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mlobo2012

Claude Desktop API MCP

by mlobo2012
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Python

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or confusion between tools. The single tool has a clear, distinct purpose that cannot be mistaken for any other tool in the set.

    Naming Consistency5/5

    A single tool inherently demonstrates perfect naming consistency. There are no other tools to compare against, so no inconsistency can exist in the naming pattern.

    Tool Count2/5

    One tool is too few for meaningful interaction with a Claude Desktop API. While the tool's purpose is clear, a single send-message operation severely limits functionality and suggests an incomplete or minimal implementation.

    Completeness1/5

    The tool surface is severely incomplete for a Claude Desktop API. With only send-message, there are no tools for receiving messages, managing conversations, handling settings, or performing any other expected API operations. This creates significant dead ends for agents.

  • Average 2.9/5 across 1 of 1 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

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  • 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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It states the action ('send a message') but doesn't describe what happens after sending, whether there are rate limits, authentication requirements, response expectations, or error conditions. This leaves significant behavioral uncertainty for an agent.

    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 perfectly concise at just 4 words, front-loading the essential action without any wasted words. Every element earns its place, making it immediately understandable while being maximally efficient.

    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 insufficiently complete. It doesn't explain what happens after sending the message, what kind of response to expect, or any behavioral characteristics. For a communication tool with zero structured metadata, more context about the interaction pattern would be needed.

    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%, with the single parameter 'message' clearly documented in the schema. The description doesn't add any parameter information beyond what's already in the schema, so it meets the baseline for high schema coverage without providing additional semantic context.

    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 ('send') and resource ('message to Claude'), making the purpose immediately understandable. However, it doesn't differentiate from siblings since there are none, and could be slightly more specific about what type of message or context this involves.

    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?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. With no sibling tools, this is less critical, but still lacks any usage context that would help an agent determine appropriate invocation scenarios.

    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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