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Implements MCP resource handlers that translate Kubernetes API calls into structured JSON responses, enabling semantic understanding of cluster topology and workload status by language models.","intents":["Query the current state of pods, deployments, and services in a Kubernetes cluster from an LLM agent","Retrieve detailed metadata about running workloads including resource requests, limits, and status conditions","Inspect cluster configuration and resource definitions without requiring kubectl CLI access","Enable an AI agent to understand cluster health and topology for decision-making in automation workflows"],"best_for":["DevOps engineers building AI-assisted Kubernetes management agents","Platform teams integrating LLM-based observability and automation into Kubernetes workflows","Developers creating intelligent deployment and troubleshooting assistants"],"limitations":["Read-only access to cluster state — no mutation capabilities through this interface","Requires Metoro monitoring agent already deployed and configured in the target cluster","Latency depends on Metoro backend API response times; not suitable for real-time streaming use cases","Limited to resources and metrics that Metoro's monitoring agent collects — custom CRDs may not be exposed"],"requires":["Kubernetes cluster with Metoro monitoring agent installed and running","Metoro API credentials (API key or authentication token)","MCP-compatible client (Claude Desktop, or custom MCP client implementation)","Network connectivity from MCP server to Metoro backend API"],"input_types":["MCP resource request (JSON-RPC format with resource URIs)","Query parameters (namespace, resource type, label selectors)"],"output_types":["JSON-structured Kubernetes resource objects","Aggregated cluster metrics and status summaries","Formatted text descriptions of cluster state"],"categories":["tool-use-integration","kubernetes-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metoro__cap_1","uri":"capability://search.retrieval.metoro.metrics.and.alerts.retrieval","name":"metoro-metrics-and-alerts-retrieval","description":"Fetches real-time and historical metrics, alerts, and health status from Metoro's monitoring backend for Kubernetes workloads, exposing them as MCP resources that LLM agents can query to understand performance, anomalies, and operational issues. Implements resource handlers that translate Metoro API metric endpoints into structured JSON, enabling agents to correlate metrics with cluster state for intelligent troubleshooting.","intents":["Retrieve current CPU, memory, and network metrics for pods and deployments","Query active alerts and incidents from Metoro's monitoring system","Access historical metric data to analyze trends and performance patterns","Enable an AI agent to diagnose performance issues by correlating metrics with cluster state"],"best_for":["SRE teams building AI-assisted incident response and root cause analysis workflows","Platform engineers creating intelligent alerting and auto-remediation agents","DevOps teams integrating LLM-based performance analysis into their observability stack"],"limitations":["Metric granularity and retention depend on Metoro's data collection and storage policies","No direct control over metric collection intervals or custom metric definitions through MCP","Requires Metoro subscription and active monitoring of target resources","Alert query capabilities limited to what Metoro's API exposes — custom alert rules may not be queryable"],"requires":["Active Metoro monitoring account with API access","Metoro API credentials with metrics and alerts read permissions","Target Kubernetes resources already onboarded and monitored by Metoro","Network connectivity from MCP server to Metoro API endpoints"],"input_types":["MCP resource request with metric/alert query parameters","Time range specifications (start/end timestamps or relative durations)","Resource identifiers (pod names, deployment names, namespace)"],"output_types":["JSON metric data with timestamps and values","Alert objects with severity, status, and description","Aggregated health summaries and anomaly indicators"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metoro__cap_2","uri":"capability://tool.use.integration.mcp.resource.schema.mapping.for.kubernetes.objects","name":"mcp-resource-schema-mapping-for-kubernetes-objects","description":"Implements MCP resource type definitions and schema mappings that translate Kubernetes API objects (pods, deployments, services, etc.) into MCP-compatible resource representations with standardized naming conventions and hierarchical URIs. Uses MCP's resource protocol to expose Kubernetes objects as queryable, typed resources with consistent interfaces, enabling LLM agents to discover and interact with cluster resources through standard MCP patterns.","intents":["Discover available Kubernetes