Server Quality Checklist

Tool Scores

• ask_local_llmA
  4.4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden. It discloses that on connection errors or timeouts, the tool returns an explanation string instead of throwing an exception—valuable behavioral context beyond the schema.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is compact and well-structured: a summary sentence, usage context, and documented args/returns. Every sentence adds value with no redundancy.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple one-parameter tool with no annotations, the description covers what it does, when to use it, and the return behavior including error handling. It lacks explicit prerequisites or model specifics, but these are not essential for basic usage.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 0% with only one required 'prompt' parameter. The description's Args section explains that prompt is the string passed to the LLM, adding a small but useful layer of meaning to the bare schema.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool sends a prompt to a local/cloud LLM via LiteLLM and returns the response text. The verb '投げ' (send) and resource 'LLM' are specific, and the purpose is unambiguous even without sibling tools.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly says to use this tool for offloading lightweight tasks like summarization, classification, or log pre-reading. It implies not for tasks the main agent should handle, but lacks explicit when-not conditions or named alternatives.

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