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floriancaro

fred-mcp-server

by floriancaro

fred_series_observations

Retrieve data values for a FRED series with options for date ranges, transformations, and aggregation frequencies.

Instructions

Get observations (data values) for a FRED series.

Args: series_id: FRED series ID (e.g., "GNPCA", "UNRATE"). realtime_start: Start of real-time period (YYYY-MM-DD). realtime_end: End of real-time period (YYYY-MM-DD). limit: Max number of results. offset: Pagination offset. sort_order: "asc" or "desc" by observation date. observation_start: Start of observation range (YYYY-MM-DD). observation_end: End of observation range (YYYY-MM-DD). units: Data transformation — "lin" (levels), "chg" (change), "ch1" (change from year ago), "pch" (percent change), "pc1" (percent change from year ago), "pca" (compounded annual rate of change), "cch" (continuously compounded rate of change), "cca" (continuously compounded annual rate of change), "log" (natural log). frequency: Aggregation frequency — "d" (daily), "w" (weekly), "bw" (biweekly), "m" (monthly), "q" (quarterly), "sa" (semiannual), "a" (annual). Weekly variants: "wef" (Fri), "weth" (Thu), "wew" (Wed), "wetu" (Tue), "wem" (Mon), "wesu" (Sun), "wesa" (Sat). aggregation_method: How to aggregate — "avg" (average), "sum", "eop" (end of period). output_type: 1=observations by real-time period, 2=all by vintage date, 3=new/revised by vintage date, 4=initial release only. vintage_dates: Comma-separated vintage dates (YYYY-MM-DD).

Returns: dict with key 'observations' containing data points with 'date' and 'value' fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
series_idYes
realtime_startNo
realtime_endNo
limitNo
offsetNo
sort_orderNo
observation_startNo
observation_endNo
unitsNo
frequencyNo
aggregation_methodNo
output_typeNo
vintage_datesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

Annotations are absent, so the description must disclose behavioral traits. It fails to mention that the tool is read-only, has rate limits, or any potential errors (e.g., invalid series_id). Although parameters are well-documented, behavioral context is missing.

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 clear one-sentence purpose followed by a parameter list. Each sentence adds value, though the parameter section is lengthy due to many options. No fluff, and the most important information is front-loaded.

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 tool's complexity (13 parameters, no annotations, low schema coverage), the description covers all essential aspects: purpose, each parameter's meaning, valid values, and the return format. It includes output schema info, making it sufficiently complete for an AI agent.

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?

Schema description coverage is 0%, so the description carries the full burden. The Args section provides thorough explanations for all 13 parameters, including enum interpretations (e.g., 'lin' for levels, 'chg' for change) and date formats. This adds significant meaning beyond the raw schema.

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 explicitly states 'Get observations (data values) for a FRED series.' This is a specific verb+resource combination that clearly distinguishes it from sibling tools like fred_series (metadata) or fred_category (categories).

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

Usage Guidelines3/5

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

The description implies usage by describing what the tool does (retrieve data values), but it does not explicitly state when to use this tool over alternatives, nor does it provide exclusion criteria. Given the many sibling tools, explicit guidance would improve usability.

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

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