{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-root-signals","slug":"root-signals","name":"Root Signals","type":"mcp","url":"https://github.com/root-signals/root-signals-mcp","page_url":"https://unfragile.ai/root-signals","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-root-signals__cap_0","uri":"capability://planning.reasoning.llm.output.evaluation.via.structured.scoring.rubrics","name":"llm output evaluation via structured scoring rubrics","description":"Provides MCP tools that allow AI agents to evaluate their own outputs against developer-defined scoring rubrics. Agents can invoke evaluation endpoints that apply multi-dimensional scoring criteria (accuracy, relevance, completeness, etc.) to generated content, receiving structured feedback scores and reasoning. This enables agents to assess quality before returning results to users or triggering refinement loops.","intents":["I want my AI agent to self-evaluate generated code or text before committing it","I need to measure if my agent's output meets specific quality thresholds before proceeding","I want to collect structured feedback signals to improve my agent's behavior over time"],"best_for":["AI agent developers building self-improving systems","Teams implementing quality gates in agentic workflows","Builders creating feedback loops for LLM-based applications"],"limitations":["Evaluation quality depends entirely on rubric design — poorly specified criteria produce unreliable scores","Adds latency per evaluation call (typically 1-3 seconds depending on LLM backend)","Requires explicit rubric definition; no automatic rubric generation from examples"],"requires":["MCP client implementation (Node.js, Python, or other MCP-compatible runtime)","Access to an LLM backend (OpenAI, Anthropic, local model, etc.) for scoring","Defined evaluation rubrics in JSON or structured format"],"input_types":["text (generated output to evaluate)","structured rubric definitions (JSON schema with scoring dimensions)"],"output_types":["structured evaluation scores (numeric ratings per dimension)","reasoning explanations (text justification for scores)","pass/fail verdicts (boolean or threshold-based)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-root-signals__cap_1","uri":"capability://data.processing.analysis.agent.performance.signal.collection.and.logging","name":"agent performance signal collection and logging","description":"Collects structured signals about agent execution (success/failure outcomes, evaluation scores, latency, token usage, error types) and logs them to a centralized signal store. Agents can emit signals at key decision points, and the system aggregates these signals to build performance profiles. This creates a telemetry foundation for understanding agent behavior patterns and identifying improvement opportunities.","intents":["I want to track which agent actions succeed or fail most frequently","I need to correlate evaluation scores with specific agent behaviors to find patterns","I want to monitor agent performance over time and detect regressions"],"best_for":["Teams running production AI agents and needing observability","Researchers analyzing agent behavior for improvement insights","Developers building feedback loops from agent execution data"],"limitations":["Signal collection adds overhead to each agent step; requires careful instrumentation to avoid performance degradation","No built-in aggregation or analytics — requires external tools (databases, dashboards) to analyze collected signals","Signal schema must be defined upfront; schema changes require migration of historical data"],"requires":["MCP client with signal emission capability","Storage backend for signal persistence (database, file system, or cloud service)","Agent instrumentation to emit signals at relevant execution points"],"input_types":["structured signal objects (outcome, scores, metadata, timestamps)","agent execution context (task, model, parameters)"],"output_types":["logged signal records (timestamped, queryable)","signal aggregations (counts, averages, distributions)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-root-signals__cap_2","uri":"capability://planning.reasoning.iterative.agent.refinement.via.feedback.loops","name":"iterative agent refinement via feedback loops","description":"Enables agents to use evaluation signals and performance data to automatically refine their behavior across multiple iterations. Agents can inspect their own evaluation results, identify failure patterns, and adjust their approach (prompts, tool selection, parameter tuning) before retrying tasks. The system tracks refinement iterations and measures improvement, creating a self-improving agent loop without human intervention.","intents":["I want my agent to retry failed tasks with a different approach based on evaluation feedback","I need to automatically tune agent parameters (temperature, tools, prompts) based on performance signals","I want to measure if my agent improves over multiple refinement iterations"],"best_for":["Builders creating autonomous agents that improve without human feedback","Teams implementing continuous agent optimization in production","Researchers studying agent self-improvement mechanisms"],"limitations":["Refinement loops can spiral into infinite retries if termination conditions are poorly defined — requires careful threshold tuning","No guarantee of convergence; agent may oscillate between different approaches without improving","Requires agents to have modifiable behavior (prompts, tools, parameters) — not applicable to fixed-behavior agents"],"requires":["Agent with mutable behavior (adjustable prompts, tool selection, or parameters)","Evaluation capability (from structured scoring rubrics) to measure improvement","Refinement strategy definition (how to adjust behavior based on feedback)"],"input_types":["evaluation feedback (scores, reasoning, failure reasons)","agent execution history (previous attempts, outcomes)"],"output_types":["refined agent behavior (updated prompts, tool selections, parameters)","improvement metrics (score deltas, iteration count, convergence status)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-root-signals__cap_3","uri":"capability://data.processing.analysis.multi.dimensional.evaluation.scoring.with.custom.rubrics","name":"multi-dimensional evaluation scoring with custom rubrics","description":"Supports evaluation rubrics with multiple independent scoring dimensions (e.g., code correctness, readability, performance, security) where each dimension has its own scoring scale and criteria. Rubrics are defined as structured schemas that specify dimension names, scoring ranges, and evaluation instructions. The evaluation engine applies all dimensions to a single output and returns a multi-dimensional score vector, enabling nuanced quality assessment beyond single-metric scoring.","intents":["I want to evaluate code on multiple criteria (correctness, style, performance) simultaneously","I need to weight different quality dimensions differently based on my use case","I want to see which dimensions my agent struggles with most"],"best_for":["Teams with complex quality requirements across multiple