Aggregate complaint counts by a single facet (product, issue, company, state, company_response, or submitted_via). Useful for ranking companies by complaint volume or finding the most common issue categories.
| Name | Required | Description | Default |
|---|---|---|---|
| facet | Yes | Field to aggregate by | |
| company | No | Optional company filter |
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, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds that results are aggregate counts by a single facet, but it does not disclose output structure, limits, sorting, or other behavioral nuances. There is no contradiction with the annotations.
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 sentences with no filler: it opens with a precise statement of what the tool does, enumerates the facet options, and closes with relevant use cases. Every sentence contributes 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 two-parameter read-only aggregation tool with a complete schema and safety annotations, the description is largely sufficient: it names the facets, describes the output concept (aggregate counts), and gives practical use cases. Since there is no output schema, a slightly more explicit description of the response shape or ordering would make it fully complete, but this is 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?
The input schema already documents both parameters fully, including an enum for facet and a description for company, so schema coverage is 100%. The description reinforces the meaning of the facet parameter by listing valid values and stating the use cases, but it does not add substantial semantic detail 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 states a specific verb and resource ('Aggregate complaint counts') and enumerates the exact allowed facets, making the tool's function immediately clear. It is clearly distinguishable from sibling tools like cfpb_complaint_detail, cfpb_search_complaints, and cfpb_complaint_trends because it focuses on single-facet aggregation rather than individual records, search, or time trends.
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 concrete use cases ('ranking companies by complaint volume' and 'finding the most common issue categories'), which gives useful context for when to invoke it. However, it does not explicitly mention alternatives or exclusion conditions, such as when to prefer cfpb_complaint_trends or cfpb_search_complaints instead.
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.