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by io.github.blackboxfoundry

fred_releases

Read-onlyIdempotent

Browse FRED economic releases (e.g. Employment Situation, CPI, GDP). With upcoming_dates=true, returns the upcoming release calendar instead. Useful for knowing when fresh data is expected.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return (default 50 for observations, 25 for catalog queries).
release_idNoOptional. If provided, return only that release's metadata.
upcoming_datesNoIf true, return upcoming release date schedule instead of release metadata. Default false.

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds the conditional behavior tied to upcoming_dates and the purpose of the release calendar, but it does not disclose pagination, rate limits, or what the default listing response contains beyond being releases. No contradiction with 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 three short sentences with no filler. It front-loads the core purpose, gives concrete examples, then explains the alternate mode and a practical use case. Every sentence earns its place.

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 simple read-only browse tool with three optional parameters, full schema descriptions, and robust annotations, the description is largely sufficient to invoke the tool correctly. It could mention related FRED tools or default output behavior, but those omissions do not block correct usage.

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 100%, so the schema already documents all three parameters. The description adds light context by explaining the upcoming_dates mode and naming real release examples, but it does not materially extend parameter semantics beyond the schema. Baseline 3 is appropriate under high schema coverage.

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 states a clear verb ('browse') and resource ('FRED economic releases') with concrete examples (Employment Situation, CPI, GDP). It also identifies the two modes of behavior (release metadata vs. upcoming release calendar), which makes it easy to distinguish from related FRED tools like fred_observations or fred_series_info.

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 gives context for when the tool is useful ('knowing when fresh data is expected') and explains the upcoming_dates toggle, but it does not explicitly say when to prefer this tool over sibling FRED tools or when not to use it. Usage guidance is implied rather than explicit.

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

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

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