{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-grafana","slug":"grafana","name":"Grafana","type":"mcp","url":"https://github.com/grafana/mcp-grafana","page_url":"https://unfragile.ai/grafana","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-grafana__cap_0","uri":"capability://tool.use.integration.mcp.protocol.bridging.to.grafana.apis","name":"mcp protocol bridging to grafana apis","description":"Implements the Model Context Protocol (MCP) server specification using the mark3labs/mcp-go framework, translating standardized MCP tool invocations into native Grafana REST API calls. The server exposes 20+ tool categories through a unified MCP interface, handling request/response marshaling, error translation, and protocol-level session management across stdio, SSE, and HTTP transports.","intents":["Enable AI assistants to interact with Grafana through a standardized protocol without custom integrations","Expose Grafana capabilities as composable tools that LLM agents can discover and invoke","Support multiple transport mechanisms for different deployment contexts (local CLI, cloud services, web clients)"],"best_for":["AI assistant developers integrating Grafana observability into Claude Desktop or other MCP clients","Teams building LLM-powered incident response workflows","Organizations standardizing on MCP for tool integration across their observability stack"],"limitations":["Protocol overhead adds ~50-100ms per tool invocation due to MCP serialization and deserialization","Tool discovery is static per session — dynamic tool registration requires server restart","No built-in request batching — multiple sequential queries generate separate MCP calls"],"requires":["MCP-compatible client (Claude Desktop, custom MCP client, or compatible AI assistant)","Go 1.19+ for building from source","Network connectivity to target Grafana instance"],"input_types":["MCP tool invocation JSON with typed parameters","Query strings, time ranges, dashboard IDs, datasource names"],"output_types":["Structured JSON responses matching MCP tool output schemas","Dashboard metadata, query results, alert definitions, incident data"],"categories":["tool-use-integration","protocol-implementation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_1","uri":"capability://automation.workflow.multi.transport.server.deployment.stdio.sse.http","name":"multi-transport server deployment (stdio, sse, http)","description":"Supports three distinct transport modes configured at startup: stdio for direct process integration with local clients, Server-Sent Events (SSE) for unidirectional streaming over HTTP, and streamable-HTTP for bidirectional communication. Each transport is implemented as a separate handler in cmd/mcp-grafana/main.go with transport-agnostic tool execution logic, enabling the same server binary to serve different deployment architectures without modification.","intents":["Deploy mcp-grafana as a local subprocess for Claude Desktop without network exposure","Run mcp-grafana as a cloud service accessible to multiple remote MCP clients via HTTP","Support both synchronous request-response and streaming response patterns depending on client capabilities"],"best_for":["Development teams using Claude Desktop locally with stdio transport","SaaS platforms offering Grafana integration as a managed service via HTTP","Enterprises requiring both local and remote deployment options from a single codebase"],"limitations":["Stdio transport limited to single client per process instance — requires separate processes for multi-user scenarios","SSE transport is unidirectional (server-to-client only) — requires separate HTTP channel for client requests","HTTP transports require TLS configuration for production; plaintext HTTP not recommended for sensitive observability data"],"requires":["Go 1.19+ for building","For stdio: direct process spawning capability in client","For HTTP/SSE: network connectivity and optional TLS certificates","Port availability (default 8080 for HTTP transports)"],"input_types":["Process stdin (stdio mode)","HTTP POST requests with MCP protocol payloads (HTTP modes)","SSE stream subscriptions (SSE mode)"],"output_types":["Process stdout (stdio mode)","HTTP response bodies (HTTP modes)","SSE event streams (SSE mode)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_10","uri":"capability://automation.workflow.prometheus.metrics.export.for.mcp.grafana.monitoring","name":"prometheus metrics export for mcp-grafana monitoring","description":"Exposes Prometheus metrics from mcp-grafana itself, tracking tool invocation counts, execution latencies, error rates, and API call performance. Implements a /metrics endpoint (Prometheus format) that exports metrics like tool_invocations_total, tool_execution_duration_seconds, grafana_api_calls_total, and datasource_query_errors. Enables operators to monitor mcp-grafana's health and performance through Grafana dashboards, alerting on high error rates or slow tool execution.","intents":["Monitor mcp-grafana's tool execution performance and error rates","Alert on degraded performance or high error rates in tool invocations","Track usage patterns of different tools and datasources"],"best_for":["Operations teams running mcp-grafana in production needing health monitoring","SaaS platforms offering mcp-grafana as a service and needing usage metrics","Organizations with Prometheus-based monitoring wanting to integrate mcp-grafana metrics"],"limitations":["Metrics are in-memory — reset on server restart; no persistence","High-cardinality labels (e.g., per-user metrics) may cause Prometheus scrape performance issues","Metrics endpoint must be exposed and scraped by Prometheus — requires separate monitoring configuration","No built-in metrics retention — Prometheus retention policy applies"],"requires":["Prometheus scraper configured to scrape /metrics endpoint","Network access to mcp-grafana's metrics