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 does well by explaining what the tool identifies (applications with no activity, interviews without scorecards, candidates stuck in early stages), default behavior (stale_days default: 7), and output format (categorized action items sorted by urgency). However, it doesn't mention potential side effects, rate limits, or authentication requirements, which keeps it from a perfect score.

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 well-structured and front-loaded with the core purpose. Every sentence adds value: the first states what it finds, the second gives usage examples, the third details what it identifies, the fourth explains parameters, and the fifth describes the return format. There's no wasted text, making it efficient and easy to parse.

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 tool with 2 parameters, 0% schema coverage, no annotations, but an output schema, the description is complete. It covers purpose, usage, parameters, and output behavior. The presence of an output schema means the description doesn't need to detail return values, and it adequately addresses all other aspects given the tool's complexity and context.

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?

Given that schema description coverage is 0%, the description must fully compensate, which it does excellently. It explains both parameters: 'job_id to filter to a specific job, or omit for all active applications' and 'stale_days controls the threshold (default: 7 days without activity = stale).' This adds crucial meaning beyond the bare schema, making parameters understandable and actionable.

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 with specific verbs and resources: 'Find candidates that need attention — stale applications, missing scorecards.' It explicitly distinguishes this from sibling tools by focusing on action items rather than general listing or retrieval operations, making it easy to understand its unique role.

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 usage scenarios: 'Use this when a recruiter asks "what needs my attention?" or "who's been sitting too long?"' It also clarifies when to omit parameters: 'Pass job_id to filter to a specific job, or omit for all active applications.' This gives clear guidance on when and how to use the tool effectively.

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