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 implies a mutation operation ('anonymize') but does not specify whether this is reversible, what permissions are required, how it affects data integrity, or any rate limits. The mention of GDPR/compliance hints at legal implications but lacks operational details, leaving significant gaps in transparency.

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 a single, efficient sentence that directly states the tool's purpose without redundancy. It is front-loaded with the core action and context, making it easy to parse quickly, with no wasted words.

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 complexity of a mutation tool with GDPR implications, no annotations, 0% schema coverage, and 2 parameters, the description is inadequate. It lacks details on behavior, parameter usage, output (though an output schema exists, the description doesn't hint at it), and integration with sibling tools, leaving critical gaps for effective tool 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 schema provides no parameter descriptions. The description mentions 'specific fields' but does not clarify what 'fields' parameter entails (e.g., which fields are anonymizable, format, or examples) or the 'candidate_id' usage. It adds minimal semantic value beyond the parameter names, failing to compensate for the low 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 action ('anonymize') and target ('specific fields on a candidate'), with a specific purpose ('for GDPR/privacy compliance'). However, it does not explicitly differentiate from sibling tools like 'update_candidate' or 'delete_candidate', which might also handle candidate data modifications, leaving some ambiguity about its unique role.

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 no guidance on when to use this tool versus alternatives (e.g., 'update_candidate' for non-anonymizing changes or 'delete_candidate' for removal). It mentions GDPR/privacy compliance as a context but does not specify prerequisites, constraints, or exclusions, offering minimal usage direction.

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