endpoint (default port 8080)"],"input_types":["Prometheus scrape configuration pointing to mcp-grafana /metrics endpoint"],"output_types":["Prometheus metrics in text format: tool_invocations_total, tool_execution_duration_seconds, grafana_api_calls_total, datasource_query_errors, etc."],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_11","uri":"capability://tool.use.integration.tool.discovery.and.dynamic.schema.generation","name":"tool discovery and dynamic schema generation","description":"Implements automatic tool discovery that generates MCP tool schemas dynamically based on Grafana's API capabilities and configured datasources. The tool management framework introspects Grafana's /api/datasources, /api/v1/rules, and other endpoints to determine available tools, then generates MCP-compliant tool schemas with typed parameters, descriptions, and validation rules. Clients discover available tools via MCP's ListTools mechanism, receiving only tools applicable to their session's Grafana instance and permissions.","intents":["Automatically expose available tools based on Grafana instance configuration without manual tool registration","Enable clients to discover which datasources and tools are available before invoking them","Adapt tool availability based on user permissions and Grafana organization"],"best_for":["Multi-tenant deployments where different organizations have different datasources and tools","Dynamic Grafana environments where datasources are frequently added/removed","AI assistants that need to discover available tools before constructing queries"],"limitations":["Tool discovery requires API calls to Grafana — adds latency to ListTools responses","Schema generation is static per session — changes to Grafana configuration require new session","Tool schemas are generated from Grafana API introspection — may not capture all datasource-specific features","No caching of tool schemas — repeated ListTools calls re-introspect Grafana API"],"requires":["Grafana API key with sufficient permissions to introspect datasources and rules","Grafana instance accessible from mcp-grafana"],"input_types":["MCP ListTools request (no parameters)"],"output_types":["Array of MCP tool definitions: {name, description, inputSchema}"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_12","uri":"capability://safety.moderation.authentication.and.api.key.management.with.rbac","name":"authentication and api key management with rbac","description":"Manages Grafana authentication through API keys provided per session, enforcing role-based access control (RBAC) inherited from Grafana's permission model. Validates API keys against Grafana's /api/auth/identity endpoint, caches authentication state per session, and enforces Grafana's datasource and dashboard permissions on all tool invocations. Supports multiple authentication methods (API keys, OAuth tokens) and propagates Grafana's RBAC decisions to MCP tool execution, ensuring users can only query resources they have permission to access.","intents":["Authenticate MCP clients using Grafana API keys without exposing credentials to clients","Enforce Grafana's RBAC on all tool invocations, preventing unauthorized data access","Support multiple authentication methods (API keys, OAuth) for different deployment scenarios"],"best_for":["Multi-tenant deployments requiring strict access control per user/organization","Enterprises with existing Grafana RBAC policies wanting to enforce them through MCP","Deployments where different users should access different datasources or dashboards"],"limitations":["Authentication is per-session — no automatic token refresh; expired tokens require new session","RBAC enforcement relies on Grafana's permission model — mcp-grafana cannot grant additional permissions","API key validation adds ~50-100ms latency per session initialization","No built-in API key rotation — key management must be handled externally"],"requires":["Grafana API key with appropriate permissions for intended tool usage","Grafana RBAC configured with datasource and dashboard permissions","Network connectivity to Grafana's /api/auth/identity endpoint"],"input_types":["Grafana API key (Bearer token format)","Optional organization ID for multi-org Grafana instances"],"output_types":["Authentication result: {userId, login, role, permissions, organizations}"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_13","uri":"capability://safety.moderation.tls.https.configuration.for.secure.http.transport","name":"tls/https configuration for secure http transport","description":"Supports TLS encryption for HTTP and SSE transports through configurable certificate and key files. Implements standard Go TLS server configuration with support for custom CA certificates, client certificate validation, and TLS version pinning. Enables secure communication between MCP clients and mcp-grafana server, protecting API keys and query results in transit. Configuration is provided via environment variables or command-line flags at server startup.","intents":["Encrypt communication between remote MCP clients and mcp-grafana server","Protect Grafana API keys and sensitive query results from network eavesdropping","Enable production-grade HTTPS deployments with certificate management"],"best_for":["Production deployments of mcp-grafana exposed over the internet","Enterprises requiring encrypted communication for compliance (HIPAA, PCI-DSS, etc.)","Cloud deployments where network traffic may traverse untrusted networks"],"limitations":["TLS adds ~5-10ms latency per connection due to handshake overhead","Certificate management must be handled externally — no automatic certificate renewal","Self-signed certificates require client-side CA configuration — not suitable for public deployments","TLS is only for mcp-grafana transport — Grafana API communication may still be unencrypted if Grafana instance is HTTP-only"],"requires":["TLS