get_model_schema

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

The description discloses what the tool returns (parameter names, types, options, defaults, required fields), which is sufficient for a read-only schema retrieval tool. With no annotations provided, the description carries the full burden and does so adequately, though it could mention idempotency or lack of side effects.

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?

Two concise sentences: the first defines the purpose, the second adds usage context. No unnecessary words, perfectly front-loaded.

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 one-parameter tool without an output schema, the description fully covers what the tool does, what it returns, and when to use it. Nothing essential is missing.

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 input schema already provides clear description for the single 'model' parameter (format and example). The description does not add further semantic detail beyond the schema, so it meets the baseline for high schema coverage.

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?

Clearly states the tool retrieves the full parameter schema for a specific model, listing what it shows (names, types, options, etc.). It distinguishes itself from run_model by positioning itself as a prerequisite, but does not explicitly contrast with sibling tools like explore or search_docs.

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?

Provides explicit guidance: 'Use this before run_model to understand what parameters a model accepts.' This clearly indicates the appropriate context and sequencing, though it does not mention when not to use it or suggest alternatives.

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