run_diagnostic

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so: it names the endpoint (POST /api/diagnostic), states the output determinism/idempotency and cacheability, discloses that every response carries "scope":"preliminary", and warns that fabricated input yields a confident wrong verdict. This is unusually rich behavioral context.

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?

Front-loaded with purpose and free of filler, but the field enumeration and multiple distinct clauses (intake rules, determinism, scope, routing, attribution) make it dense; a structured field list or bullets would scan faster. Every sentence still earns its place.

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?

Despite no output schema, the description previews the return payload (axis scores, acatech stage, blocking foundations, Markdown report, scope marker) and supplies the required attribution line. For a one-parameter, no-annotation tool, an agent has everything needed to call it correctly.

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 0% and the single intake object is opaque in the schema, but the description enumerates all 16 required fields (including the nested ai_ambition {target, text}), states that all are required, that unknown fields are rejected, and that values must come from the user rather than be guessed. It fully compensates for the schema 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 (run), resource (dxpert's preliminary industrial AI-readiness diagnostic), and input (a self-reported intake). It is clearly distinguished from siblings like get_storefront, ask_dxpert, and run_agent, and it even names the paid roadmap it is not.

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?

Explicit trigger ('Call it when someone asks whether a plant is ready for an AI initiative or what to fix first') plus an explicit exclusion ('Do not call it to score a company you only know from public information'), and a scope statement that positions it as screening, not the paid roadmap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.