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 what the tool returns (job info, stages with counts, per-candidate details) and its efficiency benefit (replaces 5-10 API calls). However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, leaving some behavioral aspects uncovered.

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 efficiently structured with front-loaded purpose, clear usage guidelines, and return details. Every sentence adds value: the first states what it does, the second when to use it, the third describes returns and efficiency, and the fourth elaborates on return structure. No wasted words.

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 complexity (aggregating pipeline data), the description is complete enough. With no annotations, it fully explains purpose, usage, and returns. The existence of an output schema means the description doesn't need to detail return values exhaustively, and it appropriately focuses on the high-level structure and benefits.

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?

The input schema has 0% description coverage, but the description compensates by clearly explaining that the single parameter 'job_id' is used to get the pipeline view for a specific job. While it doesn't provide format details beyond what the schema indicates (integer type), it adds meaningful context about what the parameter represents in the tool's operation.

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 ('Get a complete pipeline view') and resources ('for a job — candidates grouped by stage'). It distinguishes from siblings by emphasizing a comprehensive view that replaces multiple API calls, unlike simpler list or get tools in the sibling set.

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 explicitly states when to use this tool with concrete examples ('when a recruiter asks "show me the pipeline" or "how many candidates are in each stage for job X"'). It also implies an alternative approach (5-10 sequential API calls) that this tool replaces, providing clear context for usage.

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