Score AI readiness answers with the same maths as shipshapedata.com/ai-readiness: returns a 0-100 score, one of five maturity bands with guidance, per-area averages, and the weakest area with advice.
| Name | Required | Description | Default |
|---|---|---|---|
| answers | Yes | Either 16 option indices (0-4, all questions in order) or 15 point values (1-5, scored questions only, in order) |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint true, and destructiveHint false. The description adds behavioral value beyond these by specifying the return contract: a 0-100 score, five maturity bands with guidance, per-area averages, and the weakest area with advice. This is additional transparency about what the call produces.
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?
A single front-loaded sentence that efficiently packs the action, the reference method, and the concrete output list without redundancy or filler.
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?
With one parameter, no output schema, and minimal annotations, the description covers the core information an agent needs: inputs are answers, outputs include a score, bands, averages, and advice. It could optionally mention that the questionnaire comes from get_ai_readiness_questions, but that is inferrable and not essential for correct invocation.
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 100%: the parameter answers is fully documented with the two accepted formats (16 option indices or 15 point values) and their ordering. The description adds no further parameter detail, so the baseline 3 applies.
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 action (Score AI readiness answers), cites the reference method, and enumerates concrete outputs. This clearly differentiates it from sibling tools like get_ai_readiness_questions, which is the questionnaire counterpart, and search_resources/list_services, which serve different purposes.
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 wording 'Score AI readiness answers' implies the context of scoring questionnaire responses, but the description does not explicitly state when to use this tool versus alternatives such as get_ai_readiness_questions, nor mention that questions must first be obtained. The usage is implied rather than explicit.
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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