Deepy MCP Server
Thin MCP server that enables AI agents to generate video, image, and audio content via the Deepy API using a personal API key, with safe generation flow and no business logic.
README
@deepy/mcp-server
Thin MCP server that lets external AI agents (Cursor, Claude Desktop, Claude Code, Windsurf, …) drive Deepy generation of video / image / audio over the public Deepy API using a personal API key.
It carries no business logic — billing, moderation, idempotency, rate limits, provider access and ownership checks all stay in the Deepy backend, which is the source of truth. This server is only a safe adapter that exposes Deepy as MCP tools, prompts and resources.
1. Requirements
- Node.js 22+ (the connector runs on your machine).
- A Deepy API key — create one in the Deepy web app under API access.
It is shown only once and looks like
sk_live_…(orsk_test_…).
2. Install & configure
Point your MCP client at the connector and give it two environment values:
| Variable | Value |
|---|---|
DEEPY_API_BASE_URL |
Deepy API base URL, no trailing slash, e.g. https://app.prod.einfra.tech |
DEEPY_API_KEY |
your personal key (sk_live_…) — only from config/env, never chat |
DEEPY_LOG_LEVEL |
optional: debug|info|warn|error|silent (default info) |
Option A — run straight from GitHub (works today, no npm needed)
npx clones this repo, installs its dependencies and runs the committed build —
no npm publish required.
Cursor — put this in ~/.cursor/mcp.json (all projects) or
.cursor/mcp.json (one project):
{
"mcpServers": {
"deepy": {
"command": "npx",
"args": ["-y", "github:deepy-to/deepy-mcp-server"],
"env": {
"DEEPY_API_BASE_URL": "https://app.prod.einfra.tech",
"DEEPY_API_KEY": "sk_live_your_key_here"
}
}
}
}
Claude Desktop — same block in claude_desktop_config.json, then restart
Claude Desktop. Ready-made copies live in mcp-configs/.
Option B — npm (once published)
When @deepy/mcp-server is published to npm, the args simplify to
["-y", "@deepy/mcp-server"].
Option C — local copy (offline / development)
Clone this repo (git clone https://github.com/deepy-to/deepy-mcp-server), run
npm install, then point the config at the built entrypoint:
{
"mcpServers": {
"deepy": {
"command": "node",
"args": ["/ABSOLUTE/PATH/TO/deepy-mcp-server/dist/index.js"],
"env": {
"DEEPY_API_BASE_URL": "https://app.prod.einfra.tech",
"DEEPY_API_KEY": "sk_live_your_key_here"
}
}
}
}
3. Tools
| Tool | Purpose | Backend |
|---|---|---|
deepy_list_models |
list available models | GET /api/v1/public/models |
deepy_get_model |
model schema / options | GET /api/v1/public/models/{name} |
deepy_improve_prompt |
rewrite a draft prompt | POST /api/v1/public/improve-prompt |
deepy_estimate_generation |
price a generation (no charge) | POST /api/v1/public/generations/estimate |
deepy_create_generation |
start a generation (requires confirmed=true) |
POST /api/v1/public/generations |
deepy_get_generation |
poll status | GET /api/v1/public/generations/{id} |
deepy_get_result |
fetch result media (server-side) | GET /api/v1/public/generations/{id}/results/{i} |
The server also ships MCP prompts and resources (skills) that teach an agent the safe generation flow.
4. Safe generation flow
- Understand the task (ask 1–2 clarifying questions if unclear).
- Pick a model (
deepy_list_models/deepy_get_model). - Improve the prompt (
deepy_improve_prompt). - Estimate the cost (
deepy_estimate_generation). - Show the price and get explicit user approval.
- Only then
deepy_create_generationwithconfirmed=true. - Poll
deepy_get_generation, then fetchdeepy_get_result.
deepy_create_generation refuses to run without confirmed=true, generates
an idempotency key when none is given, and never retries a paid create.
5. Security
- The API key is read only from
DEEPY_API_KEY(config/env), never from tool arguments or chat text, and is redacted from every log line. - The server never talks to WaveSpeed/RunPod, the database or S3 directly, and never returns private provider/S3 URLs.
- The backend remains authoritative for moderation, billing, rate limits and
ownership. On
INSUFFICIENT_CREDITStop up; onCONTENT_REJECTEDthe content broke the rules; onMODEL_NOT_FOUNDre-list the catalog; onIDEMPOTENCY_CONFLICTdo not auto-retry.
6. Troubleshooting
| Symptom | Fix |
|---|---|
| No Deepy tools appear | Node.js installed? config saved in the right file? client fully restarted? |
Unauthorized |
key missing/mistyped/disabled — reissue it on API access |
Insufficient balance |
top up the balance and retry |
Model not found |
list models and pick one from the catalog |
7. Development
npm install
npm run build # tsc -> dist/
npm test # vitest (safety-critical behaviour)
npm run dev # tsx src/index.ts
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.