FRED Economic Data

get_series_observations

Get time series observations (data points) for a FRED series.

Returns the actual data values for an economic indicator over time.
Use search_series first to find the series_id, or use well-known IDs
like UNRATE, GDP, CPIAUCSL, FEDFUNDS, MORTGAGE30US.

For state unemployment, use state abbreviation + 'UR' (e.g. WAUR for
Washington, CAUR for California).

Results are sorted most-recent-first. For long series (e.g. daily data
since 1954), use start_date/end_date to narrow the window or increase
the limit up to 10000.

Args:
    series_id: FRED series identifier (e.g. 'UNRATE', 'GDP', 'CPIAUCSL').
    start_date: Optional start date in YYYY-MM-DD format (e.g. '2020-01-01').
    end_date: Optional end date in YYYY-MM-DD format (e.g. '2024-12-31').
    limit: Maximum observations to return (default 1000, max 10000).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`end_date`	No
`series_id`	Yes
`start_date`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses that results are sorted most-recent-first and offers performance advice for long series. It does not mention authentication or rate limits, but the description is sufficiently transparent about the tool's behavior.

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 well-structured with a brief purpose statement, usage examples, and a clear param list. It is efficient but could be slightly more concise. Overall, it earns its sentences.

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?

Given the output schema exists (explaining return values not needed), the description covers essential context: example IDs, state URL pattern, sorting order, and handling long series. It is complete for an agent to use the tool 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?

The schema has 0% description coverage, so the description adds significant value by explaining each parameter: series_id with examples, date format for start_date/end_date, and limit with default and max values. This compensates fully for the lack of schema descriptions.

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 it retrieves time series observations/data points for a FRED series. It distinguishes from sibling tools like search_series and get_series_info by specifying the tool's role in fetching actual data values.

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

Usage Guidelines4/5

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

The description provides explicit guidance on when to use this tool: after searching for a series ID or using well-known IDs. It also gives examples for state unemployment and suggests using date filters or limit for long series. However, it does not explicitly state when not to use it or mention alternatives.

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

All FRED Economic Data tools Inspect

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: get_category_series browses series by category, get_release_dates tracks publication dates, get_series_info provides metadata, get_series_observations retrieves data points, and search_series finds series by keyword. The descriptions reinforce these unique roles, eliminating any ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with 'get_' or 'search_' prefixes and descriptive nouns (e.g., get_category_series, search_series). The naming is uniform and predictable, making it easy for agents to understand the action and target resource.

Tool Count5/5

With 5 tools, this server is well-scoped for accessing FRED economic data. It covers essential operations like searching, retrieving metadata, and fetching observations, without being overly sparse or bloated. Each tool serves a clear purpose in the data exploration workflow.

Completeness4/5

The tool set provides strong coverage for browsing and retrieving economic data, including search, metadata, and observations. However, it lacks direct update or manipulation tools (e.g., for user favorites or annotations), which are minor gaps but not critical for the core domain of data access.

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