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Shared stack plan · /s/syRnouoe

Construye un MCP server en Python con las siguientes especificaciones…

“Construye un MCP server en Python con las siguientes especificaciones: Identidad y contexto Nombre del MCP: video-generator-mcp Empresa: EDIFICACIÓN (Salfacorp) Propósito: Transformar procedimientos técnicos (PDF o texto) en guiones JSON estructurados para generación de video corporativo de capacitación Stack técnico Lenguaje: Python Transport: stdio (para Claude Desktop) Imágenes: Gemini API (gemini-2.0-flash-preview-image-generation) — solicitar API key al usuario si no está configurada Renderer de video: Creatomate (API REST, JSON → video) Generación de guión: Anthropic API (claude-sonnet-4-20250514) Herramientas (tools) que debe exponer el MCP generate_video_script Input: procedure_text (string) — texto extraído del PDF o pegado directamente pdf_path (string, opcional) — ruta al PDF si se sube archivo Proceso interno: Extrae PROJECT_NAME desde el título o encabezado del procedimiento Fija COMPANY = "EDIFICACIÓN" Determina TEMPLATE desde el nombre del procedimiento (sin prefijo numérico) Analiza el contenido y determina cantidad de escenas dinámicas (libre, sin mínimo ni máximo fijo) Genera escenas: escena 1 (intro corporativa fija) + escenas dinámicas + escena final (cierre corporativo fijo) Para cada escena dinámica genera: voz_en_off, image_prompt, video_prompt Output: JSON estructurado según el formato definido abajo generate_scene_images Input: JSON del guión generado por generate_video_script Proceso: Para cada escena dinámica, llama a Gemini API con el image_prompt y retorna las rutas o base64 de las imágenes generadas Output: JSON del guión con campo imagen_generada poblado por escena render_video Input: JSON del guión con imágenes generadas Proceso: Mapea el JSON al template de Creatomate y llama a su API REST Output: URL del video renderizado”

Install with one command
$ npx mcpflix install syRnouoe

Writes claude_desktop_config.json, prompts for any required API keys, and drops skills into ~/.claude/skills/. Backed up automatically.

What this stack is

What you're building

  • Entities: Video-generator-mcp (MCP server, Python), Procedimientos técnicos (PDF/texto input), Guiones JSON estructurados (output), Gemini API (image generation), Creatomate API (video rendering), Anthropic API (script generation)
  • Constraints: Python + stdio transport for Claude Desktop, API keys for Gemini, Creatomate, Anthropic must be user-supplied or env-configured, Deterministic JSON schema for video scripts, Corporate training video format (EDIFICACIÓN/Salfacorp branding)
  • Out of scope: Web UI or frontend (MCP server only), Video hosting or CDN delivery, PDF parsing library selection (user will choose), Creatomate template design (user provides templates)

Why this stack fits anthropic-api provides claude-sonnet-4 for script generation from procedure text. No listed MCP server wraps Gemini or Creatomate, so the MCP itself will call those APIs directly. postgres or supabase are not needed; this is a stateless transformation pipeline.

Architecture

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MCP Servers

Anthropic API

mcp-anthropic

Official Anthropic MCP server; provides claude-sonnet-4-20250514 for structured script generation from procedure text.

Skills

Security Audit

/security-audit

Scan the MCP server code for hardcoded API keys, credential leaks, and OWASP issues before deployment.

Test Generator

/test-gen

Generate unit tests for the three MCP tools (generate_video_script, generate_scene_images, render_video) to verify API integration and JSON schema validation.

Install everything in one go

Copy a single setup guide that includes the MCP config and the skills installer script — paste into a doc to keep, or follow it section by section.

Implementation Plan

  1. Initialize Python MCP project structure ⏱ 15m

Create a new Python project with uv (or pip) and install mcp, anthropic, google-generativeai, requests, and pydantic. Set up a src/video_generator_mcp/ directory with init.py, server.py (main MCP server), tools.py (tool implementations), and schemas.py (Pydantic models for JSON validation). Initialize git and add .env.example for API keys.

Done when:

  • Project structure matches MCP Python template (server.py exports Server class)
  • pyproject.toml lists mcp, anthropic, google-generativeai, requests, pydantic as dependencies
  • .env.example documents ANTHROPIC_API_KEY, GEMINI_API_KEY, CREATOMATE_API_KEY

Files: pyproject.toml, src/video_generator_mcp/__init__.py, src/video_generator_mcp/server.py, src/video_generator_mcp/tools.py, src/video_generator_mcp/schemas.py, .env.example, .gitignore

Verify:

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  1. Define Pydantic schemas for video script JSON ⏱ 30m

In schemas.py, define Pydantic models for: SceneContent (voz_en_off, image_prompt, video_prompt), DynamicScene (extends SceneContent with scene_number, duration_seconds), FixedScene (intro/outro with static content), VideoScript (project_name, company, template, scenes list, metadata). Ensure JSON serialization matches Creatomate's expected format. Add validation for required fields and enum constraints (e.g., template names).

