Parse contractor license text and return structured status (valid/expired/suspended/revoked), expiry date, days until expiry, disciplinary actions, trade, state.
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion with another tool. The single tool has a clearly distinct purpose: parsing license text to extract status and expiry details.
The lone tool follows a clear verb_noun pattern: check_license_expiry. With only one name, consistency is trivially satisfied.
A single-tool server is borderline thin for a domain named 'License Checker'. While the tool is focused and non-trivial, the count falls below the typical 3-15 range and may feel under-scoped.
The tool covers text parsing for expiry and status, but the broader domain of license checking likely needs operations like verifying license numbers, searching by contractor, or validating against official sources. These notable gaps could cause agents to hit dead ends for common license-checking workflows.
Parse contractor license text and return structured data: status (valid/expired/suspended/revoked), expiry date, days until expiry, disciplinary actions, trade, state. Useful for AI agents working with construction, property management, insurance.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Raw license text (from state licensing board or document) | |
| state | No | 2-letter US state code (e.g. CA, TX, NY) — optional context | |
| trade | No | Trade type (electrical, plumbing, hvac, general, roofing) — optional context |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden. It does disclose the return shape and the possible status values (valid/expired/suspended/revoked), which is genuine transparency. However, it says nothing about failure modes (unparseable text, unrecognized state format), whether the optional state/trade inputs change accuracy, or any side effects — notable omissions for a tool with zero annotation coverage.
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?
Two sentences, front-loaded with the operation and the returned fields, with the enumerations doing useful work. The trailing 'Useful for AI agents working with...' clause is soft filler rather than actionable content, which keeps it out of the top band.
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?
There is no output schema, so the description must describe returns — and it does, listing status, expiry date, days until expiry, disciplinary actions, trade and state. Combined with a fully documented 3-parameter schema, an agent has enough to invoke it. It falls short of 5 because input-tolerance and error behavior remain unspecified.
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 three parameters and the baseline is 3. The description does not clarify the meaningful distinction that 'state' and 'trade' are optional parsing hints whose presence materially changes extraction quality, and it confusingly lists them among output fields. It therefore adds little 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 ('parse') on a specific resource ('contractor license text') and enumerates the extracted fields, so an agent knows exactly what it gets back. There is a mild scope mismatch with the tool name 'check_license_expiry', since the tool actually extracts a broad set of fields rather than just checking expiry, but the core purpose is unmistakable.
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 closing sentence ('Useful for AI agents working with construction, property management, insurance') describes an audience, not a triggering condition. Nothing tells the agent when to prefer this tool, what input it expects in practice, or what it should do when extraction fails. With no siblings there are no alternatives to route between, but the absence of any situational guidance is still a gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
check_license_expiry
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