Get the chronological legislative action history for one bill (introductions, committee referrals, votes, becoming law). Requires Congress number, bill type, and bill number.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max actions (default 50). | |
| congress | Yes | Congress number. | |
| bill_type | Yes | Bill type code: hr (House Bill), s (Senate Bill), hjres, sjres, hconres, sconres, hres, sres. | |
| bill_number | Yes | Bill number. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only, idempotent, and non-destructive, so no safety contradiction exists. The description adds meaningful behavioral context by specifying the chronological nature and contents of the returned history. It doesn't detail pagination or limit behavior beyond the schema, but the annotations lower the bar for additional disclosure.
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?
One compact sentence front-loads the operation and scope, then gives concrete examples of the content. There is no filler or redundancy.
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 four-parameter read-only lookup with full schema coverage and no output schema, the description tells an agent what kind of data it will receive and what inputs are required. It could spell out the exact response shape or pagination, but the summary plus examples is sufficient for correct invocation.
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?
All four parameters are described in the schema (100% coverage), so the schema carries the semantic burden. The description only restates the three required identifiers and adds no format, edge-case, or default-behavior nuance beyond the schema.
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?
States a specific operation ('Get ... action history') and scopes it to one bill with the required identifiers. The parenthetical enumerates the action categories, which separates it from sibling tools like congress_bill_details or congress_house_votes.
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?
Clearly signals that this is the tool to call when a complete chronological action timeline for a specific bill is needed, and that bill identifiers are prerequisites. It does not explicitly list when to prefer a sibling such as congress_bill_cosponsors or congress_bill_details, so it stops short of full exclusion guidance.
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.