colacloud-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what information is returned, it doesn't address important behavioral aspects like whether this is a read-only operation, authentication requirements, rate limits, error conditions, or what happens with invalid permit numbers. The description provides basic functional information but lacks critical behavioral context.

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 appropriately sized and well-structured with clear sections (purpose, arguments, returns). Each sentence serves a distinct purpose: the first states the tool's function, the second specifies what's returned, and the following sections document parameters and return values. There's minimal wasted text.

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?

Given the tool has an output schema (which handles return value documentation) and only one parameter with good semantic coverage in the description, the description is reasonably complete for a simple lookup tool. However, the lack of behavioral transparency and usage guidelines relative to sibling tools represents a significant gap in contextual understanding for an AI agent.

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?

With 0% schema description coverage and only one parameter, the description provides essential semantic context that the schema lacks. It explains that 'permit_number' is a 'federal permit number' and provides an example format ('NY-I-123'), which adds meaningful interpretation beyond the bare schema type declaration. This significantly compensates for the schema's lack of descriptions.

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 clearly states the tool's purpose: 'Get detailed information about a permit holder' with specific details about what information is returned (company details and recent COLAs). It uses a specific verb ('Get') and identifies the resource ('permit holder'), but doesn't explicitly differentiate from sibling tools like 'search_permittees' or 'get_cola'.

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 no guidance on when to use this tool versus alternatives. With sibling tools like 'search_permittees' and 'get_cola' available, there's no indication of when this specific tool is appropriate versus those alternatives, nor any prerequisites or contextual usage information.

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