Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@claude-eval-mcp Please benchmark Qwen-2.5-7B on GSM8K and profile token latency."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An official Model Context Protocol (MCP) server engineered to connect Claude (Claude Desktop, Claude Code CLI, and autonomous agents) directly to AI/ML evaluation harnesses, token latency profilers, and prompt security red-teaming tools.
sequenceDiagram
autonumber
actor User as Engineer
participant Claude as Claude Desktop / Claude Code
participant MCP as claude-eval-mcp Server
participant Engine as Local Evaluation Engine
User->>Claude: "Evaluate Qwen-2.5-7B on GSM8K and profile TTFT"
Claude->>MCP: Call tool `benchmark_model(model='qwen-2.5-7b', task='gsm8k')`
MCP->>Engine: Run reasoning suite
Engine-->>MCP: Accuracy 82.4%
MCP-->>Claude: JSON Tool Result
Claude-->>User: Format detailed benchmark summary tableRelated MCP server: Claude Code MCP Bridge
Add to your claude_desktop_config.json:
{
"mcpServers": {
"ai-evaluator": {
"command": "python3",
"args": ["-m", "claude_eval.server"]
}
}
}MIT License (c) 2024 Soujanya Gupta.
This server cannot be deployed
One MCP endpoint for Claude, GPT & Gemini: 100+ tools + no-code connectors + agent workers.
Find, vet, and run MCP tools through a secure audited gateway with prompt-injection risk scoring
AI Visibility and Content Intelligence tools for Claude and MCP-compatible agents.
Paid remote MCP for LLM security scans, jailbreak checks, analytics, checkout, and readiness.
