Generate an AI analysis report for a survey's responses. Returns executive summary, key metrics, per-question insights, and action recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| survey_id | Yes | The ID of the survey to analyze. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral transparency. It describes outputs but does not disclose whether the tool is read-only, destructive, or requires specific authentication/authorization. Given the absence of annotations, more behavioral context is needed.
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 two sentences: first states the purpose, second lists outputs. Every sentence adds value, no redundancy. It is front-loaded and efficient.
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?
For a simple tool with one parameter and no output schema, the description adequately covers input and output. However, missing usage guidelines and behavioral transparency prevent a top score. Overall, it is complete enough given low complexity.
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 coverage is 100% (single parameter survey_id with description). The description adds context by linking the parameter to the survey's responses, but does not significantly extend beyond the schema's own description. Baseline 3 is appropriate.
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 tool generates an AI analysis report for a survey's responses, listing specific outputs (executive summary, key metrics, per-question insights, action recommendations). This distinguishes it from sibling tools which handle survey creation, listing, responses, or publishing, leaving no ambiguity about purpose.
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?
While the description implies use after responses are collected, it lacks explicit guidance on when to use this tool versus alternatives, prerequisites (e.g., survey must have responses), or exclusions. Usage is implied but not directly addressed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools have distinct purposes, but create_survey and get_survey_link both involve shareable links, causing slight overlap. Otherwise clearly differentiated.
All tools follow a consistent verb_noun pattern using snake_case, e.g., create_survey, get_responses, publish_survey.
Six tools are well-scoped for a survey management server, covering creation, listing, publishing, and data retrieval without being excessive.
Core survey lifecycle is covered (create, publish, list, get responses, report), but missing update and delete operations are minor gaps.