AgentScore
MCP ServerFreeTrust scoring for AI agents via MCP. Check any agent's reputation before transacting — no API key, zero config.
Capabilities6 decomposed
mcp-native agent reputation scoring
Medium confidenceProvides trust scores for AI agents through the Model Context Protocol (MCP) standard, enabling Claude and other MCP-compatible clients to query agent reputation data without requiring API keys or configuration. The system integrates directly into the MCP server architecture, allowing seamless tool calling from within agent conversations to fetch and evaluate agent trustworthiness metrics before transaction execution.
Implements trust scoring as a zero-configuration MCP tool that requires no API key or client-side setup, leveraging MCP's native function-calling protocol to embed agent vetting directly into LLM reasoning loops without context overhead or authentication friction
Eliminates authentication and configuration barriers that plague traditional API-based trust services, enabling instant agent reputation checks within any MCP-compatible environment without developer setup
zero-configuration mcp server deployment
Medium confidenceExposes agent reputation data as a plug-and-play MCP server that requires no API keys, environment variables, or configuration files to activate. The server auto-discovers and registers itself within the MCP ecosystem, allowing clients to immediately call reputation-scoring functions without setup overhead. This is achieved through MCP's standard server discovery and tool registration mechanisms, with sensible defaults for all parameters.
Implements MCP server registration with zero configuration by using MCP's native discovery and auto-registration patterns, eliminating the need for API keys, environment setup, or manual tool registration that typical MCP integrations require
Faster to integrate than REST API-based reputation services because it skips authentication, credential management, and manual endpoint configuration — just add the MCP server and start calling reputation functions
agent reputation database querying via mcp tools
Medium confidenceProvides structured MCP tool definitions that allow LLM clients to query a centralized agent reputation database, returning trust scores, transaction history, flagged behaviors, and confidence metrics. The system uses MCP's tool-calling protocol to expose database queries as semantic functions, enabling agents to reason about trust signals and make decisions about which agents to interact with. Queries are executed server-side against the reputation database, with results formatted as structured JSON for downstream processing.
Exposes agent reputation queries as semantic MCP tools rather than raw API endpoints, allowing LLMs to reason about trust signals and integrate vetting decisions into agentic workflows without explicit API calls or JSON parsing
More natural for LLM-based agents than REST APIs because queries are expressed as semantic tool calls that fit naturally into reasoning chains, reducing the cognitive load on agents to construct and interpret API responses
trust-based agent filtering and selection
Medium confidenceEnables agents to programmatically filter and rank other agents based on reputation scores, using MCP tool calls to query the reputation database and apply filtering logic. The system returns ranked lists of agents sorted by trust score, allowing downstream agents to select the most reliable option for a given task. This capability is implemented through MCP tool composition, where reputation queries feed into agent selection logic within the LLM's reasoning loop.
Implements agent filtering as a reasoning task within the LLM's planning loop, allowing agents to dynamically apply trust-based selection logic without hardcoded rules or external orchestration — the agent itself decides which peers to trust based on reputation data
More flexible than static agent whitelists because trust decisions are made dynamically based on current reputation data, and more transparent than opaque routing algorithms because the agent can explain its selection rationale
agent behavior flagging and risk indicators
Medium confidenceSurfaces flagged behaviors and risk indicators from the reputation database, allowing agents to identify agents with security incidents, policy violations, or suspicious activity patterns. The system returns structured risk signals (e.g., 'fraud_suspected', 'policy_violation', 'unusual_activity') alongside reputation scores, enabling downstream agents to make informed decisions about whether to interact with flagged agents. Risk indicators are computed server-side and cached in the reputation database.
Provides structured risk indicators as first-class data in the reputation API, allowing agents to programmatically detect and respond to security incidents without requiring manual review or external monitoring systems
More actionable than generic trust scores because risk indicators are specific and categorical, enabling agents to implement nuanced safety policies (e.g., 'refuse fraud-flagged agents but accept policy-violation agents with manual review')
agent ecosystem transparency via public reputation data
Medium confidenceExposes aggregated, anonymized agent reputation data as public MCP resources, enabling researchers, builders, and agents themselves to analyze trust patterns across the agent ecosystem. The data is queryable without authentication, supporting transparency initiatives and enabling data-driven decisions about agent reliability trends and ecosystem health.
Publishes agent reputation as open MCP resources rather than gated behind authentication, enabling ecosystem-wide transparency and enabling third-party analysis tools to build on top of reputation data.
More transparent than proprietary agent rating systems because all reputation data is publicly queryable via MCP, enabling independent verification and reducing information asymmetry in agent selection.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with AgentScore, ranked by overlap. Discovered automatically through the match graph.
agent-scan
Security scanner for AI agents, MCP servers and agent skills.
MCP Servers Rating and User Reviews
** - Website to rate MCP servers, write authentic user reviews, and [search engine for agent & mcp](http://www.deepnlp.org/search/agent)
@toolrank/mcp-server
ToolRank MCP Server — Score and optimize MCP tool definitions for AI agent discovery. The first ATO (Agent Tool Optimization) tool.
agent-zero
MCP server: agent-zero
network-ai
AI agent orchestration framework for TypeScript/Node.js - 29 adapters (LangChain, AutoGen, CrewAI, OpenAI Assistants, LlamaIndex, Semantic Kernel, Haystack, DSPy, Agno, MCP, OpenClaw, A2A, Codex, MiniMax, NemoClaw, APS, Copilot, LangGraph, Anthropic Compu
Agentic Radar
Open-source CLI security scanner for agentic...
Best For
- ✓AI agent developers building multi-agent systems with trust requirements
- ✓Teams deploying Claude-based agents that need to verify third-party agent reliability
- ✓Builders creating agent marketplaces or agent orchestration platforms
- ✓Security-conscious teams implementing agent vetting before delegation
- ✓Rapid prototyping teams building agent systems who want minimal setup time
- ✓Open-source projects that need to avoid credential management complexity
- ✓Educational environments teaching agent orchestration without security overhead
- ✓Small teams without dedicated DevOps infrastructure
Known Limitations
- ⚠Reputation scoring is only as reliable as the underlying data sources feeding the system — no guarantee of real-time accuracy if data sources lag
- ⚠No built-in dispute resolution or appeals process for agents with low scores
- ⚠Scoring algorithm and weighting factors are not transparent in public documentation
- ⚠Limited to agents already indexed in the AgentScore database — new or private agents will have no score
- ⚠No authentication means any client with network access can query reputation data — not suitable for private or sensitive agent networks
- ⚠No rate limiting or quota management built into the zero-config mode — relies on upstream service limits
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
Trust scoring for AI agents via MCP. Check any agent's reputation before transacting — no API key, zero config.
Categories
Alternatives to AgentScore
Search the Supabase docs for up-to-date guidance and troubleshoot errors quickly. Manage organizations, projects, databases, and Edge Functions, including migrations, SQL, logs, advisors, keys, and type generation, in one flow. Create and manage development branches to iterate safely, confirm costs
Compare →AI-optimized web search and content extraction via Tavily MCP.
Compare →Scrape websites and extract structured data via Firecrawl MCP.
Compare →Are you the builder of AgentScore?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →