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jkawamoto

Florence-2 MCP Server

by jkawamoto
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation5/5

    The caption and ocr tools have clearly distinct purposes: one generates image captions, the other extracts text via OCR. No overlap or ambiguity.

    Naming Consistency5/5

    Both tool names are single lowercase verbs (caption, ocr), following a consistent and predictable pattern.

    Tool Count2/5

    Only 2 tools for a server named after Florence-2, a multimodal model capable of many tasks (e.g., object detection, segmentation). This feels under-scoped and insufficient.

    Completeness2/5

    Obvious gaps exist: Florence-2 supports object detection, phrase grounding, region captioning, etc. The server only covers captioning and OCR, missing major capabilities.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 24 commits in the last 12 weeks
    • Last stable release on
    • 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 provided. The description does not disclose side effects, required permissions, rate limits, or any behavioral traits beyond the basic action. It is too minimal for a tool without annotations.

    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 sentence, front-loaded, with no unnecessary words. It efficiently conveys the core purpose.

    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 and presence of an output schema (not detailed here), the description covers the essential input and action. It could mention output format, but overall adequate for a straightforward OCR 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?

    Schema description coverage is 100% for the lone parameter, which already describes 'src' as a file path or URL. The tool description adds no extra meaning, so baseline 3 is appropriate.

    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 clearly states the action (OCR) and the resource (image file or URL) and the output (extract text). It distinguishes from sibling tool 'caption' which likely does a different task.

    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?

    No guidance on when to use this tool vs alternatives. The description does not mention prerequisites, when not to use, or compare with sibling 'caption'.

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

  • Behavior3/5

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

    Description discloses basic behavior (process image, generate caption) but lacks details on performance, file size limits, or output format. With no annotations, it carries full burden and does adequately for a simple tool.

    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?

    Single, clear sentence with no redundancy. Every word contributes meaning, and it is front-loaded with the core function.

    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, output schema exists), the description is complete enough for an agent to understand purpose. Slightly more context on output format would elevate to 5.

    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 covers parameter 'src' with description 'A file path or URL to the image file'. The tool description adds no additional meaning beyond what the schema provides. Schema coverage is 100%, so baseline 3 is appropriate.

    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 uses a specific verb 'Processes' with a clear resource 'image file' and outcome 'generates captions'. It effectively distinguishes from the sibling tool 'ocr' which likely extracts text.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No explicit guidance on when to use this tool vs alternatives. Usage is implied from context (generating captions vs OCR), but no exclusions or prerequisites are stated.

    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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