List bills that have become public or private law in a given Congress. Defaults to the current Congress. Use law_type 'pub' for public laws or 'priv' for private laws.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 20). | |
| congress | No | Congress number (default current, 91). | |
| law_type | No | Law type: 'pub' or 'priv'. |
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 the read-only, idempotent, non-destructive nature of the tool. The description adds the default-to-current-Congress behavior and law_type semantics, but does not disclose ordering, recency bounds, or other behavioral details beyond the schema. This is acceptable given the annotation coverage.
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 tight sentences carry the full purpose, default behavior, and parameter instructions. No filler or redundancy; the main action is front-loaded.
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 three-parameter read-only listing tool with rich annotations and a fully described schema, the description is complete enough for correct invocation. It does not describe the return format, but the absence of an output schema and the simplicity of the tool make this a minor gap.
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%, so the schema already documents all parameters. The description reinforces the meaning of law_type ('pub' for public laws, 'priv' for private laws) and the default congress, but adds little beyond what the schema already states.
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 clearly identifies the action ('List bills that have become public or private law') and the resource scope ('in a given Congress' or current Congress). This distinguishes it from sibling tools like congress_search_bills or congress_bill_details by focusing specifically on enacted laws, not bills in progress.
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 gives clear operational guidance: defaults to the current Congress and instructs how to choose law_type. It does not explicitly name alternatives or state when not to use the tool, but the context is sufficient for an agent to choose it correctly for listing enacted laws.
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.