List recent U.S. House roll-call votes for a Congress, with vote number, question, result, and date. Defaults to the current Congress.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max votes (default 20). | |
| congress | No | Congress number (default current, 91). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish the operation is read-only, idempotent, and non-destructive. The description adds context by stating the default to the current Congress and naming the returned fields, but it does not disclose behaviors such as ordering, pagination, or what 'recent' means. This adds some value but leaves operational details unspecified.
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?
The description is two short sentences with no filler. The verb and resource are front-loaded, and the default behavior is stated succinctly. Every word earns its place.
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 optional-parameter list tool with no output schema, the description covers what the tool returns and its default scope. It does not describe pagination or the exact meaning of 'recent,' but those are minor given the tool's low complexity and the schema's coverage of parameters.
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%: both 'limit' and 'congress' are explained with defaults in the input schema. The description does not add meaning beyond what the schema already provides, so the baseline score 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 ('List') and a specific resource ('recent U.S. House roll-call votes'), and lists the exact fields returned ('vote number, question, result, and date'). This clearly distinguishes it from sibling Congress tools such as congress_bill_details and congress_recent_laws, which concern bills and laws rather than roll-call 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?
The description implies when to use the tool: when you need recent House roll-call votes, optionally for a specific Congress. However, it does not explicitly mention alternatives or state when not to use it, leaving the agent to infer the boundary from the sibling tool names.
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.