{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_agentscore-xyz-agentscore","slug":"agentscore-xyz-agentscore","name":"AgentScore","type":"mcp","url":"https://agentscores.xyz","page_url":"https://unfragile.ai/agentscore-xyz-agentscore","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:agentscore-xyz/agentscore"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_agentscore-xyz-agentscore__cap_0","uri":"capability://tool.use.integration.mcp.native.agent.reputation.scoring","name":"mcp-native agent reputation scoring","description":"Provides 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.","intents":["Check an AI agent's reputation score before deciding to use or transact with it","Integrate agent trust verification into an agentic workflow without adding authentication overhead","Query historical trust data and reputation signals for multiple agents in a single MCP session","Build safety guardrails that prevent interaction with low-reputation agents"],"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"],"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"],"requires":["MCP-compatible client (Claude, or other MCP-supporting LLM interface)","Network connectivity to AgentScore service","No API key or authentication token required"],"input_types":["agent identifier (name, address, or unique ID)","optional query parameters for filtering or sorting results"],"output_types":["structured JSON with reputation score, confidence level, and metadata","historical trust signals and transaction count","risk indicators and flagged behaviors"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agentscore-xyz-agentscore__cap_1","uri":"capability://tool.use.integration.zero.configuration.mcp.server.deployment","name":"zero-configuration mcp server deployment","description":"Exposes 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.","intents":["Quickly add agent trust verification to an existing Claude or MCP-based workflow without configuration","Deploy agent reputation checking in a development environment without managing secrets or credentials","Enable non-technical users to integrate agent vetting into their agentic systems","Reduce onboarding friction for teams adopting agent-based architectures"],"best_for":["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"],"limitations":["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","Configuration cannot be customized without modifying server code or environment","No support for custom scoring algorithms or weighted reputation models"],"requires":["MCP client implementation (Claude desktop, or MCP-compatible LLM interface)","Network access to agentscores.xyz service","No API key, no environment variables, no configuration files"],"input_types":["agent identifier passed via MCP tool parameters"],"output_types":["JSON-formatted reputation score and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agentscore-xyz-agentscore__cap_2","uri":"capability://search.retrieval.agent.reputation.database.querying.via.mcp.tools","name":"agent reputation database querying via mcp tools","description":"Provides 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.","intents":["Look up trust scores and reputation metrics for a specific agent before delegating work to it","Retrieve historical transaction data and behavioral signals for an agent to assess reliability","Compare reputation scores across multiple agents to select the most trustworthy option","Identify agents with flagged behaviors or security incidents in their history"],"best_for":["Agent orchestration systems that need to make trust-based routing decisions","Multi-agent marketplaces that want to surface reputation data to buyers","Compliance-focused teams that need audit trails of agent vetting decisions","Builders creating agent selection logic based on trust signals"],"limitations":["Query latency depends on AgentScore service availability and database performance — no local caching or offline mode","Reputation data is only as current as the last update to the database — real-time transaction data may lag","No support for custom queries or aggregations — limited to predefined tool schemas","Scoring methodology is opaque — users cannot inspect or validate how trust scores are calculated"],"requires":["MCP client with tool-calling support","Network connectivity to agentscores.xyz","Agent identifier (name, address, or ID) to query"],"input_types":["agent identifier (string)","optional filters (date range, transaction type, behavior category)"],"output_types":["JSON object with reputation score (numeric), confidence level, transaction count, flagged behaviors array, and metadata timestamps"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agentscore-xyz-agentscore__cap_3","uri":"capability://planning.reasoning.trust.based.agent.filtering.and.selection","name":"trust-based agent filtering and selection","description":"Enables 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.","intents":["Automatically select the most trustworthy agent from a pool of candidates for a specific task","Filter out low-reputation agents from consideration before delegating work","Rank agents by trust score to prioritize high-reliability options","Build decision logic that refuses to interact with agents below a trust threshold"],"best_for":["Multi-agent systems with agent pools where trust-based routing is critical","Agent marketplaces that need to surface high-reputation agents to users","Risk-averse teams building financial or security-sensitive agent workflows","Builders implementing agent selection heuristics based on historical