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Ubadream

AGY Visual Witness MCP

by Ubadream
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one analyzes images via Gemini, the other checks runtime presence and identity evidence. There is no overlap or ambiguity between them.

    Naming Consistency2/5

    The naming conventions are inconsistent: 'describe_image_gemini' follows a verb_noun_model pattern, while 'agy_visual_doctor' uses a product-prefixed noun phrase with no verb. This makes the set feel disjointed.

    Tool Count4/5

    With only two tools, the server is minimal but still coherent with its stated 'Visual Witness' purpose. It feels slightly thin but not unreasonable for a specialized utility.

    Completeness3/5

    The tools cover image description and runtime diagnostics, but the 'Visual Witness' domain might benefit from additional operations like image comparison or evidence storage. There is no obvious dead end, but the surface is narrow.

  • 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
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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 present, so the description carries the full burden of disclosing behavior. It mentions 'non-secret' to indicate that secrets are not exposed, but it does not explain side effects, execution context, dependencies, or what evidence is returned, leaving significant behavioral ambiguity.

    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, concise sentence that leads with the verb 'Report' and packs the core purpose without unnecessary words. It is appropriately sized for a zero-parameter tool.

    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?

    With no annotations and no output schema, the description needs to explain what the tool returns or how it behaves. It only says 'evidence' is reported, but not the format, structure, or examples of that evidence, leaving the context incomplete for an AI agent.

    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 zero parameters and the schema description coverage is 100%, so no parameter explanation is needed. The baseline score of 4 applies because there are no parameters to document.

    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 uses the verb 'Report' and names the subject 'agy/npx runtime presence and non-secret identity evidence', which gives a general sense of a diagnostic check. However, the phrase 'non-secret identity evidence' is vague and does not clearly define the exact resource or outcome, making the purpose only partially clear.

    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?

    There is no guidance on when to use this tool versus the sibling tool describe_image_gemini. The description only states what it does, not under what circumstances it should be chosen or excluded.

    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, the description must fully disclose behavior, but it only adds one non-deterministic output trait. It does not mention read-only nature, external API calls, permissions, rate limits, or output format. The 'never a deterministic acceptance verdict' is useful but minimal.

    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 one focused sentence, front-loaded with the core purpose and a key limitation. Every word earns its place; no fluff or repetition.

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

    Completeness1/5

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

    For a tool with 7 parameters, no output schema, and no annotations, the description is severely incomplete. It does not explain what 'visual evidence' looks like, how parameters influence behavior, or any operational details. Clearly inadequate for safe and correct invocation.

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

    Parameters1/5

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

    Schema description coverage is 0% and the description provides no explanation for any of the 7 parameters (images, model, roots, prompt, dry_run, operation, timeout_ms). The description completely fails to compensate for the lack of param documentation.

    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 returns QA-only visual evidence and explicitly denies deterministic acceptance verdicts, giving a specific verb and resource. However, it does not explicitly differentiate from sibling tool agy_visual_doctor, though the QA-only scope helps distinguish it from a verification tool.

    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 clear usage context: use for QA visual evidence, and a when-not: never for deterministic acceptance. It implies alternatives for acceptance checks but does not name any tool directly. This is clear context without explicit alternative listing.

    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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