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dpaluy

MCP Claude Desktop

by dpaluy
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Server Quality Checklist

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  • Latest release: v1.2.0

  • Disambiguation5/5

    The two tools have completely distinct purposes with no overlap: 'ask' is for sending prompts and getting responses, while 'get_conversations' is for retrieving conversation lists. An agent can easily tell them apart as they target different actions and resources.

    Naming Consistency4/5

    The naming is mostly consistent with a verb-based pattern ('ask', 'get_conversations'), but there is a minor deviation: 'ask' uses a simple verb while 'get_conversations' follows a verb_noun format. This slight inconsistency is noticeable but does not hinder readability.

    Tool Count3/5

    With only 2 tools, the count feels thin for a Claude Desktop server, which might be expected to handle more interactions like managing conversations or settings. While it covers basic prompt and conversation listing, the scope seems limited, bordering on under-scoped for the apparent domain.

    Completeness2/5

    There are significant gaps in the tool surface for a Claude Desktop server. It lacks operations for managing conversations (e.g., delete, rename), handling settings, or interacting with specific conversation details. This incomplete coverage could lead to agent failures when trying to perform common desktop tasks beyond basic prompting and listing.

  • Average 3/5 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

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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 of behavioral disclosure. It states the tool sends a prompt and gets a response, implying a read/write interaction, but lacks details on permissions, rate limits, error handling, or response format. This is inadequate for a tool with 4 parameters and no output schema, leaving significant gaps in understanding its behavior.

    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, efficient sentence: 'Send a prompt to Claude Desktop and get a response.' It is front-loaded with the core action and has no wasted words, making it highly concise and well-structured for quick understanding.

    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 the complexity (4 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't cover parameter meanings, behavioral traits, or output details, leaving the agent with insufficient information to use the tool effectively. The conciseness doesn't compensate for these gaps.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the schema provides no parameter details. The description mentions 'prompt' implicitly but doesn't explain any of the 4 parameters (prompt, conversationId, timeout, pollingInterval) or their purposes. It adds minimal value beyond the schema, failing to compensate for the lack of coverage.

    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 tool's purpose: 'Send a prompt to Claude Desktop and get a response.' It specifies the verb ('send') and resource ('Claude Desktop'), making the action clear. However, it doesn't differentiate from its sibling tool 'get_conversations,' which appears to be a different operation, so it doesn't fully distinguish from alternatives.

    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. It doesn't mention the sibling tool 'get_conversations' or any other context for usage, such as prerequisites or scenarios. This leaves the agent without explicit direction on tool selection.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation which implies read-only behavior, but doesn't specify whether this requires authentication, what format the conversations are returned in, if there are rate limits, or how many conversations are returned (all vs paginated). The description is minimal and lacks important behavioral context.

    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, efficient sentence that states exactly what the tool does without any wasted words. It's appropriately sized for a simple listing operation and gets straight to the point with no unnecessary elaboration.

    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?

    For a tool with no annotations, no output schema, and a simple purpose, the description is insufficient. It doesn't explain what 'available conversations' means, what format they're returned in, whether there are any filters or limitations, or what the response structure looks like. While the tool is simple, the description leaves too many unanswered questions about its behavior and output.

    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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description doesn't need to compensate for any parameter gaps, and it appropriately doesn't mention parameters since there are none. This meets the baseline expectation for a parameterless tool.

    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 ('Get') and resource ('list of available conversations in Claude Desktop'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling 'ask' tool, which appears to be a different type of operation rather than a direct alternative for listing conversations.

    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 or in what context it should be invoked. While the sibling 'ask' tool seems functionally different (likely for querying rather than listing), the description doesn't mention this distinction or provide any usage context.

    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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