Kimi Vision MCP Server
Enables analysis of local images through Kimi (Moonshot AI) vision models via the MCP protocol, supporting features like OCR and long context understanding.
README
Kimi Vision MCP Server
MCP server for Kimi (Moonshot AI) vision models β analyze images through
the OpenAI-compatible /chat/completions endpoint at
https://api.moonshot.cn/v1.
Features
- πΌοΈ Analyze images (local files: png/jpg/jpeg/gif/webp)
- π§ Auto-pick strongest thinking mode per model:
kimi-k3βreasoning_effort: "max"kimi-k2.6/kimi-k2.5/kimi-k2.7-codeβthinking: {type: "enabled"}
- π Up to 1M token context (kimi-k3)
- π° Same billing as Moonshot V1 β see pricing
- β‘ One-line
npxdeploy, zero non-MCP dependencies
Requirements
- Node.js >= 18
- A platform.kimi.com API key
Install & run
cd kimi-vision-mcp-server
npm install
npm start
Environment variables
| Variable | Required | Default | Description |
|---|---|---|---|
KIMI_API_KEY |
β | β | Your Kimi/Moonshot API key. (MOONSHOT_API_KEY also accepted.) |
KIMI_MODEL |
kimi-k3 |
Model name. Vision-capable: kimi-k3, kimi-k2.7-code, kimi-k2.6, kimi-k2.5, moonshot-v1-*-vision-preview. |
|
KIMI_BASE_URL |
https://api.moonshot.cn/v1 |
Override endpoint (for proxies). | |
KIMI_MAX_TOKENS |
4096 |
Default max output tokens. |
Claude Code / CC-Switch config
{
"mcpServers": {
"kimi-vision": {
"type": "stdio",
"command": "npx",
"args": ["-y", "kimi-vision-mcp-server"],
"env": {
"KIMI_API_KEY": "your-kimi-api-key",
"KIMI_MODEL": "kimi-k3"
}
}
}
}
Or run from a local checkout:
{
"mcpServers": {
"kimi-vision": {
"command": "node",
"args": ["D:\\GitHub\\Claude\\MCP\\Kimi\\kimi-vision-mcp-server\\src\\index.js"],
"env": {
"KIMI_API_KEY": "your-key-here"
}
}
}
}
Tool: kimi_vision_understand
| Parameter | Type | Required | Description |
|---|---|---|---|
image |
string | β | Local image file path (C:/path/to/x.png) or ms://<file-id> for pre-uploaded. Remote HTTP URLs NOT supported. |
prompt |
string | β | What to ask about the image. |
detail |
enum | auto / low / high. Default auto. |
|
max_tokens |
number | Max output tokens. Default 4096. | |
thinking |
bool | Enable reasoning. Default false. Auto-mapped per model. |
Important: Remote URLs not supported
Kimi's vision API does not accept remote HTTP image URLs (per the official docs). You must either:
- Pass a local file path (the MCP will inline it as base64), or
- Upload to Moonshot first and pass
ms://<file-id>(advanced).
Remote URLs are rejected with a clear error message.
Why no temperature parameter?
Per the Kimi API model params reference, all current Kimi models have fixed temperature:
| Model | Temperature |
|---|---|
kimi-k3 |
fixed 1.0 |
kimi-k2.7-code (and -highspeed) |
fixed 1.0 |
kimi-k2.6 thinking |
fixed 1.0 |
kimi-k2.6 non-thinking |
fixed 0.6 |
kimi-k2.5 thinking |
fixed 1.0 |
kimi-k2.5 non-thinking |
fixed 0.6 |
Passing any other value returns HTTP 400. Moonshot has already tuned each
model to its optimal temperature, so this MCP deliberately omits the parameter
and lets the API use its built-in default. Use the thinking flag to switch
between the 1.0 / 0.6 modes for K2.6 / K2.5.
Why Kimi for vision?
- Longest context: kimi-k3 ships with 1M tokens β useful for analyzing long documents alongside images.
- Strong Chinese & English OCR.
- Native video understanding on kimi-k3 / kimi-k2.7-code / kimi-k2.6 (not exposed by this MCP yet β file upload only).
- Cost-effective: Β₯2/M output for the flagship.
Related projects
- doubao-vision-mcp-server β ByteDance Doubao vision
- glm-vision-mcp-server β Zhipu GLM-5V-Turbo
- qwen-vision-mcp-server β Alibaba Qwen3.7-plus
- @kira4094/agnes-image-mcp-server β Agnes Image (text-to-image)
License
MIT
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.