List the cosponsors of one bill with their party and state. Useful for mapping coalitions behind legislation. Requires Congress number, bill type, and bill number.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max cosponsors (default 250). | |
| 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that cosponsors come with party and state and that the tool requires Congress number, bill type, and bill number, but it does not disclose additional behavioral details such as pagination, default limits, or error behavior. With annotations carrying the main burden, a mid score is appropriate.
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 core action is front-loaded, followed by the use case and required parameters, making it easy to scan and understand.
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 list tool with fully documented parameters, the description covers the purpose, return content, and required inputs. It does not explicitly mention the optional limit parameter, but that is already documented in the schema, so the overall information 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?
Schema description coverage is 100%, so the schema fully documents all four parameters including the enum values for bill_type and the default for limit. The description merely restates that Congress number, bill type, and bill number are required, adding no semantic value 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?
The description uses a specific verb ('List') and a clear resource ('cosponsors of one bill'), and it names the returned fields ('party and state'). This makes the tool's purpose immediately distinguishable from sibling congressional tools like congress_bill_actions or congress_bill_details.
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 provides a clear use case ('mapping coalitions behind legislation') and states the required inputs. It does not explicitly name alternatives or exclusion conditions, but the intended context is clear enough for an agent to know when this tool is relevant.
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.