trustwiseai/trustwise-mcp-server
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The Trustwise MCP Server is a Model Context Protocol server designed to evaluate AI safety, alignment, and performance using advanced metrics.
The Trustwise MCP Server is a cutting-edge Model Context Protocol (MCP) server that offers a comprehensive suite of evaluation tools for assessing AI safety, alignment, and performance. It is specifically designed to help developers and AI tools programmatically evaluate the quality, safety, and cost of outputs generated by large language models (LLMs). By leveraging Trustwise's industry-leading metrics, users can ensure that AI-generated content is reliable, safe, and aligned with desired outcomes. The server is particularly useful for evaluating the safety and reliability of LLM responses, measuring the alignment, clarity, and helpfulness of AI-generated content, and estimating the carbon footprint and cost of model inference. It can be seamlessly integrated into AI pipelines, agents, or orchestration frameworks, providing robust evaluation capabilities to enhance AI development and deployment processes.
Features
- Advanced evaluation tools for AI safety and alignment
- Industry-leading metrics for assessing LLM outputs
- Programmatic assessment of quality, safety, and cost
- Integration with AI pipelines and orchestration frameworks
- Estimation of carbon footprint and inference cost
Usages
usage with Claude Desktop
{ "mcpServers": { "trustwise": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "TW_API_KEY", "ghcr.io/trustwiseai/trustwise-mcp-server:latest" ], "env": { "TW_API_KEY": "<YOUR_TRUSTWISE_API_KEY>" } } } }
usage with Cursor
{ "mcpServers": { "trustwise": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "TW_API_KEY", "-e", "TW_BASE_URL", "ghcr.io/trustwiseai/trustwise-mcp-server:latest" ], "env": { "TW_API_KEY": "<YOUR_TRUSTWISE_API_KEY>" } } } }
Tools
faithfulness_metric
Evaluate the faithfulness of a response to its context
answer_relevancy_metric
Evaluate relevancy of a response to the query
context_relevancy_metric
Evaluate relevancy of context to the query
pii_metric
Detect PII in a response
prompt_injection_metric
Detect prompt injection risk
summarization_metric
Evaluate summarization quality
clarity_metric
Evaluate clarity of a response
formality_metric
Evaluate formality of a response
helpfulness_metric
Evaluate helpfulness of a response
sensitivity_metric
Evaluate sensitivity of a response
simplicity_metric
Evaluate simplicity of a response
tone_metric
Evaluate tone of a response
toxicity_metric
Evaluate toxicity of a response
carbon_metric
Estimate carbon footprint of a response
cost_metric
Estimate cost of a response