AI Agent for Root Signals
Integrate Root Signals MCP Server to enable precise measurement and control of LLM automation quality. Easily evaluate AI outputs against critical benchmarks like clarity, conciseness, and policy adherence using robust Root Signals evaluators. Perfect for teams aiming to elevate AI agent performance, compliance, and transparency in real-time workflows.
Automated LLM Output Evaluation
Root Signals MCP Server exposes a set of advanced evaluators as tools, enabling automated quality assessment for all your AI assistant and agent responses. Effortlessly measure clarity, conciseness, relevance, and policy adherence to ensure consistent, high-quality outputs.
- Evaluator Tool Access.
- Access a library of evaluators for measuring response quality, including conciseness, relevance, and clarity.
- Policy Adherence.
- Run coding policy adherence checks leveraging AI rules files and policy documents.
- Judge Collections.
- Utilize 'judges'—collections of evaluators—to form comprehensive LLM-as-a-judge workflows.
- Seamless Integration.
- Deploy via Docker and connect to any MCP client such as Cursor for instant evaluation in your existing stack.
Real-Time AI Quality Feedback
Receive actionable, real-time feedback on AI agent performance. The Root Signals MCP Server uses SSE for live network deployment and can be integrated directly into tools like Cursor or via code, ensuring that every LLM interaction is continuously measured and improved.
- Live SSE Deployment.
- Implement live feedback loops with Server Sent Events (SSE) for networked environments.
- Flexible Integration.
- Integrate via Docker, stdio, or direct code for maximum compatibility with your preferred development environment.
- Instant Evaluation Results.
- Get instant scoring and justifications for every LLM output, ensuring rapid iteration and improvement.
Boost LLM Automation Transparency
With Root Signals, monitor, audit, and enhance your AI automation workflows. Ensure every LLM-powered process is transparent, compliant, and optimized for business needs, supporting both product and engineering teams with robust evaluation infrastructure.
- Process Transparency.
- Track and audit every LLM evaluation step to ensure full visibility for compliance and improvement.
- Automated Auditing.
- Automate quality and compliance checks across your AI workflows for peace of mind.
MCP INTEGRATION
Available Root Signals MCP Integration Tools
The following tools are available as part of the Root Signals MCP integration:
- list_evaluators
Lists all available evaluators on your Root Signals account for selection and use.
- run_evaluation
Runs a standard evaluation using a specified evaluator ID to assess responses.
- run_evaluation_by_name
Runs a standard evaluation by evaluator name, enabling flexible quality assessments.
- run_coding_policy_adherence
Evaluates coding policy adherence using policy documents and AI rules files.
- list_judges
Lists all available judges—groups of evaluators for LLM-as-a-judge scenarios.
- run_judge
Runs a judge evaluation using a specified judge ID to assess with multiple evaluators.
Unlock LLM Evaluation for Your AI Workflows
Start measuring, improving, and controlling your AI assistant and agent outputs with Root Signals. Book a demo or try it instantly—see how easy quality assurance for LLM automations can be.
What is Root Signals
Root Signals is a comprehensive LLM Measurement & Control Platform designed to empower teams to deliver reliable, measurable, and auditable large language model (LLM) automations at scale. The platform enables users to create, optimize, and embed automated evaluators directly into their codebase, allowing for continuous monitoring of LLM behaviors in production environments. Root Signals addresses the core challenges of deploying generative AI—trust, control, and safety—by providing tools to measure LLM output quality, prevent hallucinations, and ensure regulatory compliance. It is LLM-agnostic, supporting integration with leading models and tech stacks, and is tailored for organizations that require robust evaluation, traceability, and ongoing improvement of AI-powered products.
Capabilities
What we can do with Root Signals
Root Signals provides robust tools to monitor, evaluate, and control the outputs and behaviors of LLM-powered applications. The service is purpose-built for development and operations teams who need to ensure their AI-driven features are launching with measurable quality and safety.
- Continuous LLM evaluation
- Continuously monitor and evaluate the outputs of your LLMs in production to ensure high quality and trustworthy results.
- Automated evaluator integration
- Embed custom, automated evaluation logic directly in your application code to automate quality checks.
- Prompt and judge optimization
- Experiment and optimize prompts and judges to balance quality, cost, and latency for your AI features.
- Production monitoring
- Get real-time visibility into LLM behaviors to catch issues early and prevent reputation-damaging outputs.
- LLM-agnostic integration
- Seamlessly connect with any major LLM or technology stack, adapting to your team’s preferred infrastructure.
How AI Agents Benefit from Root Signals
AI agents benefit from Root Signals by gaining access to automated, continuous evaluation frameworks that ensure LLM-generated outputs are trustworthy, accurate, and compliant. The platform's monitoring and optimization capabilities help AI agents adapt in real-time, prevent hallucinations, and maintain the quality of their responses as they interact within production systems. This results in more reliable AI-driven workflows, reduced risk, and faster iteration cycles for organizations deploying generative AI solutions.