Get full detail for one bill, including title, sponsor, latest action, policy area, and a cosponsor party breakdown. Requires the Congress number, bill type, and bill number (e.g. 118, 'hr', 3076).
| Name | Required | Description | Default |
|---|---|---|---|
| congress | Yes | Congress number, e.g. 118. | |
| bill_type | Yes | Bill type code: hr (House Bill), s (Senate Bill), hjres, sjres, hconres, sconres, hres, sres. | |
| bill_number | Yes | Bill number, e.g. 3076. |
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 covered. The description adds useful context by naming the data fields returned, but it does not disclose any additional behavioral traits such as response shape, error behavior, or external dependencies.
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, with the core purpose and output fields front-loaded, followed by the required parameters and a concrete example. Every sentence earns its place and there is no filler or redundant restating of the tool name.
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 three-parameter read-only detail tool, the description covers the purpose, key output fields, and required inputs with an example. There is no output schema, so the description does most of the work; it is not exhaustive about the full return structure, but it gives enough for an agent to call the tool correctly.
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 each parameter is already described with examples (e.g. '118', 'hr', '3076'). The description largely repeats these examples and adds no deeper meaning about parameter relationships, formats, or constraints beyond what the schema provides, so the baseline of 3 is appropriate.
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 uses a specific verb and resource: 'Get full detail for one bill' and lists the returned fields (title, sponsor, latest action, policy area, cosponsor party breakdown). This makes it clear this is a single-record detail endpoint, distinguishably different from sibling search or action-list tools, though it does not explicitly name a sibling.
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?
The description tells the agent what inputs are required ('Requires the Congress number, bill type, and bill number') and gives a concrete example. However, it gives no explicit guidance on when to choose this tool over siblings like congress_bill_actions or congress_bill_cosponsors, so the usage context is implied rather than stated.
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.