PPT Generator MCP

by SUSTechWLA

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a distinct role in the PPT generation pipeline: template discovery, loading, placeholder filling, image generation, icon rendering, page assembly, validation, and source parsing. No overlaps exist.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_templates, fill_placeholders). The convention is uniform and predictable.
Tool Count5/5
With 8 tools covering the entire workflow from source parsing to final validation, the count is well-scoped for a specialized PPT generation server. No tool feels redundant or missing.
Completeness5/5
The tool set provides a complete pipeline: parse source content → list/load template → fill placeholders → generate images → render icons → assemble page → validate. All necessary steps for generating a presentation are covered.
Average 3.9/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 106 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
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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 provided, so description carries full burden. Lists actions but omits failure modes (e.g., what happens if placeholders remain), permissions needed, or side effects. For a mutation tool with no annotations, this is insufficient.
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 purpose, no filler. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks output schema and does not describe return value or error handling. Given complexity of config and no annotations, more detail would be needed for an AI agent to use correctly.
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 baseline is 3. Description adds context about the overall assembly process but does not enhance parameter understanding beyond schema. No contradictions.
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?
Description clearly states it assembles final HTML page, lists specific actions (remove XML comments, check placeholders, inline CSS, minify, write file), and distinguishes from siblings as the final output step.
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?
Explicitly states it should be called after all fill/render steps, providing clear usage context. Could improve by specifying when not to use or alternative tools, but sibling context helps.
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?
No annotations provided, so the description must fully disclose behavior. It describes a read operation (listing) with no mention of side effects, permissions, or rate limits. While adequate, it lacks explicit read-only confirmation.
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 with a follow-up note, front-loading the core action. No wasted words; every part serves a 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?
For a simple listing tool with one optional parameter, the description is largely complete. It mentions the output (metadata fields) but could further specify the exact structure or field types, especially given no output schema.
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?
The schema covers the single parameter (templatesDir) with 100% coverage, so the description adds no additional meaning. Baseline 3 is appropriate as the schema suffices.
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 action (list templates) and the resource (templates/ directory). It specifies the return data (metadata including name, applicable scenario, format, components) and distinguishes from siblings by focusing on discovery rather than loading or processing templates.
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 implies usage for orchestration layer template selection but does not explicitly state when not to use it or compare with alternatives like load_template. Guidance is present but not comprehensive.
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, the description carries the burden of disclosing behavior. It states 'pure local operation, no API calls,' which is helpful, but it omits details like error handling for missing icons or whether the original HTML is mutated.
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 two sentences with no superfluous words. It front-loads the core action and adds a behavioral note efficiently.
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 complete parameter schema, the description covers the essential transformation and safety aspect. However, it lacks explanation of the return value format, which would be beneficial.
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 coverage is 100% with individual parameter descriptions, so the tool description does not need to add parameter details. It provides context about the transformation but does not enhance meaning beyond the schema.
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 action: replacing <icon> tags with <img> tags using a specific verb and resource. It distinguishes from sibling tools like list_templates and generate_image by focusing on a localized icon substitution step.
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 mentions it is a local operation with no API calls, implying safe usage, but does not provide explicit guidance on when to use it versus alternatives like fill_placeholders. No exclusions or context for complex scenarios are given.
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?
No annotations provided, so description carries full burden. It discloses the two modes and that expand uses LLM API, but lacks detail on side effects, error handling, rate limits, or state modification. Adequate 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?
Two efficient sentences covering purpose, modes, and context. No redundancy or irrelevant details; each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given nested objects, two modes, no output schema, and no annotations, the description explains modes and that expand requires LLM config but omits output format, error conditions, and prerequisites. Adequate but incomplete for a tool with this complexity.
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 has 100% coverage with parameter descriptions. Description adds a concise summary of modes but does not significantly extend meaning beyond the schema. 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?
Description clearly states the tool fills XML placeholders in templates, explicitly names two modes (direct and expand), and positions itself as the core orchestration tool for LLM text generation, differentiating it from siblings like generate_image.
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?
Mentions two modes and that expand calls LLM API, providing some context for use. However, it does not explicitly state when to choose direct vs expand, nor when to avoid this tool (e.g., for image generation). Guidance is implied but not fully explicit.
