llm-vision-mcp
A TypeScript MCP server that gives text-only LLMs image understanding through StepFun vision models.
README
llm-vision-mcp
A TypeScript MCP server that gives text-only LLMs image understanding through StepFun vision models.
This is useful when the primary model, such as GLM5.2 or DeepSeek V4, does not support image input. The model can call these MCP tools, receive text or structured visual analysis, then continue reasoning with the result.
Tools
analyze_image: general image understandingextract_text_from_image: OCR for screenshots, logs, documents, code, and UI textdiagnose_error_screenshot: error screenshot and stack trace diagnosisunderstand_technical_diagram: architecture, flowchart, UML, ER, sequence, and network diagramsanalyze_data_visualization: charts, tables, dashboards, and metrics screenshotsui_to_artifact: UI screenshot to implementation notes or design specsui_diff_check: expected vs actual UI screenshot comparison
Setup
npm install
cp .env.example .env
Set STEPFUN_API_KEY in the MCP client environment. Injecting env vars through the MCP client config is usually the most explicit and reliable setup.
Required:
STEPFUN_API_KEY=your_stepfun_api_key
Optional:
# standard | step_plan
STEPFUN_API_MODE=standard
STEPFUN_BASE_URL=https://api.stepfun.com/v1
STEPFUN_VISION_MODEL=step-1o-turbo-vision
STEPFUN_DEFAULT_DETAIL=high
STEPFUN_TIMEOUT_MS=120000
Step Plan
Step Plan uses the same API key style but a different Base URL:
STEPFUN_API_MODE=step_plan
When STEPFUN_API_MODE=step_plan is set, defaults change to:
STEPFUN_BASE_URL=https://api.stepfun.com/step_plan/v1
STEPFUN_VISION_MODEL=step-3.7-flash
You can still override either value explicitly:
STEPFUN_API_MODE=step_plan
STEPFUN_BASE_URL=https://api.stepfun.com/step_plan/v1
STEPFUN_VISION_MODEL=step-3.7-flash
For backward compatibility, STEPFUN_USE_STEP_PLAN=true also enables Step Plan mode when STEPFUN_API_MODE is not set.
Run
npm run build
npm run start
Run With npx
After this package is published to npm:
npx -y llm-vision-mcp
The published package runs on Node.js and does not require Bun on the user's machine.
MCP Client Config
Example:
{
"mcpServers": {
"llm-vision-mcp": {
"command": "node",
"args": ["/Users/shaoyun/workdir/llm-vision-mcp/dist/index.js"],
"env": {
"STEPFUN_API_KEY": "your_stepfun_api_key",
"STEPFUN_API_MODE": "step_plan",
"STEPFUN_DEFAULT_DETAIL": "high"
}
}
}
}
npm package example after publishing:
{
"mcpServers": {
"llm-vision-mcp": {
"command": "npx",
"args": ["-y", "llm-vision-mcp"],
"env": {
"STEPFUN_API_KEY": "your_stepfun_api_key",
"STEPFUN_API_MODE": "step_plan",
"STEPFUN_DEFAULT_DETAIL": "high"
}
}
}
}
Image Inputs
Every single-image tool accepts:
{
"image": "/absolute/path/to/screenshot.png",
"question": "What does this error mean?",
"detail": "high"
}
The image field supports:
- local file path
file://pathhttp://orhttps://URLdata:image/...;base64,...Data URL
ui_diff_check accepts two images:
{
"expected_image": "/absolute/path/to/expected.png",
"actual_image": "/absolute/path/to/actual.png",
"question": "Focus on layout and missing buttons.",
"detail": "high"
}
Notes
- Use
detail: "high"for OCR, UI, diagrams, charts, and screenshots. - Use
detail: "low"for faster, cheaper coarse image understanding. - StepFun supports JPG/JPEG, PNG, WebP, and static GIF image inputs.
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.
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.
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.
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.