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chameleonscool

User Intent MCP

by chameleonscool
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    There is only one tool, so there is no possibility of confusion between tools. The tool's purpose is completely unambiguous.

    Naming Consistency5/5

    The tool name 'collect_user_intent' follows a clear verb_noun pattern and is descriptive. With only one tool, there is no inconsistency to evaluate.

    Tool Count3/5

    The server has a single tool, which feels thin compared to the typical 3-15 range. However, the tool is well-scoped for the server's narrow purpose of collecting user intent, so the count is borderline but not problematic.

    Completeness5/5

    For the stated purpose of collecting user intent, the tool covers the full interaction loop: sending a message, displaying it, and collecting a response with optional images. There are no obvious gaps within this narrow domain.

  • Average 4.2/5 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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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

  • Behavior4/5

    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 clearly states that the tool displays content in a web interface, waits for user input, and allows text and optional image attachments, which provides meaningful insight into its interaction model.

    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 well-structured with a clear opening sentence, an important warning in bold, and a bulleted list of use cases. Every sentence provides relevant information without unnecessary verbosity.

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

    Completeness4/5

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

    Given the tool's simplicity (one parameter and an output schema), the description adequately covers its purpose, behavior, and usage scenarios. It doesn't detail edge cases like timeouts or error handling, but these are not essential for this straightforward interaction tool.

    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 already provides 100% coverage for the single 'question' parameter, so the description adds little beyond reinforcing that only this parameter is accepted. The warning against 'intent', 'message', or 'prompt' is redundant given additionalProperties:false but does reinforce the constraint.

    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 opens with 'Send a message to the user and collect their response,' which clearly states the tool's function. It further elaborates that it displays content in a web interface and waits for user input, making the purpose unambiguous and distinct from any sibling tools.

    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 provides explicit use cases such as requesting clarification, getting approval, collecting context, and showing progress. It also warns against passing incorrect parameters, though it doesn't mention when not to use the tool since there are no sibling alternatives.

    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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