get_certificateThe full certificate for a dataset made by generate_dataset: every claim (stated vs actual),
every requirement's status and evidence, realism findings, planted defects/anomalies if any, and
the pattern charts. This is the whole answer key, not the trimmed form generate_dataset returns.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered structurally. The description earns credit by disclosing the payload's depth and contents (full vs trimmed), which is meaningful behavioral context the annotations cannot convey. It does not mention rate limits, auth requirements, or pagination, keeping it below a 5.
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 with no filler: the first establishes what the tool returns and enumerates contents, the second front-loads the key differentiation from the trimmed generate_dataset output. Every clause carries information.
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?
There is no output schema, so the description must describe the return surface — and it does so thoroughly, listing every category of certificate content. With annotations covering the safety profile and a single obvious parameter, nothing an agent needs to call this correctly is missing.
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 single parameter dataset_id is undocumented in the schema. The description implicitly scopes it by tying the tool to 'a dataset made by generate_dataset,' which gives the identifier useful provenance, but it adds no format, sourcing, or lookup detail beyond that. Adequate but with a clear gap.
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?
States a specific verb and resource ('the full certificate for a dataset') and enumerates exactly what it contains: claims stated vs actual, requirement statuses and evidence, realism findings, planted defects, and pattern charts. It also distinguishes itself from the sibling generate_dataset by noting this is the untrimmed form, so an agent can select it without opening either schema.
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 clearly situates usage: this is the 'whole answer key' versus the trimmed certificate generate_dataset returns, which tells the agent when to prefer this tool. It stops short of an explicit when-not-to-use statement or naming a distinct alternative tool by name, so it is clear context rather than a full routing rule.
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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