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LLM Vision MCP Server

A TypeScript MCP server that gives text-only LLMs image understanding through StepFun vision models.

Node.jsstdio

context tax

pending

security

pending

cold start

pending

freshness

pending

Our reading of LLM Vision, from the data

LLM Vision is a ai & llm tools MCP server.

We haven't measured its tool list yet, so its token cost is unknown.

It runs locally on your machine over stdio.

Install LLM Vision MCP server

Install in Claude Code

claude mcp add llm-vision -- npx -y llm-vision-mcp

Freshness

Not enough public signals yet to judge maintenance.

  1. Last commit (default branch)

    no data · GitHub

  2. Latest release

    no data · GitHub

  3. Package published

    no data · npm/PyPI

  4. Registry entry updated

    no data · official registry

How it compares in AI & LLM Tools

Against the most-starred ai & llm tools servers we index. Token cost is per model turn, measured from each server's live tool list.

ServerStarsToolsTokens / turnFreshnessRuns
LLM Vision (this)——not measured—local
Memory91,356—not measuredActivelocal
Claude Flow76,638—not measuredActivelocal
Next AI Draw.io36,318—not measured—local
Serena Agent30,371—not measuredActivelocal
Trycua30,160—not measuredActive—
All ai & llm tools MCP servers

FAQ

›How do I install the LLM Vision MCP server in Claude Code?

Run: claude mcp add llm-vision -- npx -y llm-vision-mcp. For Cursor, VS Code, Claude Desktop and Windsurf, use the install tabs above.

›Does LLM Vision require an API key?

No required environment variables are declared in its published metadata.

›Can I use LLM Vision as a remote (hosted) MCP server?

No hosted endpoint is published; it runs locally over stdio.

›Is LLM Vision in the official MCP registry?

No. It was indexed from npm.

Sources and method

Package registry (npm / PyPI)
listed
Last checked against all sources
2026-10-19

The description, packages and links come from the maintainer's own published metadata. Token cost, security signals, freshness and the reading above are MCPSkillHub's own measurements; see the methodology. Spot an error? Request a correction or claim the listing.

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