resource types and their properties through MCP resource listing","Query specific Kubernetes objects using MCP resource URIs with semantic naming","Enable LLM agents to understand Kubernetes object schemas and relationships","Provide consistent, language-model-friendly representations of Kubernetes state"],"best_for":["LLM application developers building Kubernetes-aware agents with MCP","Teams standardizing on MCP for infrastructure automation and observability","Developers creating multi-tool agents that need consistent resource abstraction across systems"],"limitations":["Schema mapping is static and defined at server startup — dynamic CRD discovery may be limited","Resource URI naming conventions may not match existing Kubernetes naming patterns, requiring agent adaptation","Nested resource hierarchies (e.g., containers within pods) may have depth limitations in MCP representation","Schema versioning tied to Metoro server version — schema changes require server restart"],"requires":["MCP client library or implementation compatible with resource protocol","Understanding of MCP resource URI conventions and resource listing patterns","Metoro MCP server running and accessible to the client"],"input_types":["MCP resource list requests (with optional type filters)","MCP resource read requests with URIs"],"output_types":["MCP resource descriptions with schema information","Structured Kubernetes object representations","Resource type catalogs and hierarchies"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metoro__cap_3","uri":"capability://search.retrieval.kubernetes.namespace.and.workload.filtering","name":"kubernetes-namespace-and-workload-filtering","description":"Enables MCP resource queries to be scoped and filtered by Kubernetes namespace, resource type, labels, and other selectors, allowing agents to narrow queries to specific workloads or environments. Implements filtering logic in resource handlers that applies Kubernetes-native selectors (label queries, namespace filters) before returning results, reducing result set size and enabling targeted queries.","intents":["Query pods or deployments in a specific namespace without retrieving cluster-wide results","Filter resources by labels to target specific application tiers or environments","Retrieve only resources matching specific criteria (e.g., pods with failed status)","Enable agents to scope operations to specific namespaces or workload groups"],"best_for":["Multi-tenant Kubernetes environments where agents need namespace isolation","Teams with large clusters requiring efficient, targeted queries","Agents performing namespace-specific operations or diagnostics"],"limitations":["Filtering performance depends on the number of resources in the cluster — large clusters may have query latency","Label selector syntax must match Kubernetes conventions; complex selectors may not be fully supported","No regex or advanced query syntax — limited to Kubernetes-native label and field selectors","Filtering is applied server-side; no client-side filtering capability"],"requires":["Knowledge of target namespace names and label conventions","Metoro monitoring coverage of the target namespaces","MCP client capable of passing query parameters in resource requests"],"input_types":["Namespace name (string)","Resource type (string)","Label selectors (Kubernetes label query syntax)","Field selectors (Kubernetes field query syntax)"],"output_types":["Filtered list of Kubernetes resources","Count of matching resources","Aggregated metrics for filtered resource sets"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metoro__cap_4","uri":"capability://tool.use.integration.mcp.tool.calling.for.kubernetes.operations","name":"mcp-tool-calling-for-kubernetes-operations","description":"Exposes Kubernetes operations (e.g., describe pod, get logs, check deployment status) as MCP tools that LLM agents can invoke through the MCP tool-calling protocol. Implements tool definitions with input schemas and handlers that translate tool calls into Metoro API requests or Kubernetes queries, enabling agents to perform structured operations on cluster resources with type-safe parameters.","intents":["Invoke structured operations on Kubernetes resources from an LLM agent","Get detailed pod descriptions, logs, or event information through tool calls","Check deployment rollout status or replica counts","Enable agents to perform multi-step Kubernetes diagnostics through tool composition"],"best_for":["LLM agents performing Kubernetes troubleshooting and diagnostics","Developers building AI-assisted deployment and operational workflows","Teams using Claude or other MCP-compatible LLMs for infrastructure automation"],"limitations":["Tool set is predefined and fixed at server startup — no dynamic tool registration","Mutation operations (apply, delete, patch) are not supported — read-only diagnostics only","Tool execution latency depends