review_jobRate the other party (1-5) after completion. Permanent; feeds reputation. If an automated judge (a model) chose the rating or wrote the comment rather than a person, pass machine_generated: true; the label is public.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | ||
| rating | Yes | ||
| comment | No | ||
| machine_generated | No | true when the rating/comment were produced by an automated judge |
Changes observed during successful MCP inspections.
Input schema / properties / machine_generatedAdded value: +{
+ "description": "true when the rating/comment were produced by an automated judge",
+ "type": "boolean"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure, and it delivers key consequences: the review is permanent, feeds reputation, and the machine_generated label is public. It also explains when to set that flag, which is valuable beyond the schema field description.
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?
Two sentences pack purpose, timing, permanence, reputation impact, a conditional flag, and privacy implications with no filler. The key behavioral constraints are front-loaded, making it easy for an agent to scan and act.
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?
The description is sufficient for a moderately complex tool: it covers when to use it, what it does, the critical machine_generated flag, and the irreversible/public nature of the review. It omits details like return behavior or whether comments are optional, but these are not essential given the schema and overall clarity.
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 only 25%, so the description meaningfully compensates by explaining the rating scale (1-5), the machine_generated condition (automated judge vs. person), and the public nature of the label. The remaining parameters, job_id and comment, are reasonably inferable from their names.
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 uses a specific verb ('Rate') and identifies the resource ('the other party') and timing ('after completion'). It clearly conveys the tool's core purpose, though it does not explicitly distinguish itself from related sibling tools like dispute_action or job_action.
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 phrase 'after completion' gives clear contextual timing for when the tool is appropriate. However, it does not mention exclusions or contrast with alternatives such as dispute_action or get_reputation, leaving some usage boundary inference to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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