s3-md-pdf-converter-mcp
Server Quality Checklist
Latest release: v1.2.3
- Disambiguation4/5
The three tools differ by input source: file, S3 object, and content string. However, the generic 'convert_markdown_to_pdf' could be misread as covering S3 or content, though the explicit S3 and content tools reduce ambiguity.
Naming Consistency3/5The first two tools follow a convert_X_to_pdf pattern, but the third breaks convention by starting with 'markdown_content' instead of a verb. This mixed style is readable but not fully consistent.
Tool Count5/5With three input modes (file, S3, content), each tool serves a distinct purpose and justifies its existence. This is an appropriate scope for a specialized markdown-to-PDF converter.
Completeness4/5The server covers the core conversion needs for local files, S3 objects, and raw content. Minor gaps like URL-based conversion or batch processing are absent but not critical for the core purpose.
Average 3/5 across 3 of 3 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 MIT License.
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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of transparency. It only states the conversion action without disclosing key behavioral traits such as whether existing output files are overwritten, whether input can be a URL, or what happens on conversion failure. No additional context is provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, but it simply restates the tool name and title without adding meaningful information. It is under-specified rather than concise; the sentence does not earn its place because it provides no additional value over the already-known title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a nested margin object, multiple output formats, and no output schema, the description is inadequate. It omits context about return values, side effects, or how the conversion handles edge cases, leaving the agent without practical guidance beyond the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides full descriptions for all parameters (100% coverage), including nested margins and enum options. The description adds no parameter-specific details, so it does not improve upon the schema; it neither compensates for gaps nor introduces ambiguity, warranting the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states the specific verb 'Convert' and the resource 'markdown file' to PDF, clearly indicating the primary function. However, it does not differentiate this tool from its siblings 'convert_s3_markdown_to_pdf' or 'markdown_content_to_pdf', which have distinct input sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the alternatives. It lacks any mention of prerequisites, exclusions, or specific scenarios where this local-path/URL-based converter is preferred over S3 or content-based converters.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not state whether existing files are overwritten, what permissions are required, or what the function returns. As a tool that writes a PDF file, this lack of side-effect disclosure is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the action, and contains no wasteful language. It is appropriately concise, though it borders on being too minimal to provide meaningful value beyond the title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a nested object parameter (markdownContent) and multiple optional settings (title, format, margin), but the description does not address return values, usage examples, or limitations. For a tool with this complexity, the description is incomplete and insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter documented in the input schema. The description itself adds no additional parameter semantics, such as relationships or special constraints, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and resource 'markdown content to PDF', making the core function obvious. However, it does not explicitly differentiate from sibling tools like convert_markdown_to_pdf or convert_s3_markdown_to_pdf beyond the word 'directly', which is implied rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 alternatives. The description does not mention convert_s3_markdown_to_pdf or any conditions that would favor one tool over another. Users are left to infer usage from the tool name and title.
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?
No annotations are provided, and the description only states the core conversion action without disclosing any side effects (e.g., whether the file is uploaded back to S3, credential requirements, or output handling). It fails to describe behavior beyond the literal conversion, leaving the agent without information about potential permissions or network dependencies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the action and resource. It contains no filler or redundant explanation, making it highly scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has seven parameters, nested objects, and no output schema, the description is sparse. It doesn't mention the uploadToS3 behavior, default region, or any distinctions from sibling tools, which are important for correct selection and invocation. The schema covers parameters, but the description lacks contextual depth beyond the basic conversion.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with all 7 parameters described, so the baseline is 3. The description adds no extra parameter information beyond mentioning bucket and key, which is already in the schema. It does not clarify the format/margin parameters or outputPath semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Convert' with clear source ('markdown file from S3 bucket') and target (PDF), and explicitly mentions bucket and key parameters, distinguishing it from sibling tools like convert_markdown_to_pdf which likely handle non-S3 sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is for S3-hosted markdown files, but it does not explicitly state when to use it over sibling tools like markdown_content_to_pdf or convert_markdown_to_pdf, nor does it provide exclusions or prerequisites. This is only implied usage, not clear 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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