Root Signals
Equip AI agents with evaluation and self-improvement capabilities with Root Signals.
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Overview
> [!WARNING] > This repository is deprecated and no longer maintained. > > Scorable now runs a hosted remote MCP server at https://api.scorable.ai/mcp. It needs no > installation, no local process, and no container, and it tracks the platform automatically. > > bash > claude mcp add --transport http scorable https://api.scorable.ai/mcp \ > --header "Authorization: Bearer $SCORABLE_API_KEY" > > > The hosted server covers everything this one did and more: alongside running evaluators and > judges it can list, create, and update them, generate a judge from a plain-language > description, and query past execution logs — 14 tools in total. It also scores whole > conversations, not just single request/response pairs. > > See the MCP Server documentation for > install instructions for Claude Code, Codex, Cursor, and other clients. > > This repository stays available for anyone who specifically needs a stdio transport or wants > to run the MCP layer inside their own network, but it will not re
Installation
docker run -e SCORABLE_API_KEY=<your_key> -p 0.0.0.0:9090:9090 --name=rs-mcp -d ghcr.io/scorable/scorable-mcp:latest
Configuration
{
"mcpServers": {
"scorable": {
"url": "http://localhost:9090/sse"
}
}Capabilities
- List available evaluators
Retrieve all evaluators defined in your Scorable account with `list_evaluators`.
- Run an evaluator by ID
Score a request/response pair against a specific evaluator using `run_evaluation`.
- Run an evaluator by name
Score content using an evaluator’s name instead of its ID with `run_evaluation_by_name`.
- List available judges
Get all LLM-as-a-judge configurations from your account via `list_judges`.
- Run a judge
Evaluate a request/response pair through a judge collection using `run_judge`.
- Check coding policy adherence
Evaluate code against policy documents or AI rules files with `run_coding_policy_adherence`.
Tags
- ai-agent
- llm
- testing-qa
- workflow-automation
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