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TINKPA

MCP-MinerU

by TINKPA
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    parse_pdf and list_backends have completely distinct purposes: one performs the actual document/image parsing, while the other reports system capabilities. No overlap or ambiguity exists between them.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern in snake_case (parse_pdf, list_backends). The naming is predictable and uniform across the set.

    Tool Count3/5

    With only two tools, the server feels minimal for its stated purpose of document and image parsing. While the scope is narrow, a slightly more complete set (e.g., separate format detection or result retrieval) might be expected in a fuller-featured server.

    Completeness5/5

    For the domain of document parsing, parse_pdf covers the core extraction of text, tables, formulas, and structure across multiple file types, while list_backends addresses capability discovery. There are no obvious dead ends or missing critical operations within this focused scope.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 Apache 2.0.

  • This repository includes a README.md file.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 carries full responsibility. It merely states the function without disclosing behavioral details such as what 'system capabilities' includes, whether the operation is read-only, the return format, or potential error conditions.

    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, front-loaded sentence with no redundancy or filler. Every word contributes to conveying the tool's purpose.

    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?

    For a tool with no output schema and no annotations, the description is incomplete. It does not explain the returned data format, how to interpret 'recommended backends,' or provide context for using the sibling parse_pdf, leaving the agent with insufficient guidance for effective invocation.

    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, so the schema trivially covers everything. The description adds no parameter-specific semantics, but none are needed; baseline 4 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 tool's function with specific verbs 'check' and 'list' and identifies the resource (system capabilities, recommended backends). It effectively distinguishes from the sibling parse_pdf, which handles actual parsing rather than capability listing.

    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 is given on when to use this tool versus parse_pdf or alternatives. The description implies checking capabilities before parsing, but it lacks explicit context, exclusions, or alternative recommendations.

    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?

    With no annotations provided, the description carries the full burden for behavioral disclosure. It mentions supported formats and backend behaviors (e.g., MLX acceleration on Apple Silicon) but does not disclose output format, error handling, or any potential side effects. This is a moderate level of transparency—better than nothing but not comprehensive.

    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 concise and front-loaded: two sentences quickly state what the tool does, then add context about backends and use cases. There is no redundant or filler content. The minor oddity of 'vunknown' does not affect conciseness.

    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 has six parameters and no output schema, the description covers the core functionality, supported input types, and backend options, which is largely sufficient for an agent to understand the tool's role. However, it does not explain return structure or potential limitations, leaving small gaps. Still, it's fairly complete for a parsing 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%, so all parameters are already fully documented in the input schema. The description adds no new semantic details beyond repeating that PDF/JPEG/PNG files are supported and that tables/formulas are extracted, which are already present in the schema property descriptions. Baseline of 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 tool parses PDF and image files to extract text, tables, formulas, and structure. The specific verb 'Parse' and the listed resource types distinguish it from the sibling tool list_backends, making its purpose unambiguous.

    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 context on when to use this tool—for documents, screenshots, photos, and scanned images—and notes backend options including Apple Silicon acceleration. However, it does not explicitly mention exclusions or compare against list_backends, so it's not a 5, but it offers sufficient guidance.

    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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