on Metoro API response times; not suitable for real-time streaming","Error handling and retry logic are basic — complex failure scenarios may require agent-side handling"],"requires":["MCP client with tool-calling support (e.g., Claude with MCP integration)","Metoro API credentials with appropriate permissions for the operations","Target Kubernetes resources accessible through Metoro monitoring"],"input_types":["Tool call requests with JSON parameters","Resource identifiers (pod name, deployment name, namespace)","Optional parameters (log lines, time ranges)"],"output_types":["Tool execution results (JSON or text)","Kubernetes resource descriptions","Log excerpts and event summaries"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metoro__cap_5","uri":"capability://safety.moderation.metoro.api.authentication.and.credential.management","name":"metoro-api-authentication-and-credential-management","description":"Handles authentication with Metoro's backend API using API keys or tokens, managing credential lifecycle and request signing for all MCP resource and tool operations. Implements credential storage (environment variables, config files) and request middleware that injects authentication headers into Metoro API calls, abstracting authentication complexity from MCP clients.","intents":["Authenticate the MCP server with Metoro's API using provided credentials","Manage API key lifecycle and rotation without restarting the server","Ensure all Metoro API requests are properly authenticated and authorized","Support multiple authentication methods (API keys, tokens) for different deployment scenarios"],"best_for":["DevOps teams deploying Metoro MCP server in production environments","Organizations requiring credential management and rotation policies","Teams integrating Metoro MCP with existing secret management systems"],"limitations":["Credentials must be provided at server startup — no runtime credential updates without restart","No built-in credential rotation or expiration handling — relies on external secret management","Authentication errors are not retried automatically — failed requests fail immediately","No audit logging of authentication attempts or credential usage"],"requires":["Valid Metoro API credentials (API key or authentication token)","Environment variable or config file for credential storage","Network connectivity to Metoro API endpoints"],"input_types":["API key or authentication token (string)","Credential source (environment variable, config file)"],"output_types":["Authenticated HTTP requests to Metoro API","Authentication status and error messages"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Kubernetes cluster with Metoro monitoring agent installed and running","Metoro API credentials (API key or authentication token)","MCP-compatible client (Claude Desktop, or custom MCP client implementation)","Network connectivity from MCP server to Metoro backend API","Active Metoro monitoring account with API access","Metoro API credentials with metrics and alerts read permissions","Target Kubernetes resources already onboarded and monitored by Metoro","Network connectivity from MCP server to Metoro API endpoints","MCP client library or implementation compatible with resource protocol","Understanding of MCP resource URI conventions and resource listing patterns"],"failure_modes":["Read-only access to cluster state — no mutation capabilities through this interface","Requires Metoro monitoring agent already deployed and configured in the target cluster","Latency depends on Metoro backend API response times; not suitable for real-time streaming use cases","Limited to resources and metrics that Metoro's monitoring agent collects — custom CRDs may not be exposed","Metric granularity and retention depend on Metoro's data collection and storage policies","No direct control over metric collection intervals or custom metric definitions through MCP","Requires Metoro subscription and active monitoring of target resources","Alert query capabilities limited to what Metoro's API exposes — custom alert rules may not be queryable","Schema mapping is static and defined at server startup — dynamic CRD discovery may be limited","Resource URI naming conventions may not match existing Kubernetes naming patterns, requiring agent adaptation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"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":"2026-06-17T09:51:03.578Z","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=metoro","compare_url":"https://unfragile.ai/compare?artifact=metoro"}},"signature":"clG52Ls19yBz2mTdPlgeSQw/OF6YxSEIQ4P0j36wztbiT0q4IZjJea1du9PwtfIizuk8LDR32uURkGZdMNByCQ==","signedAt":"2026-06-20T21:43:51.049Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/metoro","artifact":"https://unfragile.ai/metoro","verify":"https://unfragile.ai/api/v1/verify?slug=metoro","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"}}