Skip to main content
Glama
lichman0405

PDF2ZH MCP Server

by lichman0405
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v1.9.11

  • Disambiguation5/5

    Only two tools with clearly distinct purposes: one lists supported languages, the other translates PDFs. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: list_supported_languages and translate_pdf. No mixing of conventions.

    Tool Count3/5

    With only 2 tools, the server feels thin for a broader translation service, but it is scoped to a single core task. The count is borderline acceptable.

    Completeness4/5

    The core workflow (translating a PDF with language selection) is fully covered. Missing optional features like output format control, but no critical gaps for the stated purpose.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 AGPL 3.0.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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

  • Behavior4/5

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

    With no annotations, the description fully bears the transparency burden. It discloses that the tool preserves formulas and layout, uses an LLM controlled by environment variables, returns absolute paths, and produces two output files. It does not mention any destructive actions or limitations, but it is sufficiently transparent for a translation tool.

    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?

    The description is well-structured with an 'Args' and 'Returns' section, front-loading the main purpose. It contains several sentences but each earns its place by providing necessary context. Slight improvement could condense some details, but it is not overly verbose.

    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 4 parameters, no output schema (though description covers returns), and sibling tool presence, the description covers input handling, output paths, integration hints, and environment variables. It lacks explicit error handling or size limits, but overall it provides a complete picture for usage.

    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 input schema has 0% description coverage, so the description must add meaning. It does so by explaining path resolution logic (absolute vs relative), providing example language codes, and describing the output directory's default and purpose. This adds significant value beyond the schema's bare type definitions.

    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 purpose: 'Translate a PDF file while preserving formulas and layout.' This is a specific verb+resource combination that distinguishes it from the sibling tool 'list_supported_languages'.

    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?

    The description provides context about path resolution, environment variables, and output directory integration with other MCP tools, but it does not explicitly state when to use this tool versus alternatives or when not to use it. It mentions the sibling tool's existence only through context, not in guidelines.

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

  • Behavior4/5

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

    No annotations provided, but description accurately states it returns a table of codes. Simple read-only tool; no further behavioral traits needed.

    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?

    Two sentences, front-loaded with the main action, no extraneous words. Perfectly concise for a zero-parameter utility tool.

    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?

    Low complexity tool with no parameters and an output schema. Description fully explains purpose, usage context, and relation to sibling. No gaps.

    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 needed—schema is empty. Description adds value by explaining the codes' purpose (translation parameters), which is not apparent from schema alone.

    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 verb 'list' and resource 'language codes supported for translation'. Directly distinguishes from sibling translate_pdf by specifying that these codes are used as translation parameters.

    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?

    Explains that the returned codes can be used as lang_in or lang_out parameters, implying the tool should be called before translation. Lacks explicit when-not-to-use guidance but sufficient given one sibling.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

pdftranslate-mcp MCP server

Copy to your README.md:

Score Badge

pdftranslate-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/lichman0405/pdftranslate-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server