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FRED Economic Series

fred_series
Read-onlyIdempotent

Fetch any FRED time-series by curated slug (gdp, cpi, unemployment, fed_funds, treasury_10y, vix, wti, mortgage_30y, ...) or raw FRED ID (CPIAUCSL, UNRATE, DGS10). ~800k series available — covers BLS, Census, Fed, Treasury, OECD, IMF, BIS. Free upstream, server-side key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoISO date YYYY-MM-DD.
sortNodesc
limitNo
startNoISO date YYYY-MM-DD.
series_or_slugYesCurated slug (e.g. 'gdp') or raw FRED series ID (e.g. 'CPIAUCSL').

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful operational context beyond annotations: the upstream is free, a server-side key is required, and the tool covers roughly 800k series across major data sources. It does not describe response format or pagination, but this is less critical given the rich annotations.

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 compact, front-loaded with the core action, and every sentence earns its place. The examples are inline and the coverage/key-requirement details are relevant operational context. There is no fluff or repetition of schema information.

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

Completeness4/5

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

For a tool with no output schema, the description could say more about the response structure, units, or default time range. However, the name and 'time-series' wording make the return type clear enough, and the description covers input alternatives, scale of data, source coverage, and the server-side key requirement. It is adequate for correct invocation, with only minor gaps around output details.

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 description significantly enriches the main parameter series_or_slug by listing many example slugs and clarifying the raw-ID alternative. However, schema description coverage is only 60%; sort and limit have no description in the schema and the tool description does not explain their semantics either. The main parameter is well handled, but 40% of the schema is left without added meaning.

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 opens with a specific action and resource — 'Fetch any FRED time-series' — and clearly scopes the tool to retrieval via curated slug or raw FRED ID. Concrete examples ('gdp', 'CPIAUCSL') make the operation unambiguous and distinguish it from sibling tools like fred_search and fred_popular, which are about discovery rather than direct series fetching.

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 the right usage — call this tool when you already have a slug or raw FRED series ID — but it does not explicitly say when to prefer fred_search or fred_popular instead. There is clear context on input forms, but no direct when-to-use/when-not-to-use routing.

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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TDQS

B3.2/5.0
Disambiguation2/5

Multiple tools have genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.

Naming Consistency4/5

All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).

Tool Count1/5

129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.

Completeness4/5

Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.

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