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floriancaro

fred-mcp-server

by floriancaro

fred_series_search

Search FRED economic data series by text query. Return matching series with options to filter by type, dates, tags, and sort results.

Instructions

Search for FRED series by text.

Args: search_text: Search query (e.g., "GDP", "unemployment rate"). search_type: "full_text" for full-text search, "series_id" to search series IDs. 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. order_by: Sort results by this field. sort_order: "asc" or "desc". filter_variable: Filter by "frequency", "units", or "seasonal_adjustment". filter_value: Value to filter by (depends on filter_variable). tag_names: Semicolon-delimited tag names to filter by (e.g., "usa;gdp"). exclude_tag_names: Semicolon-delimited tag names to exclude.

Returns: dict with key 'seriess' containing matching series.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
search_textYes
search_typeNo
realtime_startNo
realtime_endNo
limitNo
offsetNo
order_byNo
sort_orderNo
filter_variableNo
filter_valueNo
tag_namesNo
exclude_tag_namesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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. It only lists parameters and mentions the return dict, but does not disclose behavioral traits like rate limits, pagination behavior, or that it is a read operation. The real-time period parameters are mentioned but their significance is not explained beyond format.

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 efficiently structured with a brief intro followed by a bullet-point parameter list. It front-loads the purpose and avoids unnecessary words, though it could be slightly more concise by integrating parameter descriptions into a single sentence.

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

Completeness3/5

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

Given the complexity (12 parameters, no annotations) and presence of an output schema, the description explains all parameters and the return structure. However, it lacks usage guidelines and behavioral details like timeouts or error handling, leaving some gaps for an AI agent.

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

Parameters4/5

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

Schema description coverage is 0%, and the description compensates by explaining all 12 parameters with examples (e.g., 'search_text: Search query (e.g., "GDP", "unemployment rate").') and value constraints (e.g., 'search_type: "full_text" or "series_id"'). This adds significant meaning beyond the bare 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 clearly states 'Search for FRED series by text', specifying the verb 'search' and the resource 'FRED series'. It distinguishes from sibling tools like fred_series (which retrieves a specific series by ID) and fred_series_search_tags (which searches tags).

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 for text-based searches but does not explicitly state when to use this tool versus alternatives. No exclusions or when-not-to-use guidance is provided, such as noting that fred_series is better for known series IDs.

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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