Official contractor-license verification sources and freshness caveats for 12 states. Use this before a state lookup so an agent can route work to the authoritative board instead of relying on a lead database.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter state code. |
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?
With readOnlyHint, idempotentHint, and destructiveHint false, the annotations already establish a safe read-only profile. The description adds context beyond that: the output includes 'freshness caveats' and the tool is meant to direct agents to authoritative boards. 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 the core deliverable and immediately follows with the usage directive, so the most important information 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 single-parameter, read-only reference tool, the description covers the key facts: what it provides, which states it covers, and when to call it. Although there is no output schema, 'sources and freshness caveats' gives an adequate sense of the return value. It does not detail the output structure, but that is not essential 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 coverage is 100%; the state parameter has a clear description ('Two-letter state code') and an enum constraining the 12 valid values. The description adds no parameter-specific detail beyond confirming the 12-state scope, so the baseline 3 applies.
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 defines the tool as providing 'official contractor-license verification sources and freshness caveats' for a fixed set of states, which is a specific resource. It also clarifies its role in routing work to the authoritative board. However, it lacks an explicit verb like 'returns' and does not differentiate from sibling tools by name.
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 explicitly instructs the agent to use this tool 'before a state lookup' and frames the goal as routing to authoritative sources 'instead of relying on a lead database.' This provides clear when-to-use context. It does not name alternative tools or state exclusions, so it stops short of a 5.
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.