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 computes (conversion rates, stage metrics, time averages) and the return format (stage-by-stage breakdown with counts, percentages, time metrics), though it doesn't mention potential limitations like data freshness or error conditions.

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, followed by usage scenarios, computed metrics, and benefits. Every sentence adds value without redundancy, 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?

Given the tool's analytical nature, single parameter, and the presence of an output schema, the description provides complete context. It explains what metrics are calculated, when to use it, and the return structure, leaving no significant gaps for agent understanding.

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 implicitly clarifying that 'job_id' refers to the job for which pipeline metrics are computed. However, it doesn't specify format constraints or valid ranges for the job_id parameter.

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 ('Compute pipeline conversion rates and stage metrics') and resource ('for a job'), distinguishing it from sibling tools like 'pipeline_summary' or 'time_to_hire' by focusing on detailed stage-by-stage analysis rather than overall summaries.

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?

Explicit guidance is provided with concrete examples of when to use this tool ('when a recruiter asks "what are our conversion rates?" or "where are we losing candidates?"'), and it distinguishes itself from alternatives by stating 'One call instead of assembling data from multiple endpoints.'

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