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, the description carries the full burden. It discloses the automatic replacement of <figures> with <img> in HTML. However, it omits details like rate limits, cost implications, error handling, and the fact that images are saved to the output directory (implied by required param but not stated). This leaves some behavioral gaps.
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 two clear sentences with no redundancy. It immediately states the core purpose, then explains the two modes and side effect. Every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the core functionality but lacks some completeness. It does not specify what the tool returns (e.g., updated HTML or success message) despite no output schema. It also does not mention the file saving behavior or error conditions. For a tool with 5 params and a nested object, more detail would be beneficial.
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 coverage is 100%, so the baseline is 3. The description adds workflow context (HTML extraction vs direct prompt) that is not in individual parameter descriptions, but it does not provide deeper constraints or examples. Value added is moderate given the schema already documents each parameter.
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 calls DALL-E API to generate images, with two distinct modes (HTML with <figures> extraction or direct prompt). It is specific about the verb (generate), resource (images), and differentiates from sibling tools by focusing on image generation rather than template processing.
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 explains two usage modes (HTML-based or direct prompt), providing clear context. However, it does not explicitly state when to choose one over the other, nor does it mention prerequisites like API key availability. Still, it offers sufficient guidance for an AI agent given the sibling tool set.
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?
无注解，描述承担全部责任。解释了两种模式及llm模式配置要求，并列出输出项，但未说明错误处理、输入限制或副作用，行为透明度中等。
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?
两句话简洁明了，信息密度适中，但第二句话罗列输出项稍显密集，结构尚可。
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?
在无输出schema的情况下，说明了返回的结构化对象、图片提示词和推荐模板slug，并指明在编排中的角色（传递给fill_placeholders），对4参数工具较完整，但缺少错误处理描述。
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覆盖率100%，描述基本复述schema内容，对参数含义无显著补充（如sourceText仅重述‘原始Markdown或纯文本’），但额外说明templateSlug留空则自动推荐，略有增值。
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?
明确说明工具用于将原始Markdown/正文解析为结构化content对象，支持两种模式，并指出与fill_placeholders的衔接以及返回图片提示词和推荐模板slug，与兄弟工具区分度高。
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?
说明了调用时机（在fill_placeholders之前）和返回内容如何传递，但未明确说明不适合使用的情况或替代方案，仅提供基本使用导向。
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?
With no annotations provided, the description carries the full burden. It discloses that it loads full HTML, returns placeholders and icon references, and is used by the orchestration layer. However, it does not mention error handling, prerequisites, or potential side effects. Still, it provides adequate transparency for a read-only loading 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?
The description is two short sentences, front-loaded with the core action, and every sentence adds value. No unnecessary words or redundancy.
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 lack of output schema, the description explains the return values (placeholders and icon references) and provides context about the orchestration layer's use. It is mostly complete, though could mention the return structure (e.g., list format).
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 coverage is 100% with detailed descriptions for both parameters. The tool description does not add new meaning beyond what the schema already provides for parameters, meeting the baseline of 3.
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 that the tool loads the complete HTML content of a specified template, parses placeholders and icon references, and returns them. It distinguishes from siblings like list_templates and fill_placeholders by specifying its specific role in the orchestration pipeline.
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 explains what the tool does and its place in the orchestration, but does not provide explicit guidance on when to use it versus alternatives like list_templates or parse_source_content. Usage context is implied but not explicit.
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?
No annotations provided, so description carries full burden. It discloses the checks performed but does not mention return format, error handling, or if the tool has side effects. As a validation tool, it is likely read-only, but this is not stated.
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 concise sentences: first lists all checks, second states invocation context. No redundancy, front-loaded with specific actions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Describes inputs and usage context well, but lacks information about output (e.g., does it return a boolean, error messages, or details?) and lacks behavioral transparency (no annotations). For a validation tool, return value is important for agents to act on results.
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?
Schema coverage is 100%. Description adds meaning to the 'checks' parameter by mapping each enum value to a natural language check (e.g., 'no-xml-tags' corresponds to '检查是否残留 XML 占位符'). This goes beyond the schema which only lists enum literals.
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?
Description clearly states the tool validates generated pages by checking for XML placeholders, icon rendering, image references, and HTML validity. It distinguishes from sibling tools (e.g., assemble_page) by being the final quality check before delivery.
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?
Description specifies 'before delivery' as the usage context. It implicitly excludes earlier stages by stating it's a final check, but does not explicitly list when not to use or alternatives.
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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