AI-powered docs that write, organize & scale
Docsie MCP brings your documentation workflow directly into Claude. Create, edit, and publish docs without leaving your AI assistant. Convert videos into structured documentation instantly, run compliance analysis on text, audio, and video content, and use semantic search to find exactly what you need across your knowledge base. Whether you're building product docs, SOPs, or technical guides, Docsie MCP makes it faster and smarter - all powered by AI.
This is a well-structured MCP server with proper OAuth2 authentication, SSRF protection, and secure credential handling. The codebase demonstrates good security practices including input validation, safe URL checking, and proper token management via context variables. No critical vulnerabilities were identified, though a few minor code quality improvements are recommended.
7 files analyzed · 5 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: API_KEY
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This plugin can run on your machine or connect to a hosted endpoint. during install.
Once installed, try these example prompts and explore these capabilities:
From the project's GitHub README.
Remote MCP server for Anthropic's Connectors Directory. Converts videos into structured documentation using Dokuta AI.
Users on Claude can:
Free tier: Videos up to 5 minutes are processed free. Longer videos require a Docsie account with AI credits.
Claude → POST https://mcp.docsie.io/mcp (Bearer token)
│
├─→ Docsie /o2/ (OAuth2 token validation)
├─→ Dokuta API (video analysis)
└─→ Docsie /api/internal/mcp/ (credit deduction)
/o2/ provider| Tool | Description |
|---|---|
analyze_video | Submit a video URL for documentation generation |
check_job_status | Poll for progress and retrieve completed results |
list_doc_types | List available doc types and quality tiers |
cd mcp-server
# Install dependencies
pip install -e .
# Configure environment
cp .env.example .env
# Edit .env with your Dokuta API key and Docsie internal URL
# Run
uvicorn app.main:app --reload --port 8000
# Health check
curl http://localhost:8000/health
# OAuth metadata
curl http://localhost:8000/.well-known/oauth-authorization-server
# MCP tool listing (via MCP protocol)
curl -X POST http://localhost:8000/mcp \
-H "Content-Type: application/json" \
-d '{"jsonrpc":"2.0","id":"1","method":"tools/list","params":{}}'
| Variable | Description | Default |
|---|---|---|
MCP_DOCSIE_BASE_URL | Docsie public URL (for OAuth metadata) | https://app.docsie.io |
MCP_DOCSIE_INTERNAL_URL | Docsie internal URL (for API calls) | http://docsie-web:8080 |
MCP_DOKUTA_API_URL | Dokuta video analysis API | https://app.videodokuta.com/api/v1 |
MCP_DOKUTA_API_KEY | Dokuta API key | (required) |
MCP_MCP_INTERNAL_API_KEY | Shared secret for Django internal endpoints | (required) |
MCP_OAUTH2_CLIENT_ID | OAuth2 client ID for the MCP app | (required) |
MCP_FREE_TIER_MAX_SECONDS | Max free video duration in seconds | 300 |
Docker images are built and pushed automatically via GitHub Actions on push to main.
# Deploy with Helm (secrets managed via Doppler)
helm upgrade --install mcp-staging helm/docsie-mcp/ \
-n mcp \
--set image.tag=v2026.04.06.01 \
--set doppler.token=$DOPPLER_SERVICE_TOKEN
In your .env or environment:
MCP_INTERNAL_API_KEY=your-shared-secret-here
In Django admin at /o2/applications/register/:
GET /api/internal/mcp/user-context/ — token validation + user infoPOST /api/internal/mcp/deduct-credits/ — credit deductionTo submit to the directory, you'll need:
https://mcp.docsie.io/mcphttps://mcp.docsie.io/.well-known/oauth-authorization-serverSubmit at: https://console.anthropic.com/dashboard/connectors
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