Skip to main content
Glama
automatikstudio

PitchDeck MCP Server

by automatikstudio
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    The single tool name 'generate_pitch_deck' follows a clear verb_noun pattern, and with no other tools, consistency is inherently perfect. There are no deviations or mixed conventions to evaluate.

    Tool Count2/5

    A single tool is too few for a server named 'PitchDeck MCP Server', which suggests a broader domain like pitch deck creation and management. This minimal toolset feels thin and limits functionality, as it only covers generation without supporting operations like editing, listing, or deleting pitch decks.

    Completeness2/5

    The tool surface is severely incomplete for the implied domain of pitch deck management. While generation is covered, there are significant gaps such as updating existing decks, retrieving or listing decks, deleting decks, or handling slide-level operations, which will likely cause agent failures in broader workflows.

  • Average 3.1/5 across 1 of 1 tools scored.

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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It mentions the tool 'returns a complete slide deck' but lacks critical behavioral details: whether this is a generative AI operation (implied by 'AI-powered'), potential rate limits, quality expectations, format of the return (e.g., PDF, presentation file), or any authentication requirements. The description is insufficient for a tool with no annotation coverage.

    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 appropriately concise with two sentences that efficiently convey the core functionality and output. It's front-loaded with the main action and avoids unnecessary details. Every sentence earns its place, though minor improvements in structure are possible.

    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?

    Given the tool's complexity (generative output with 9 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return format, quality, limitations, or how parameters map to the output. For a tool with no structured behavioral data, this leaves significant gaps for an AI agent.

    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 the schema fully documents all 9 parameters. The description adds no parameter-specific information beyond implying the inputs feed into the slide deck generation. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.

    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 with specific verbs ('generate', 'returns') and resources ('professional AI-powered pitch deck', 'complete slide deck'). It distinguishes what it produces (a full deck with specific slide types) without restating the tool name. No sibling tools exist, so differentiation isn't needed.

    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?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites, or constraints. It simply states what the tool does without context about appropriate scenarios or limitations. No sibling tools exist, but general usage context is missing.

    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

pitchdeck-mcp MCP server

Copy to your README.md:

Score Badge

pitchdeck-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/automatikstudio/pitchdeck-mcp'

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