Recommend AI agent configuration

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

Annotations already indicate read-only and idempotent behavior. The description adds that it is stateless, does not store/list/update, and includes diagnostic capabilities (diagnoses oscillation, overload, etc.). It also lists return values, enhancing transparency beyond annotations.

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 concise, front-loaded with the usage directive, and contains no redundant information. Every sentence adds value, covering purpose, usage, behavior, and return values.

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?

Given the output schema exists, return values are already covered. The description explains what the tool returns (profile values, parameters, confidence, etc.) and describes input contexts. It also clarifies non-behaviors. For a stateless recommendation tool, it is complete.

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?

The description mentions environmental concepts like entropy and stakes, which map to input parameters, but does not elaborate on parameter structure. The schema provides descriptions for all fields, so the description adds moderate value by contextualizing the parameters within the tool's purpose.

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's purpose: to recommend LLM and runtime settings for AI agents. It uses specific verbs ('recommend', 'diagnoses', 'returns') and identifies the resource (agent configuration). It also explicitly states what the tool does not do (store, list, update), eliminating ambiguity.

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 begins with 'Use this...when...' providing clear usage context. It implicitly excludes use cases involving storage, listing, or updating past recommendations. However, without sibling tools, it does not contrast with alternatives explicitly.

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