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believe3344

multimodal-mcp

by believe3344
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one converts images to text, the other checks environment configuration. No overlap or ambiguity.

    Naming Consistency3/5

    Naming pattern is mixed: 'describe_image' follows a verb_noun convention, while 'multimodal_config_status' uses a noun phrase prefix. Though both are descriptive, the lack of a consistent pattern slightly reduces coherence.

    Tool Count3/5

    With only 2 tools, the surface feels thin for a server named 'multimodal-mcp'. A typical well-scoped server has 3-15 tools, so this is borderline.

    Completeness1/5

    The server claims to be multimodal but only supports image description. Missing tools for other modalities like audio, video, or additional image operations. The surface is severely incomplete for its stated purpose.

  • Average 4.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 38 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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?

    Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is clear. The description adds context about clipboard reading, failure behavior, and the tool's limited role (no reasoning). No contradictions.

    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?

    Structured with sections, bullet points, and examples. Front-loaded with core purpose. Could be slightly more concise (e.g., Args section somewhat repeats schema), but overall well-organized and informative.

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

    Completeness5/5

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

    Covers all necessary aspects: when to use, parameter handling, clipboard trick, failure mode, output format (Markdown). Output schema exists, so return value explanation is sufficient. No gaps given complexity and sibling context.

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

    Parameters5/5

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

    Schema coverage is 100%, but description enriches each parameter with auto-detection rules, clipboard fallback for 'image', custom instruction purpose, and detail level differences. Provides examples for usage.

    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?

    Clearly states the tool converts an image into structured text for text-only models. Distinguishes from sibling 'multimodal_config_status' by focusing on image description.

    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?

    Provides explicit when-to-call scenarios (image attachments, screenshots, placeholders, OCR needs) and when-not-to-call (no image, already have description). Also explains the tool does not answer questions, leaving reasoning to the main model.

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

  • Behavior5/5

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

    Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds critical safety context: 'never the values' and 'API key itself is never exposed; only a boolean.' Also specifies exact return fields.

    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?

    Four sentences, each earning its place: purpose, usage, safety, and return format. Front-loaded with main action. No superfluous text.

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

    Completeness5/5

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

    For a parameterless, read-only, idempotent tool with annotations covering safety, the description fully covers usage context, return format, and security. No gaps remain.

    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?

    No parameters exist, so schema coverage is 100%. Description adds no parameter info (unnecessary), meeting 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.

    Purpose5/5

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

    Description clearly states 'Report whether the required vision env vars are set' with a specific verb ('report') and resource ('config status'). Differentiates from sibling tool 'describe_image' by focusing on configuration, not image analysis.

    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?

    Explicitly says 'Call once after first wiring the server into a client' to confirm configuration, providing clear context for when to use. Does not mention exclusions or alternatives, but the single intended use case is well-defined.

    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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