recommend_servers

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

With no annotations provided, the description carries the full transparency burden. It discloses the sampling mechanism, the fallback behavior when sampling is unsupported, and the return structure (recommendations with scores and rationale). This provides good insight into how the tool behaves and its limits.

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?

Three sentences, each earning its place: the first defines the action, the second defines the output and fallback, the third gives usage guidance. No filler or redundancy.

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 read-only recommendation tool with no output schema, the description covers the input, mechanism, output, fallback, and usage context. It doesn't detail error conditions or the shape of the fallback catalog, but these are minor given the tool's simplicity.

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 (task_description and max_results) with clear descriptions, so the description adds no extra parameter context. It restates that the input is a natural-language task description without adding syntax or format details, so the baseline of 3 is appropriate.

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 specific verb ('recommend'), a resource (servers), and the input (a natural-language task description). It explains the mechanism (host LLM sampling) and output (ranked recommendations with scores and rationale), which clearly distinguishes it from siblings like search and list_servers that are direct retrieval operations.

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 states when to use: when the user's request spans domains and you're unsure which server to use. It does not mention when not to use or name alternatives explicitly, but the use case is clear enough for an agent to decide without confusion.

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