dimensions","Code generation or content creation workflows requiring nuanced evaluation","Builders analyzing agent performance across different quality axes"],"limitations":["Rubric design is non-trivial; poorly balanced dimensions can skew overall assessment","Evaluation cost scales with number of dimensions (each dimension typically requires separate LLM call)","No automatic rubric generation or optimization — requires manual design and iteration"],"requires":["Structured rubric definition with dimension specifications","LLM backend capable of multi-turn or multi-prompt evaluation","Clear evaluation instructions for each dimension"],"input_types":["output to evaluate (code, text, structured data)","rubric schema (dimension definitions, scoring ranges, criteria)"],"output_types":["multi-dimensional score vector (numeric scores per dimension)","per-dimension reasoning (text explanation for each score)","aggregate score (optional weighted combination)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-root-signals__cap_4","uri":"capability://tool.use.integration.mcp.protocol.integration.for.agent.tool.invocation","name":"mcp protocol integration for agent tool invocation","description":"Exposes Root Signals evaluation and refinement capabilities as standard MCP tools that agents can discover and invoke like any other tool. The MCP integration layer handles tool schema definition, parameter validation, and response formatting, allowing agents to call evaluation and signal emission functions using their native tool-calling mechanisms. This enables seamless integration into existing agentic frameworks without custom glue code.","intents":["I want my agent to call evaluation tools using its standard tool-calling interface","I need to integrate Root Signals into my existing MCP-based agent framework","I want evaluation and refinement to be discoverable as standard agent tools"],"best_for":["Teams using MCP-compatible agent frameworks (Claude, custom agents, etc.)","Developers building agent systems with standard tool-calling patterns","Builders integrating multiple MCP tools into a unified agent platform"],"limitations":["Requires MCP client implementation; not compatible with non-MCP agent frameworks","Tool schema must match agent's tool-calling expectations; schema mismatches cause invocation failures","MCP protocol overhead adds latency compared to direct function calls"],"requires":["MCP-compatible agent runtime (Claude SDK, custom MCP client, etc.)","MCP server implementation of Root Signals tools","Tool schema definitions matching agent's tool-calling format"],"input_types":["tool invocation requests (MCP format with parameters)","tool schema definitions (JSON schema for parameters and outputs)"],"output_types":["tool responses (structured results in MCP format)","error responses (MCP error format with diagnostic info)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-root-signals__cap_5","uri":"capability://planning.reasoning.signal.driven.agent.behavior.adaptation","name":"signal-driven agent behavior adaptation","description":"Analyzes accumulated performance signals to identify patterns in agent behavior and automatically suggest or apply behavior adaptations. The system correlates evaluation scores, execution outcomes, and signal metadata to detect failure modes (e.g., 'agent fails on tasks with X characteristic'), then recommends behavior changes (prompt modifications, tool additions, parameter adjustments) to address identified patterns. Adaptations can be applied automatically or presented to developers for review.","intents":["I want to understand why my agent fails on certain types of tasks","I need to automatically detect and fix recurring agent failure patterns","I want recommendations for how to improve my agent based on its execution history"],"best_for":["Teams running agents in production and needing automated optimization","Researchers studying agent failure modes and improvement strategies","Developers iterating on agent design based on empirical performance data"],"limitations":["Pattern detection requires sufficient signal volume; unreliable with sparse execution data","Recommended adaptations may be incorrect or counterproductive; requires validation before application","No causal analysis — correlations between signals and failures may be spurious"],"requires":["Accumulated performance signals from multiple agent executions","Evaluation data correlating outputs with quality scores","Agent behavior model that supports recommended adaptations"],"input_types":["signal history (timestamped performance records)","evaluation results (scores, failure reasons)","agent execution metadata (task type, parameters, tools used)"],"output_types":["failure pattern analysis (identified recurring issues)","adaptation recommendations (suggested behavior changes with rationale)","impact predictions (estimated improvement from adaptations)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Node.js, Python, or other MCP-compatible runtime)","Access to an LLM backend (OpenAI, Anthropic, local model, etc.) for scoring","Defined evaluation rubrics in JSON or structured format","MCP client with signal emission capability","Storage backend for signal persistence (database, file system, or cloud service)","Agent instrumentation to emit signals at relevant execution points","Agent with mutable behavior (adjustable prompts, tool selection, or parameters)","Evaluation capability (from structured scoring rubrics) to measure improvement","Refinement strategy definition (how to adjust behavior based on feedback)","Structured rubric definition with dimension specifications"],"failure_modes":["Evaluation quality depends entirely on rubric design — poorly specified criteria produce unreliable scores","Adds latency per evaluation call (typically 1-3 seconds depending on LLM backend)","Requires explicit rubric definition; no automatic rubric generation from examples","Signal collection adds overhead to each agent step; requires careful instrumentation to avoid performance degradation","No built-in aggregation or analytics — requires external tools (databases, dashboards) to analyze collected signals","Signal schema must be defined upfront; schema changes require migration of historical data","Refinement loops can spiral into infinite retries if termination conditions are poorly defined — requires careful threshold tuning","No guarantee of convergence; agent may oscillate between different approaches without improving","Requires agents to have modifiable behavior (prompts, tools, parameters) — not applicable to fixed-behavior agents","Rubric design is non-trivial; poorly balanced dimensions can skew overall assessment","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=root-signals","compare_url":"https://unfragile.ai/compare?artifact=root-signals"}},"signature":"CybM6gYb4ew+9Ri+mzIv6HR/8QtNr9bCv6AZyyXnst0jWu7V4eofzJmjXRUBfTTLhB0ucj/hVGbfVzn9v0SxBg==","signedAt":"2026-06-21T02:48:41.083Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/root-signals","artifact":"https://unfragile.ai/root-signals","verify":"https://unfragile.ai/api/v1/verify?slug=root-signals","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}