Search LDA contribution reports (political contributions disclosed by lobbyists/registrants). Filter by year, registrant, or lobbyist name.
| Name | Required | Description | Default |
|---|---|---|---|
| page_size | No | Results per page (default 20). | |
| filing_year | No | Filing year, e.g. 2025. | |
| lobbyist_name | No | Lobbyist name, partial match. | |
| 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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful domain context (LDA source, contribution reports) but does not disclose pagination behavior, output format, or matching semantics beyond what the schema already states.
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 tight sentences with no redundant filler. It front-loads the core resource and action, then lists the available filters. Every sentence 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 read-only search tool with strong annotations and fully documented parameters, the description is adequate but not complete. It does not describe the return value shape, pagination behavior, or how this tool differs from several similarly named lobbying siblings, all of which an agent might need to choose correctly among the available tools.
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 four parameters including defaults and partial-match behavior. The description only restates the year/registrant/lobbyist filters and adds no extra meaning about page_size or parameter relationships.
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 names a specific verb ('Search'), a specific resource ('LDA contribution reports'), and clarifies the domain ('political contributions disclosed by lobbyists/registrants'). It is clear on its own, but it does not explicitly differentiate itself from the closely named sibling tools like lobbying_search, lobbying_lobbyists, or lobbying_registrants.
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 usage: use this to find political contribution reports and filter by year, registrant, or lobbyist name. However, it provides no explicit when-to-use versus alternatives like lobbying_search or lobbying_detail, and no exclusion criteria or guidance on when not to use this tool.
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.