 1, 2, 3, 4. Defaults to 1.
`realtime_end`	No	The end of the real-time period. Format: YYYY-MM-DD. Defaults to today's date.
`vintage_dates`	No	Comma-separated list of vintage dates.
`realtime_start`	No	The start of the real-time period. Format: YYYY-MM-DD. Defaults to today's date.
`observation_end`	No	End date of observations. Format: YYYY-MM-DD.
`observation_start`	No	Start date of observations. Format: YYYY-MM-DD.
`aggregation_method`	No	Aggregation method for frequency. Options: 'avg', 'sum', 'eop'. Defaults to 'avg'.	avg

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 30, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Get', implying a read operation, but does not describe response format, pagination behavior, real-time period semantics, potential errors, or any side effects. The description is too thin to inform the agent about how the tool behaves beyond the obvious.

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

Conciseness4/5

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

The description is a single concise sentence with zero wasted words and is front-loaded with the core verb and resource. It is appropriately brief, though given the tool's complexity, a little more structure could have made it more useful without losing conciseness.

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

Completeness2/5

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

This is a 13-parameter tool with no output schema and no annotations. The description does not explain what an 'observation' is, what the return payload looks like, or how concepts like vintage dates and real-time periods work. The schema covers parameters, but the overall tool context is under-specified for an agent to use it confidently.

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

Parameters3/5

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

The schema has 100% description coverage across all 13 parameters, so the baseline is 3. The description itself contributes nothing to parameter understanding, but the schema adequately documents defaults, formats, and enums, so no deduction beyond baseline is warranted.

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

Purpose4/5

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

The description states a clear verb ('Get') and resource ('series observations') with the API ('Fred'), which adequately identifies the operation. However, it does not elaborate on what 'observations' actually are or provide any differentiating detail, and it closely mirrors the tool name.

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

Usage Guidelines2/5

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

No guidance is given about when to use this tool, what conditions favor it, or what alternatives exist. Since there are no sibling tools, there is no differentiation burden, but there is also no scenario or prerequisite context to help an agent decide to invoke it.

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

Deploy Server

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get_fred_series_observationsC