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generate_gamma

Turn your text into AI-powered Gamma presentations, documents, or social posts. Customize format, theme, card count, text, images, and sharing options.

Instructions

Generate a Gamma presentation, document, or social media post using AI. Requires GAMMA_API_KEY environment variable to be set. The inputText parameter is required and should contain the content you want in your slides. Supports various customization options including format, theme, number of cards, text options, image options, and more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoThe output format typepresentation
exportAsNoExport the generated content as PDF or PPTX
numCardsNoNumber of cards/slides to generate (1-60 for Pro, 1-75 for Ultra)
textModeNoHow to process the input text. 'generate' creates new content from a prompt, 'condense' summarizes the input, 'preserve' keeps the input text mostly as-is.generate
cardSplitNoHow to split content into cards. 'auto' lets AI decide, 'inputTextBreaks' uses line breaks in input.auto
inputTextYesThe text content to generate from (1-100,000 tokens / ~1-400,000 characters). Can be a short prompt, messy notes, or polished content.
themeNameNoName of a specific theme to use. ONLY use if the user explicitly requests a custom theme by name. Theme must exist in your Gamma workspace. Omit this parameter to use Gamma's default theme selection.
cardOptionsNoCard layout options
textOptionsNoOptions for text generation
imageOptionsNoOptions for image generation/sourcing
sharingOptionsNoSharing and access options
additionalInstructionsNoAdditional instructions to guide content and layout (1-500 characters)
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses the API key requirement and mandatory inputText, which are useful behavior expectations. However, it does not describe what the tool returns (likely a generation ID or resource), whether it is asynchronous, error conditions, or any side effects. This is a generation tool with no output schema, so some return-value or workflow context is missing.

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 three sentences long and fairly efficient. It front-loads the purpose and then adds the API key requirement and inputText note. The final sentence listing customization options is somewhat redundant with the schema but still provides a helpful high-level summary. It is not overly verbose and earns its place, though it could be slightly tighter.

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 tool has high complexity (12 parameters, nested objects, no output schema), and the description gives a useful overview but leaves gaps: it doesn't explain what the generated output looks like or how to retrieve it, which is especially relevant given the sibling 'get_gamma_generation'. The rich schema compensates for parameter-level detail, but for a generation tool, the missing workflow context (e.g., returns an ID, asynchronous) makes it incomplete.

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 input schema has 100% description coverage, providing detailed explanations for every parameter. The description's mention of 'format, theme, number of cards, text options, image options' merely restates what the schema already documents, adding no new meaning or constraints. The baseline of 3 applies because the schema does the heavy lifting, and the description doesn't supplement it.

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 states a specific verb ('Generate') and resource ('Gamma presentation, document, or social media post'), clearly distinguishing it from the sibling tool 'get_gamma_generation'. It also indicates the AI-driven generation nature, leaving no ambiguity about the tool's core function.

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 gives clear context: it requires the GAMMA_API_KEY environment variable and the inputText parameter is required with expected content. It implies this tool is for creating new content, while the sibling 'get_gamma_generation' likely retrieves existing content, but it never explicitly says 'use this when you want to create, use get_gamma_generation to retrieve'. This is clear context without explicit exclusions or alternative naming.

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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