Greenhouse MCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a read operation (implied by 'List'), includes default behavior ('Default threshold is 14 days'), specifies sorting ('sorted by days since last activity'), and outlines the return format ('with candidate name, current stage, and job name'). However, it doesn't mention pagination or rate limits, leaving some gaps.

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 front-loaded with the core purpose in the first sentence, followed by usage context, defaults, filtering details, return format, and next steps. Every sentence earns its place with no wasted words, making it highly efficient and well-structured for quick comprehension.

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's moderate complexity (3 parameters, no annotations, but has an output schema), the description is largely complete. It covers purpose, usage, parameters, and return values adequately. The output schema likely details the return structure, so the description doesn't need to exhaustively explain outputs, but it could briefly mention pagination or error handling for full completeness.

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 0%, so the description must compensate. It explains the meaning of 'days' ('no activity for N days'), 'job_id' ('filter to one job'), and implies 'limit' through context (though not explicitly). The description adds significant value beyond the bare schema, clarifying the purpose and usage of each parameter, though it doesn't detail all three explicitly.

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 specific action ('List applications with no activity for N days') and resource ('applications'), distinguishing it from siblings like 'list_applications' by focusing on stale candidates. It also specifies the sorting order ('sorted by stalest first'), making the purpose explicit and distinct.

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 explicit guidance on when to use this tool ('for pipeline hygiene — find candidates that have been sitting untouched') and when not to use it (implied: for general application listing, use other tools). It also names an alternative action ('Use bulk_reject to act on the results'), clearly linking to a sibling tool for follow-up operations.

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