Search registered lobbying firms (registrants) by name. Returns registrant profile records.
| Name | Required | Description | Default |
|---|---|---|---|
| page_size | No | Results per page (default 20). | |
| registrant_name | No | Registrant (firm) name, partial match. |
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, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that the tool returns 'registrant profile records,' which is useful since no output schema exists, but it does not disclose additional behaviors such as pagination behavior or field-level output details.
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 a single front-loaded sentence that names the action, target, search field, and return type with no wasted words. It is appropriately sized for a simple lookup tool and every phrase contributes meaningful 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, optional-parameter search tool with strong annotations and full schema coverage, the description is mostly adequate: it identifies the resource, the search field, and the return type. It loses a point because it does not clarify how this tool relates to nearby lobbying tools or describe the structure of the returned profile records, which would be helpful without an 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%, so both registrant_name and page_size are already documented. The description reinforces the meaning of registrant_name by saying 'by name' but adds nothing about page_size or result control beyond what the schema provides, which keeps this at the baseline.
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 states a specific verb and resource: 'Search registered lobbying firms (registrants) by name.' It also indicates the return type ('registrant profile records'), making the core purpose unambiguous. However, it does not explicitly differentiate this tool from closely related siblings like lobbying_search or lobbying_lobbyists.
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 phrase 'by name' implies a usage context, but the description offers no guidance on when to choose this tool over alternatives, no exclusions, and no mention of related lobbying tools. An agent would have to infer selection criteria from the tool name and sibling list rather than from explicit 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.
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.