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seungwonme

prompt-new-mcp

by seungwonme
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: 'list' retrieves existing prompts, while 'save' logs user messages. There is no overlap or ambiguity between these functions, making it easy for an agent to choose the correct tool based on the task.

    Naming Consistency5/5

    Both tool names follow a simple, consistent verb-only pattern ('list' and 'save'), which is clear and predictable. There are no deviations in naming style, making the set easy to understand at a glance.

    Tool Count2/5

    With only 2 tools, the server feels thin for a prompt management system. While 'list' and 'save' cover basic logging and retrieval, there are obvious gaps like updating, deleting, or searching prompts, which limits functionality for the domain.

    Completeness2/5

    The tool surface is severely incomplete for prompt management. It lacks essential operations such as update, delete, search, or get specific prompts, leaving agents unable to perform full CRUD workflows. The 'save' tool's mandatory use for logging also seems misaligned with typical prompt management tasks.

  • Average 3.2/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • 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. It states it lists prompts but doesn't disclose behavioral traits like whether it's read-only, what format the output is in, if there's pagination, or any error conditions. This is a significant gap for a tool with no annotation coverage.

    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 with zero waste. It's appropriately sized and front-loaded, efficiently conveying the core purpose without unnecessary details.

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

    Completeness2/5

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

    Given no annotations and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., list format, metadata), behavioral constraints, or usage context. For a tool with minimal structured data, this leaves significant gaps.

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

    Parameters3/5

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

    Schema description coverage is 100%, so the schema already documents the 'limit' parameter. The description doesn't add any meaning beyond what the schema provides, such as default behavior when limit is omitted or how prompts are ordered. Baseline 3 is appropriate when the schema does the heavy lifting.

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

    Purpose4/5

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

    The description clearly states the verb ('List') and resource ('saved prompts in the prompts directory'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling 'save' tool, which would require mentioning it's for retrieval rather than storage.

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

    Usage Guidelines2/5

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

    No guidance is provided on when to use this tool versus alternatives. The description doesn't mention the sibling 'save' tool or any context for usage, leaving the agent to infer based on the tool name alone.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It discloses behavioral traits such as the mandatory invocation timing and universal applicability, but lacks details on what happens during logging (e.g., where data is stored, error handling, or side effects). It adds some context but is incomplete for a tool with no annotation coverage.

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

    Conciseness4/5

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

    The description is concise with two sentences that directly state the tool's mandatory usage and purpose. It is front-loaded with the key requirement, though it could be slightly more structured by explicitly mentioning the tool name or resource type. Every sentence earns its place by providing essential guidance.

    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's complexity (simple logging with 2 parameters), no annotations, and no output schema, the description is partially complete. It covers usage guidelines well but lacks details on behavioral aspects like storage location or response format. It's adequate as a minimum viable description but has clear gaps in transparency.

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

    Parameters3/5

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

    The schema description coverage is 100%, so the schema already documents both parameters ('content' and 'name') with descriptions. The description does not add any meaning beyond what the schema provides, as it doesn't explain parameter roles or usage. Baseline 3 is appropriate when the schema does the heavy lifting.

    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's purpose as logging user messages, which is a clear verb+resource combination. However, it doesn't distinguish this from its sibling tool 'list' or specify what type of logging occurs (e.g., saving to a file, database). The purpose is somewhat vague beyond the mandatory calling requirement.

    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 provides explicit usage guidelines: 'MUST be called before responding to any user input' and 'always use this tool to log the user's message, regardless of its content or intent.' This clearly states when to use it (always, before responding) and includes no exclusions, though it doesn't mention alternatives since it's mandatory.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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