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swairshah

InputMCP

by swairshah
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    A single tool inherently has consistent naming, as there are no other tools to compare it against. The name 'collect_input' follows a clear verb_noun pattern.

    Tool Count2/5

    One tool is too few for most server purposes, as it severely limits functionality and scope. It feels thin and incomplete for handling user input in a robust way.

    Completeness2/5

    The server's purpose appears to be collecting user input, but with only one tool, there are significant gaps. For example, no tools for validating, processing, or managing different input types, making the surface severely incomplete.

  • Average 2.5/5 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

  • 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 must fully disclose behavioral traits. It mentions 'get input from user' which implies interactive user prompting, but doesn't specify if this blocks execution, requires user authentication, has rate limits, or what happens on cancellation. The description is minimal and leaves key behavioral aspects undefined.

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

    Conciseness3/5

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

    The description is two sentences, but the second sentence is redundant ('This is used to get contextual input from the user of different kinds') and adds no value. It could be more front-loaded and eliminate waste. However, it's not overly verbose, just inefficient.

    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, no output schema, and 7 parameters with moderate complexity (including enums and defaults), the description is incomplete. It doesn't explain the return values, error conditions, or how parameters interact (e.g., 'gridWidth' only relevant for 'pixelart'). For a user-input tool with multiple modes, more context is 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?

    Schema description coverage is high at 86%, so the baseline is 3. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'kind' affects other parameters or the interaction flow). It mentions 'different kinds' which loosely relates to the 'kind' enum but provides no additional semantic context.

    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 'get image, text, or pixel art input from user' which specifies the verb ('get') and resources ('image, text, or pixel art input'), but it's vague about the mechanism (e.g., UI prompt, file upload). The second sentence 'This is used to get contextual input from the user of different kinds' is redundant and adds no clarity. It doesn't distinguish from siblings, but none exist.

    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 constraints. It merely restates the purpose without indicating appropriate contexts or exclusions. Since there are no sibling tools, this is less critical, but still lacks any usage direction.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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