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LiveDataLink

by io.github.blackboxfoundry

spending_search_awards

Read-onlyIdempotent

Search federal awards (contracts, grants, loans) from USAspending.gov by recipient company, keyword, and/or awarding agency, with optional fiscal year and minimum amount. Returns each award's id, recipient, amount, awarding agency, type, start date, and description, sorted by amount.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows (default 10, max 50).
agencyNoAwarding agency name, e.g. 'Department of Defense'.
keywordNoFree-text keyword across the award.
categoryNoAward category: 'contracts' (default), 'grants', 'loans', or 'other'.
recipientNoRecipient company/org name, e.g. 'Lockheed Martin'.
min_amountNoMinimum award amount in USD.
fiscal_yearNoFederal fiscal year, e.g. 2024.

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, openWorld, idempotent, non-destructive). The description adds valuable behavioral context: the data source (USAspending.gov), the sorted-by-amount output ordering, and the exact fields returned. This goes beyond the structured metadata without contradicting it.

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 redundancy. The purpose and primary filters are front-loaded, followed by a precise description of the output. Every phrase 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 search tool with 7 parameters and no output schema, the description covers search criteria, output fields, sorting, and source. It lacks pagination or rate-limit details, but those are typically not essential for a read-only search. The safety profile is already covered by annotations.

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 parameters are already well-documented. The description mentions recipient, keyword, agency, fiscal year, and minimum amount, but doesn't explicitly reference 'limit' or 'category' (though category is implied by the award types listed). It adds marginal value over the schema.

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?

Clearly states a specific verb (Search), resource (federal awards), and scope (contracts, grants, loans) with filters and output fields. However, it does not explicitly differentiate from closely related siblings like spending_award_details or spending_recipient_summary, so it stops short of a 5.

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?

Provides context on search criteria (recipient, keyword, agency, fiscal year, minimum amount) but offers no guidance on when to use this tool versus alternatives like spending_award_details for award-specific details or spending_recipient_summary for aggregated recipient data. No exclusions or prerequisites are mentioned.

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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