Server Quality Checklist

Tool Scores

• interactive_feedbackA
  4.7/5.0
  Behavior5/5

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

  The description extensively explains behavior beyond annotations: blocks until user submits, returns MCP content blocks, raises ToolError on validation, and documents accepted/ignored parameters for cross-tool compatibility. Annotations (readOnlyHint=false, etc.) are consistent with the description; no contradiction.

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

  Conciseness2/5

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

  The description is excessively long and includes internal implementation details (import statement, cold-start cost, FastMCP context injection, Python code comments). These are not essential for an AI agent selecting or invoking the tool. The structure is organized, but the verbosity reduces conciseness.

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

  Completeness5/5

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

  Given the tool's complexity (23 optional parameters, aliases, loop engineering features, output behavior), the description is remarkably complete. It covers expected behavior, parameter interactions, error handling, and output format. No gaps are apparent.

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

  Parameters5/5

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

  Although schema coverage is 100%, the description adds significant value by explaining aliases, ignored parameters, best practices (e.g., 'prefer the dict form for predefined_options'), truncation behavior, and loop engineering semantics. This goes far beyond the schema's basic descriptions.

  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 verb ('Ask the human user for interactive feedback') and the resource ('through the Web UI'). It lists multiple specific use cases (decision, clarification, confirmation, plan approval, design review, final sign-off), making the purpose unmistakable. No siblings exist, so differentiation is not needed.

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

  Usage Guidelines5/5

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

  The description explicitly tells when to use the tool: 'whenever you need a human decision... especially when the next step has multiple valid approaches, irreversible side effects, or significant trade-offs.' This is concrete and actionable. It does not discuss when not to use, but given the lack of siblings, the guidance is sufficient.

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