Greenhouse MCP

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

No annotations are provided, so the description carries full burden. It explains what the tool does (aggregates applications, calculates metrics) but lacks details on behavioral traits like data freshness, permission requirements, rate limits, or error handling. It's adequate but has clear gaps for a data analysis tool.

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: purpose first, then usage examples, then parameter guidance. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly.

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 (2 parameters, analysis function) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, usage, and parameters well, though could benefit from more behavioral context given the lack of annotations.

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 effectively explains both parameters: 'job_id' for specific job analysis vs. org-wide, and 'created_after' for time window limitation with format example. This adds crucial meaning beyond the bare schema.

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 ('analyze', 'aggregates', 'calculates') and resources ('candidate sources', 'applications by source'). It distinguishes from siblings by focusing on source effectiveness metrics, unlike other tools that handle applications, candidates, or jobs directly.

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 "which sources are working?" or "where should we invest recruiting budget?"'). It distinguishes from siblings by specifying this tool is for analyzing source performance, not general candidate or job operations.

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