performance"],"limitations":["Filtering logic is implemented in the LLM's reasoning loop — no server-side filtering or optimization","No support for weighted scoring or custom ranking algorithms — limited to simple trust score sorting","Threshold-based filtering can exclude all agents if thresholds are too strict — no fallback or escalation logic","Agent reputation is static at query time — does not account for real-time performance or recent incidents"],"requires":["MCP client with tool-calling and reasoning capabilities","Access to agent reputation database via MCP","List of agent identifiers to filter and rank"],"input_types":["array of agent identifiers","optional trust threshold (numeric score)","optional sorting criteria (score, transaction count, recency)"],"output_types":["ranked array of agents with reputation scores and metadata, filtered by trust threshold"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agentscore-xyz-agentscore__cap_4","uri":"capability://safety.moderation.agent.behavior.flagging.and.risk.indicators","name":"agent behavior flagging and risk indicators","description":"Surfaces 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.","intents":["Identify agents with known security incidents or fraud history before interacting with them","Detect agents that have violated policies or terms of service","Flag agents with unusual or suspicious activity patterns for manual review","Build safety guardrails that automatically refuse interaction with high-risk agents"],"best_for":["Security-focused teams building agent systems with strict risk tolerance","Compliance-heavy organizations that need to audit agent interactions","Agent marketplaces that want to surface risk information to users","Teams implementing automated incident response for compromised agents"],"limitations":["Risk indicators are only as reliable as the underlying detection systems — false positives and false negatives are possible","No real-time incident detection — risk flags are updated asynchronously and may lag behind actual incidents","No support for custom risk categories or weighted risk scoring — limited to predefined risk types","Risk data is opaque — no visibility into how risk indicators are computed or what evidence supports them"],"requires":["MCP client with tool-calling support","Access to agent reputation database with risk indicator data","Agent identifier to query"],"input_types":["agent identifier (string)"],"output_types":["JSON object with risk indicator array (strings like 'fraud_suspected', 'policy_violation'), risk score (numeric), and supporting metadata"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agentscore-xyz-agentscore__cap_5","uri":"capability://search.retrieval.agent.ecosystem.transparency.via.public.reputation.data","name":"agent ecosystem transparency via public reputation data","description":"Exposes 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.","intents":["Analyze reputation distribution across agent categories to identify ecosystem trust gaps","Build dashboards or reports showing agent reliability trends over time","Enable agents to benchmark their own reputation against peer agents in their category"],"best_for":["Researchers studying AI agent ecosystem trust and reliability","Marketplace operators building transparency features into agent discovery UIs","Agent developers seeking to understand competitive reputation landscape"],"limitations":["Public data exposure may enable gaming or reputation manipulation if scoring methodology is reverse-engineered","Aggregated data may obscure important context about individual agent use cases or specializations","No built-in privacy controls for agents that prefer not to be publicly ranked"],"requires":["MCP client with network access"],"input_types":["optional filters: category, date range, score threshold"],"output_types":["JSON array of agent reputation records with metadata, suitable for analysis and visualization"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude, or other MCP-supporting LLM interface)","Network connectivity to AgentScore service","No API key or authentication token required","MCP client implementation (Claude desktop, or MCP-compatible LLM interface)","Network access to agentscores.xyz service","No API key, no environment variables, no configuration files","MCP client with tool-calling support","Network connectivity to agentscores.xyz","Agent identifier (name, address, or ID) to query","MCP client with tool-calling and reasoning capabilities"],"failure_modes":["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","Configuration cannot be customized without modifying server code or environment","No support for custom scoring algorithms or weighted reputation models","Query latency depends on AgentScore service availability and database performance — no local caching or offline mode","Reputation data is only as current as the last update to the database — real-time transaction data may lag","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.062Z","last_scraped_at":"2026-05-03T15:19:49.548Z","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=agentscore-xyz-agentscore","compare_url":"https://unfragile.ai/compare?artifact=agentscore-xyz-agentscore"}},"signature":"ArEBddtyu1wmtnJSTN9ajbniPVMfj4+tvdk7zAaks8PxntIb32CM1iE+/cQ79BWcJbLhdvN503d0cOUMPVHcAw==","signedAt":"2026-06-22T06:51:26.774Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentscore-xyz-agentscore","artifact":"https://unfragile.ai/agentscore-xyz-agentscore","verify":"https://unfragile.ai/api/v1/verify?slug=agentscore-xyz-agentscore","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"}}