contractor-license-mcp-server

clv_verify_license

Read-onlyIdempotent

Verify contractor license status, expiration, and disciplinary history by checking state licensing board portals in real-time across 45 US states.

Instructions

Verify a contractor's license by checking the state licensing board portal. Returns license validity, holder name, status, expiration, and any disciplinary actions.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "state": "<state>",
      "license_number": "<license_number>"
    },
    "name": "clv_verify_license"
  }
}

Input Schema

Name	Required	Description	Default
`state`	Yes	Two-letter US state code (e.g. 'CA', 'TX'). Use clv_list_supported_states to see available states.
`license_number`	Yes	The contractor's license number as shown on their license card. Format varies by state.
`trade`	No	The trade/contractor type (e.g. 'General Contractor', 'Electrical'). Use clv_list_supported_states to see valid values per state.	general
`force_refresh`	No	Bypass cache and fetch fresh data from the state portal.
`response_format`	No	Response format.	markdown

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.3Apr 13, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, indicating a safe, non-mutating, repeatable operation. The description adds valuable context beyond annotations: it specifies the data source ('state licensing board portal'), mentions caching behavior ('bypass cache' via force_refresh parameter), and lists the specific return fields (e.g., disciplinary actions), enhancing transparency about behavior and output without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, dense sentence that front-loads the core action and resource, followed by a concise list of return values. Every word earns its place, with no redundancy or fluff, making it highly efficient and well-structured for quick comprehension by an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (5 parameters, no output schema) and rich annotations (read-only, non-destructive, idempotent), the description is largely complete: it covers purpose, data source, return fields, and caching behavior. However, it lacks details on error handling (e.g., invalid license numbers), rate limits, or authentication requirements, which could be useful for an agent despite the safe annotations. The absence of an output schema means the description must fully explain returns, which it does adequately but not exhaustively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with each parameter well-documented in the input schema (e.g., state codes, license number format, trade defaults, force_refresh purpose, response_format options). The description does not add significant semantic details beyond the schema, such as explaining parameter interactions or trade-specific nuances. With high schema coverage, the baseline score of 3 is appropriate, as the description relies on the schema for parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('verify a contractor's license'), resource ('contractor's license'), and scope ('by checking the state licensing board portal'), distinguishing it from sibling tools like clv_batch_verify (batch operations) and clv_search_by_name (search by name). It explicitly mentions the return data (validity, holder name, status, expiration, disciplinary actions), making the purpose highly specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (verifying individual licenses via state portals) and references sibling tools (clv_list_supported_states for valid inputs), providing clear guidance on prerequisites. However, it does not explicitly state when to use this tool versus alternatives like clv_batch_verify (e.g., for single vs. multiple licenses) or clv_search_by_name (e.g., when license number is unknown), missing explicit exclusions or comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server