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FRED Catalog Search

fred_search
Read-onlyIdempotent

Free-text search of FRED's ~800k series catalog. Ordered by popularity. Use when you don't know the series ID — e.g. q='initial jobless claims' surfaces ICSA + state-level variants.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYes
limitNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is clear. The description adds behavioral context by stating 'Ordered by popularity,' which informs the agent of the result ordering. It also implies a search behavior (free-text) that is not explicit in annotations. No contradiction exists.

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?

Two sentences with no filler. The purpose and usage are front-loaded, and the example is compact and illustrative. Every word earns its place.

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?

For a simple two-parameter search tool with annotations covering safety, the description is complete. It clarifies the query semantics, ordering, and typical use case. No output schema is present, but the agent can infer the return is a list of series without extra detail. Nothing critical is missing.

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?

Schema description coverage is 0%, so the description should compensate. It explains q via example ('q='initial jobless claims''), illustrating its use, but does not describe the limit parameter at all. Limit is self-explanatory (a max count), but since coverage is zero, the description leaves a gap. It partially compensates for q but not limit, warranting a mid-range score.

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 the tool performs a free-text search of FRED's ~800k series catalog, ordered by popularity. It includes a concrete example (e.g., 'initial jobless claims') that illustrates the query type. It distinguishes itself from siblings like fred_series (which likely operates on a known ID) and fred_popular (which may list popular series without search), making the purpose unmistakable.

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 explicitly says 'Use when you don't know the series ID,' giving a clear trigger condition. It does not name the alternative tool (e.g., fred_series) but implies that when the ID is known, a different tool should be used. The guidance is actionable but stops short of explicitly contrasting with siblings.

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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