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LiveDataLink

by io.github.blackboxfoundry

grants_search

Read-onlyIdempotent

Search federal funding opportunities on Grants.gov (keyless). Filter by keyword, opportunity status (forecasted/posted/closed/archived), agency, funding category and eligibility. Returns opportunity number, title, agency, status, open/close dates and CFDA numbers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsNoMax results (default 25, max 100).
keywordNoFree-text keyword (e.g. 'clean energy', 'rural health').
agenciesNoAgency code filter (e.g. 'NSF', 'HHS'). Pipe-separate multiples.
oppStatusesNoPipe-separated statuses. Default 'forecasted|posted'. Options: forecasted, posted, closed, archived.
eligibilitiesNoApplicant-eligibility code filter (e.g. '25' state governments, '99' unrestricted). Pipe-separate multiples.
fundingCategoriesNoFunding category code filter (e.g. 'ENV', 'ED'). Pipe-separate multiples.
fundingInstrumentsNoFunding-instrument code filter (e.g. 'G' grant, 'CA' cooperative agreement). Pipe-separate multiples.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds value by noting 'keyless' (no auth needed) and specifying exactly what the search returns, giving the agent useful behavioral context without contradicting 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?

Three short, front-loaded sentences each add value: what is searched, how to filter, and what is returned. There is no filler or redundancy, and the most important action/resource pair appears first.

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?

Despite lacking an output schema, the description explicitly lists the return fields and all relevant filter categories. Combined with fully documented parameters and clear annotations, the agent has enough information to select and correctly invoke the tool.

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 all seven parameters are already documented. The description enumerates filter dimensions but does not add semantic detail beyond the schema, such as code formats or defaults, making the baseline score of 3 appropriate.

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 specific verb and resource ('Search federal funding opportunities on Grants.gov'), then lists concrete filters and return fields. This clearly distinguishes it from the sibling grants_get_opportunity, which is evidently a detail-lookup tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no explicit guidance about when to use this tool instead of siblings like grants_get_opportunity or org_funding_profile. It implies a search use case but does not state conditions, prerequisites, or exclusions.

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