Create an explicit evidence checklist for a scientific question, add question-specific requirements, then submit each field to audit_answer to verify evidence completeness.
Start scientific questions with an explicit evidence checklist. Add question-specific requirements; later submit every field to audit_answer. No scientific completeness guarantee.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | ||
| extra_fields | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only/non-destructive/openWorld=false and oddly declare idempotentHint=false with no explanation here. The description adds one genuine behavioral note ('No scientific completeness guarantee'), which is useful scope framing, but says nothing about what the tool returns, whether state is stored between calls, or how the checklist relates to the later audit.
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?
Three short sentences, front-loaded with the action, then the customization, then the hand-off. Nothing wasted, though the first sentence's phrasing is slightly awkward and could be a flat imperative.
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 should explain what a 'checklist' is structurally — default fields, return shape, whether it is text or structured. It also leaves the idempotency flag unexplained. For a tool whose whole value is the artifact it produces, this is a significant omission.
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 carry parameter meaning, and it only vaguely gestures at extra_fields via 'add question-specific requirements' while never explaining the required 'question' input or the format of the extra field names. An agent must guess whether extra_fields are field labels, criteria strings, or evidence categories.
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 verb phrase 'Start scientific questions with an explicit evidence checklist' gestures at a checklist-creation tool, but never states plainly what it produces (a template? a stored scaffold? a returned field list). Naming audit_answer does differentiate it from siblings like search/fetch, so it is not a tautology, but the core purpose stays abstract.
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?
It gives a clear trigger ('start scientific questions'), an in-lifecycle action ('add question-specific requirements'), and an explicit follow-up ('later submit every field to audit_answer'). That is solid workflow routing; only the exclusion side (when NOT to build a checklist) is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.