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eternnoir

AI Studio MCP Server

by eternnoir

generate_content

Generate content with Gemini AI using text prompts, file uploads, Google search, and code execution to create documents, analyze media, and automate tasks.

Instructions

Generate content using Gemini with optional file inputs, code execution, and Google search. Supports multiple files: images (JPG, PNG, GIF, WebP, SVG, BMP, TIFF), video (MP4, AVI, MOV, WebM, FLV, MPG, WMV), audio (MP3, WAV, AIFF, AAC, OGG, FLAC), documents (PDF), and text files (TXT, MD, JSON, XML, CSV, HTML). MIME type is auto-detected from file extension.

Example usage:

{
  "user_prompt": "Analyze this video",
  "files": [
    {
      "path": "/path/to/video.mp4"
    }
  ]
}

PDF to Markdown conversion:

{
  "user_prompt": "Convert this PDF to well-formatted Markdown, preserving structure and formatting",
  "files": [
    {"path": "/document.pdf"}
  ]
}

With Google Search:

{
  "user_prompt": "What are the latest AI breakthroughs in 2024?",
  "enable_google_search": true
}

With Code Execution:

{
  "user_prompt": "Write and run a Python script to calculate prime numbers up to 100",
  "enable_code_execution": true
}

Combining features with thinking mode:

{
  "user_prompt": "Research quantum computing and create a Python simulation",
  "model": "gemini-2.5-pro",
  "enable_google_search": true,
  "enable_code_execution": true,
  "thinking_budget": -1
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_promptYesUser prompt for generation
system_promptNoSystem prompt to guide the AI behavior (optional)
filesNoArray of files to include in generation (optional). Supports images, video, audio, PDFs, and text files.
modelNoGemini model to use (optional)gemini-2.5-flash
temperatureNoTemperature for generation (0-2, default 0.2)
enable_code_executionNoEnable code execution capability for the model
enable_google_searchNoEnable Google search capability for the model
thinking_budgetNoThinking budget for models that support thinking mode (-1 for unlimited)
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 of behavioral disclosure. It does well by detailing capabilities (file support, code execution, Google search), file type specifics, and example workflows. It mentions auto-detection of MIME types and thinking budget usage. However, it omits critical behavioral traits like rate limits, authentication needs, or potential costs, which are important for a generative AI tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear purpose statement, but it becomes lengthy due to multiple detailed examples. While examples are helpful, they occupy most of the text, making it less concise. Some sentences (like the file type list) could be more streamlined. It's informative but could be more efficiently structured.

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 the tool's complexity (8 parameters, no output schema, no annotations), the description is moderately complete. It covers purpose, usage examples, and some behavioral context, but lacks details on output format, error handling, or limitations. Without annotations or output schema, more completeness is needed for a generative tool with multiple features.

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 already documents all 8 parameters thoroughly. The description adds minimal parameter semantics beyond the schema—it mentions file type support and provides usage examples that illustrate parameter combinations. This meets the baseline of 3, as the schema does the heavy lifting, but the description doesn't significantly enhance understanding of parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate content using Gemini with optional file inputs, code execution, and Google search.' It specifies the verb ('generate content') and resource ('using Gemini'), and lists key capabilities. However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score.

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 provides clear context for usage through multiple examples showing different scenarios (e.g., video analysis, PDF conversion, Google search, code execution). It implicitly guides when to use features like enable_google_search or enable_code_execution. However, it lacks explicit when-not-to-use guidance or comparisons to alternatives, as no sibling tools exist.

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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