Done when:

  • Pydantic models validate and serialize to JSON without errors
  • VideoScript model includes intro scene (fixed), dynamic scenes list, outro scene (fixed)
  • Each DynamicScene has voz_en_off, image_prompt, video_prompt, scene_number, duration_seconds

Files: src/video_generator_mcp/schemas.py

Verify:

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  1. Implement generate_video_script tool ⏱ 60m · • medium-risk

In tools.py, create generate_video_script(procedure_text, pdf_path=None). Extract PROJECT_NAME from the first heading or title line. Set COMPANY='EDIFICACIÓN'. Determine TEMPLATE by removing numeric prefixes from the procedure name. Call Anthropic API (claude-sonnet-4) with a system prompt that instructs the model to: (1) analyze the procedure, (2) determine optimal number of dynamic scenes, (3) generate voz_en_off (Spanish narration), image_prompt (for Gemini), and video_prompt (for Creatomate) for each scene. Return a VideoScript Pydantic model, serialized to JSON.

Done when:

  • Tool accepts procedure_text and optional pdf_path
  • Extracts PROJECT_NAME and sets COMPANY='EDIFICACIÓN'
  • Calls Anthropic API and receives structured JSON response
  • Returns VideoScript JSON with intro, dynamic scenes, and outro

Files: src/video_generator_mcp/tools.py

Verify:

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  1. Implement generate_scene_images tool ⏱ 45m · • medium-risk

In tools.py, create generate_scene_images(video_script_json). Iterate over each dynamic scene in the script. For each scene, extract image_prompt and call Gemini API (gemini-2.0-flash-preview-image-generation) to generate an image. Store the returned image (base64 or URL) in a new field imagen_generada for each scene. Return the updated VideoScript JSON with all images populated. Handle API rate limits and errors gracefully.

Done when:

  • Tool iterates over dynamic scenes and calls Gemini API for each image_prompt
  • imagen_generada field is populated with base64 or image URL
  • Returns updated VideoScript JSON with all images
  • Handles Gemini API errors and rate limits

Files: src/video_generator_mcp/tools.py

Verify:

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  1. Implement render_video tool ⏱ 60m · ⚠ high-risk

In tools.py, create render_video(video_script_json). Map the VideoScript JSON to Creatomate's template schema (composition, layers, timeline). Call Creatomate API (POST /v1/renders) with the mapped JSON payload. Poll the render status endpoint until completion (or timeout after 5 minutes). Return the final video URL or error message. Document the expected Creatomate template structure in a README section.

Done when:

  • Tool maps VideoScript JSON to Creatomate composition format
  • Calls Creatomate API and receives job ID
  • Polls render status until completion or timeout
  • Returns video URL on success, error message on failure

Files: src/video_generator_mcp/tools.py, README.md

Verify:

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Rollback: Delete the render job via Creatomate API using the job ID returned from the initial POST request; document job IDs in logs for manual cleanup if needed.

  1. Register tools in MCP server and add stdio transport ⏱ 30m · • medium-risk

In server.py, instantiate the MCP Server class. Register the three tools (generate_video_script, generate_scene_images, render_video) with their input schemas. Set up stdio transport so the server can communicate with Claude Desktop via stdin/stdout. Add error handling and logging. Test locally with mcp-cli or by running the server and sending test JSON payloads.

Done when:

  • MCP Server class instantiated with three registered tools
  • Stdio transport configured for Claude Desktop
  • Tool input schemas match Pydantic models
  • Server starts without errors: python -m src.video_generator_mcp.server

Files: src/video_generator_mcp/server.py

Verify:

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  1. Configure Claude Desktop to load the MCP server ⏱ 15m · • medium-risk

Edit ~/.claude/claude_desktop_config.json to add a new entry under mcpServers for video-generator-mcp. Set the command to python -m src.video_generator_mcp.server (or the installed package path). Set environment variables for ANTHROPIC_API_KEY, GEMINI_API_KEY, and CREATOMATE_API_KEY. Restart Claude Desktop. Verify the server appears in the MCP list and tools are callable.

Done when:

  • claude_desktop_config.json contains mcpServers.video-generator-mcp entry
  • Command path is correct and server starts without errors
  • Environment variables are set for all three API keys
  • Claude Desktop shows the three tools in the MCP panel after restart

Files: ~/.claude/claude_desktop_config.json

Verify:

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  1. Test end-to-end workflow with sample procedure ⏱ 120m · ⚠ high-risk

Create a sample procedure text (e.g., a construction safety checklist or equipment maintenance guide in Spanish). Call generate_video_script with the sample text. Verify the returned JSON has correct structure, PROJECT_NAME, COMPANY, and dynamic scenes. Call generate_scene_images on the output. Verify images are generated or URLs are populated. Call render_video on the final script. Verify a video URL is returned. Document the test case in tests/ directory.

Done when:

  • Sample procedure text is processed without errors
  • generate_video_script returns valid VideoScript JSON
  • generate_scene_images populates imagen_generada for all scenes
  • render_video returns a valid video URL
  • End-to-end test passes and is documented

Files: tests/test_e2e.py, tests/fixtures/sample_procedure.txt

Verify:

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Rollback: Delete any test videos created in Creatomate via the API or web dashboard; clear test image files from local cache.

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