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tisu19021997

Meta Prompt MCP Server

by tisu19021997
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Python
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ready_to_answer

Present verified final solutions after obtaining independent expert review, ensuring accuracy and reliability in collaborative problem-solving workflows.

Instructions

Use this tool when you already obtained or verified the final solution with at least two independent experts and are ready to present your final answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/meta_prompt/server.py:107-115 (handler)
    The handler function for the 'ready_to_answer' tool. It is decorated with @mcp.tool() for registration and returns a string instructing to ask the user about the final answer presentation format.
    def ready_to_answer() -> str:
    """
    Use this tool when you already obtained or verified the final solution
    with at least two independent experts and are ready to present your final answer.
    """
    return (
        "Now let's ask the user how they want you to present "
        "the final answer (as a report, implement the solution, etc.)."
    )

Tool Definition Quality

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool is used when 'ready to present your final answer,' implying it might trigger a submission or output action, but it doesn't describe what the tool actually does behaviorally (e.g., whether it logs, notifies, or finalizes). This leaves significant gaps in understanding the tool's effects.

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 a single, clear sentence that efficiently states the usage condition without unnecessary details. It is front-loaded with the key information and has zero wasted words, making it highly concise and well-structured.

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

Completeness3/5

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

Given the tool has no parameters, no annotations, and no output schema, the description provides basic context about when to use it. However, it lacks details on what the tool does (e.g., behavioral outcomes or return values), which is a gap for a tool that likely triggers a significant action like finalizing an answer. This makes it minimally adequate but incomplete.

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?

The input schema has 0 parameters with 100% coverage, meaning there are no parameters to document. The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as it doesn't have to compensate for any gaps.

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

Purpose3/5

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

The description states the tool is used when 'ready to present your final answer,' which indicates its purpose is to signal completion of a verification process. However, it doesn't specify what action the tool performs (e.g., submits, logs, or finalizes the answer), making it somewhat vague. It distinguishes from sibling 'expert_model' by focusing on answer presentation rather than expert consultation.

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 states when to use this tool: 'when you already obtained or verified the final solution with at least two independent experts.' This provides clear context and prerequisites. However, it doesn't mention when not to use it or explicitly compare to alternatives like 'expert_model,' which could be used for obtaining expert input instead.

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

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