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by io.github.blackboxfoundry

fred_observations

Read-onlyIdempotent

Get time-series observations for a FRED series ID. Workhorse query for any economic indicator. Optional date range, units transformation (lin, chg, pch, log, etc.), and frequency aggregation (m, q, a).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoInclusive upper-bound ISO date (YYYY-MM-DD).
limitNoMaximum rows to return (default 50 for observations, 25 for catalog queries).
startNoInclusive lower-bound ISO date (YYYY-MM-DD).
unitsNoUnits transformation: 'lin' (default), 'chg' (change), 'ch1' (change YoY), 'pch' (% change), 'pc1' (% change YoY), 'log', etc.
frequencyNoAggregate to a different frequency: 'd', 'w', 'bw', 'm', 'q', 'sa', 'a'.
series_idYesFRED series ID (e.g. 'GDP', 'UNRATE'). See https://fred.stlouisfed.org/ for the catalog.
aggregation_methodNoAggregation method when changing frequency: 'avg', 'sum', or 'eop' (end of period).

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description adds no behavioral detail beyond optional parameters; it does not mention pagination behavior, default limits, output format, or how invalid series IDs or date ranges are handled. It does not contradict the 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 two sentences with no wasted words. It front-loads the core action first, then summarizes the optional transformations in a compact phrase that matches the schema's parameter set.

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?

The description covers what the tool does and the main parameter classes, while the schema handles full parameter semantics. However, with no output schema present, the description could more explicitly describe return shape, pagination, or default limits to make an agent fully confident in formatting results.

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 seven parameters in detail. The description paraphrases some options (date range, units, frequency aggregation) but adds no meaning beyond the schema's fuller parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get time-series observations for a FRED series ID' — a specific verb, resource, and scope. It adds 'Workhorse query for any economic indicator,' which conveys broad applicability and sets it apart from more specialized siblings, though it does not explicitly name any sibling alternatives.

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?

'Workhorse query for any economic indicator' provides useful context for when to use this tool. However, it does not explicitly state when not to use it or point to alternatives like fred_search for finding series IDs or fred_series_info for metadata, leaving some routing decisions to inference.

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.

Resources