verify_provider_key

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

No annotations provided, but the description covers key behaviors: returns key validity, live models, error messages, and mentions env var fallback. It lacks explicit statement about being read-only or potential side effects, but is still thorough.

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: two paragraphs with clear purpose, returns, and provider lists. No unnecessary words; well 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?

Despite having no output schema, the description fully explains what the tool returns. It lists all supported providers and parameters. For a verification tool, it covers all necessary context.

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%, so baseline is 3. The description adds value by explaining the api_key can be omitted if env var is set, and clarifies provider_type values with use-case context (extract_data, sync_to_vectordb).

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 a specific action: 'Verify an LLM or embedding API key and return the live list of models.' It uses a strong verb-resource pair and is distinct from sibling tools.

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?

Explicitly advises to call this tool before any LLM/embedding operation and warns against assuming model names. This provides clear when-to-use guidance.

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