Greenhouse 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. It clearly indicates this is a read/analysis operation (not a mutation) by using 'calculate' and 'analyzes,' which is helpful. However, it doesn't disclose important behavioral aspects like whether it requires specific permissions, how it handles data freshness, pagination, or error conditions for invalid inputs. The description adds basic context but lacks depth for a tool with no annotation support.

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 perfectly structured and concise. The first sentence states the core purpose, followed by usage examples, then the computation details, and finally parameter guidance. Every sentence earns its place with no wasted words, and key information is front-loaded.

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 that an output schema exists (context signals indicate 'Has output schema: true'), the description doesn't need to explain return values. It adequately covers the tool's purpose, usage, and basic parameter guidance. However, with no annotations and incomplete parameter documentation (missing created_after), there are gaps in behavioral transparency and parameter semantics that prevent a perfect score.

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 semantics of the job_id parameter ('for a specific role or omit for org-wide metrics'), which is valuable. However, it doesn't mention the created_after parameter at all, leaving one of the two parameters completely undocumented. The description adds meaningful context for one parameter but misses the other.

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 ('calculate time-to-hire metrics') and the resource ('hired candidates'), distinguishing it from siblings like pipeline_metrics or source_effectiveness. It explicitly defines the scope as analyzing hired applications to compute specific statistics (average, median, min, max days from application to hire).

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 guidance with concrete examples ('when asked "how long does it take to hire?" or "what's our average days-to-offer?"'). It also clarifies the scope decision point: 'Pass job_id for a specific role or omit for org-wide metrics,' which helps the agent choose between this tool and potential alternatives for different granularities.

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