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 of behavioral disclosure. It mentions retrieval via the Ingestion API but doesn't cover key traits like whether it's read-only (implied by 'retrieve'), pagination behavior (though parameters hint at it), rate limits, authentication needs, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly, which aligns well with the conciseness dimension.

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 has an output schema (which reduces the need to describe return values), no annotations, and low parameter complexity, the description is somewhat complete but lacks depth. It covers the basic purpose but misses usage guidelines, behavioral details, and parameter explanations, making it minimally viable but with clear gaps for effective agent use.

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?

The input schema has two parameters ('per_page' and 'page') with 0% description coverage, meaning the schema provides no semantic details. The description doesn't add any meaning to these parameters beyond what's implied by their names (e.g., it doesn't explain default values, ranges, or how they affect retrieval). However, with only two parameters and a baseline of 3 due to minimal complexity, the score reflects adequate but uninformative handling.

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 states the tool's purpose as 'Retrieve jobs visible to the partner via Ingestion API,' which includes a verb ('Retrieve') and resource ('jobs'). However, it's somewhat vague about what 'jobs' specifically refers to (e.g., ingestion jobs, processing jobs) and doesn't distinguish it from sibling tools like 'list_jobs' or 'retrieve_ingestion_candidates,' which could lead to confusion in selection.

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?

No guidance is provided on when to use this tool versus alternatives. The description lacks context such as prerequisites, when it's appropriate compared to other job-related tools (e.g., 'list_jobs'), or any exclusions. This absence makes it harder for an agent to decide correctly without additional inference.

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