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 key behaviors: pagination behavior ('Default returns one page of 500'), auto-fetch capability ('Set paginate="all" to auto-fetch every page'), and available filters. However, it doesn't mention potential rate limits, error conditions, or authentication requirements, which would be helpful for a tool with 9 parameters.

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 zero waste. It opens with the core purpose, immediately provides key usage information about pagination, lists available filters, and concludes with clear sibling tool alternatives. Every sentence serves a distinct purpose, making it easy to parse and understand.

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 (9 parameters, no annotations, but has output schema), the description is quite complete. It covers purpose, usage guidelines, pagination behavior, and filter options. The existence of an output schema means the description doesn't need to explain return values. However, for a tool with this many parameters and no annotations, mentioning authentication or rate limits would make it more complete.

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?

With 0% schema description coverage for 9 parameters, the description must compensate. It successfully explains the purpose of the 'paginate' parameter, lists available filter types (job_id, candidate_id, status, date ranges), and provides status enum values ('active'/'rejected'/'hired'). While it doesn't cover all 9 parameters individually, it adds substantial semantic value 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 as 'List applications with optional filters,' specifying both the verb ('List') and resource ('applications'). It distinguishes from siblings by explicitly naming three alternative tools (pipeline_summary, stale_applications/candidates_needing_action, pipeline_metrics) for different use cases, showing clear differentiation.

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 guidance on when to use this tool versus alternatives. It states 'For pipeline views with candidates grouped by stage, use pipeline_summary instead. For finding stale candidates, use stale_applications or candidates_needing_action. For conversion rates and hire metrics, use pipeline_metrics.' This gives clear context for when to choose this tool over siblings.

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