get_docs

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

With no annotations, the description carries the burden of behavioral disclosure. 'Search' reasonably signals a read-only operation and 'latest docs' signals freshness, but it doesn't state what is returned, whether network access is required, or failure behavior.

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 compact sentences: the first states the action and parameters, the second provides the scope. There is no filler or redundant restatement of parameter names.

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?

The tool is simple and fully parameterized, but with no output schema the description still omits the return format and any caveats about the search. It is adequate for invoking the tool, but not fully self-sufficient.

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 schema already documents both parameters with examples, so the baseline is 3. The description adds the supported-library list (langchain, openai, llama-index, uv), which constrains the 'library' parameter beyond what the schema provides.

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 states a clear verb ('Search'), resource ('latest docs'), and the two inputs (query, library), and names the supported libraries. It is unambiguous, though there are no sibling tools to differentiate from and it doesn't describe the result shape.

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 explicit 'Supports langchain, openai, llama-index and uv' tells the agent which libraries are valid, making applicability clear. It stops short of 5 because there is no explicit when-not-to-use or alternative routing, though no siblings exist.

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