Greenhouse MCP

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

With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses that tags are created if they don't exist, processing is sequential with rate-limit delays, and returns success/failure counts. This covers key operational traits like side effects and performance characteristics, though it doesn't specify exact rate limits or error handling details.

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 front-loaded with the core purpose in the first sentence, followed by usage examples, parameter guidance, and behavioral notes in compact sentences. Every sentence adds value without redundancy, making it efficiently structured 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 complexity (batch mutation with rate limits), no annotations, and an output schema present (which likely covers return values), the description does well by explaining use cases, behavior, and parameters. It could improve by detailing error cases or prerequisites, but it provides sufficient context for effective tool selection and invocation.

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 that 'candidate_ids' is a list of IDs and 'tag_name' is the tag name (created if missing), adding basic meaning. However, it doesn't provide details on ID formats, tag name constraints, or list size limits, leaving some parameter semantics unclear despite the baseline being lowered due to poor schema coverage.

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 ('Add a tag to multiple candidates'), resource ('candidates'), and scope ('in one call'). It distinguishes from sibling 'add_tag_to_candidate' by emphasizing batch processing versus single-candidate tagging, making the purpose unambiguous.

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 clear context for when to use this tool ('to tag a batch of sourced candidates, mark candidates from a hiring event, or categorize candidates for reporting'), offering practical scenarios. However, it does not explicitly state when NOT to use it or mention the single-candidate alternative ('add_tag_to_candidate') by name, which would be needed for a perfect score.

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