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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, non-destructive behavior. The description adds useful behavioral context by saying the output is chronological and includes introductions, committee referrals, votes, and law status, but it does not disclose response shape, pagination behavior, or other limits beyond what annotations and schema imply.
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 short sentences deliver the core purpose, content examples, and required parameters with no filler. The main action is front-loaded, and every sentence adds useful information.
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 one-bill lookup, the description covers what the tool returns, the scope, and the prerequisites. It does not describe return value shape explicitly, but the action-history examples sufficiently communicate expected output for a tool with no output schema.
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 every parameter already has a meaningful description, including expanded enum labels. The description only repeats the required parameter names ('Congress number, bill type, and bill number') without adding new semantic detail, 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 ('Get') and a specific resource ('chronological legislative action history') scoped to 'one bill'. It clearly distinguishes this from siblings like congress_bill_details, congress_bill_cosponsors, and congress_house_votes by naming a distinct bill-action-history resource.
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 it: when a caller has a specific bill (Congress number, bill type, bill number) and needs its action history. It does not explicitly name alternatives or exclusions, but the 'for one bill' scope gives clear context against search-style siblings.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.