Summarize a company's federal awards: total dollars and top awards for a recipient name in a category (contracts by default). Useful for due diligence and to see who the government pays.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Top awards to list (default 5, max 25). | |
| category | No | Award category: 'contracts' (default), 'grants', 'loans', or 'other'. | |
| recipient | Yes | Recipient company/org name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey read-only, idempotent, open-world, non-destructive behavior, so the description's job is lighter. It adds that the result is an aggregate summary with total dollars and top awards, but discloses no additional edge cases, limitations, or response-shape details. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with verb, resource, and output content. The second sentence adds practical context without padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only summary tool with fully documented parameters, the description covers what it returns (total dollars, top awards) and the core input (recipient, category). It lacks an explicit note that the returned top-awards list is shaped by the limit parameter, but the schema covers that, so the overall picture is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description largely restates recipient and category semantics without adding new parameter-level detail. It does reinforce that category defaults to contracts, but the schema already says this.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Summarize'), a resource ('a company's federal awards'), and concrete outputs ('total dollars and top awards'). It also identifies the recipient-name scope and default category, so an agent can distinguish it from the sibling detail/search spending tools by its aggregate nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives clear intended use cases ('due diligence' and seeing who the government pays) and the default category behavior. It does not explicitly contrast with spending_award_details or spending_search_awards, but the summary-vs-detail/search distinction is strongly implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.