certificate file (PEM format)","TLS private key file (PEM format)","Optional CA certificate for client certificate validation","Port availability for HTTPS (default 8443)"],"input_types":["Environment variables: TLS_CERT_FILE, TLS_KEY_FILE, TLS_CA_FILE","Command-line flags: --tls-cert, --tls-key, --tls-ca"],"output_types":["HTTPS server listening on configured port with TLS encryption"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_14","uri":"capability://memory.knowledge.context.window.management.and.token.usage.tracking","name":"context window management and token usage tracking","description":"Implements context window awareness for LLM interactions by tracking token usage across tool invocations and providing token budgeting information to clients. Monitors query result sizes and estimates token consumption based on response content, enabling AI assistants to make informed decisions about query scope and result pagination. Provides token usage metrics through OpenTelemetry spans and Prometheus metrics, allowing operators to track and optimize token consumption.","intents":["Help AI assistants understand token cost of queries before executing them","Limit query result sizes to fit within LLM context windows","Track token usage across multiple tool invocations for billing or optimization"],"best_for":["AI-powered incident response systems with limited context windows","SaaS platforms offering mcp-grafana as a service and tracking token usage for billing","Teams optimizing LLM costs by understanding token consumption patterns"],"limitations":["Token estimation is approximate — actual token count depends on LLM tokenizer","Large result sets may exceed context windows — requires client-side pagination","No automatic result truncation — clients must implement pagination logic","Token tracking adds overhead to tool execution — requires result size measurement"],"requires":["OpenTelemetry or Prometheus configured to track token metrics","Client capability to handle token usage information in tool responses"],"input_types":["Query parameters with optional result limit hints"],"output_types":["Tool responses with estimated token count in metadata","Prometheus metrics: estimated_tokens_per_query, result_size_bytes"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_15","uri":"capability://safety.moderation.read.only.deployment.mode.for.restricted.access","name":"read-only deployment mode for restricted access","description":"Supports read-only deployment mode that disables all write operations and restricts tool invocations to query-only capabilities. Implements permission checks that prevent dashboard modifications, alert rule changes, and incident updates, exposing only tools for querying dashboards, datasources, alerts, and logs. Configuration is enforced at the tool execution layer, ensuring read-only semantics are maintained across all transport modes and authentication contexts.","intents":["Deploy mcp-grafana in restricted environments where write access is not permitted","Provide read-only access to observability data for external AI assistants or partners","Enforce least-privilege access by disabling write operations in production"],"best_for":["Production deployments where write operations should be prevented","Sharing mcp-grafana access with external partners or AI services with restricted permissions","Compliance scenarios requiring audit trails and preventing unauthorized modifications"],"limitations":["Read-only mode is enforced at tool level — does not prevent Grafana API write operations if client has direct access","No granular write restrictions — all write operations are disabled; cannot selectively allow specific modifications","Read-only mode must be configured at server startup — cannot be changed per-session"],"requires":["Configuration flag or environment variable to enable read-only mode","Grafana API key with read-only permissions (recommended for defense-in-depth)"],"input_types":["Environment variable: READ_ONLY_MODE=true"],"output_types":["Tool invocations return error if attempting write operations in read-only mode"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_16","uri":"capability://data.processing.analysis.grafana.sift.and.asserts.integration.for.advanced.analytics","name":"grafana sift and asserts integration for advanced analytics","description":"Integrates Grafana Sift (AI-powered log analysis) and Asserts (SLO and error tracking) through dedicated MCP tools that expose their REST APIs. Implements tools for Sift pattern detection, anomaly analysis, and log summarization, as well as Asserts SLO tracking, error rate analysis, and service health assessment. Enables AI assistants to leverage Grafana's advanced analytics capabilities for root cause analysis and service reliability insights.","intents":["Use Sift's AI-powered log analysis to detect patterns and anomalies in logs","Query Asserts for SLO compliance and error rate trends","Correlate Sift insights with Asserts SLO data for root cause analysis"],"best_for":["Organizations using Grafana Sift and Asserts for advanced observability analytics","AI-powered incident response systems leveraging Sift's pattern detection","SRE teams using Asserts for SLO tracking and error analysis"],"limitations":["Requires Grafana Sift and Asserts subscriptions — not available in open-source Grafana","Sift pattern detection may have latency — analysis is asynchronous","Asserts SLO data is limited by retention configuration","No Sift or Asserts configuration through MCP — read-only access to analysis results"],"requires":["Grafana Sift and Asserts configured and integrated with Grafana instance","Grafana API key with Sift and Asserts permissions","Active Sift and Asserts subscriptions"],"input_types":["Log query for Sift analysis","Service or SLO name for Asserts queries"],"output_types":["Sift analysis: patterns, anomalies, summaries","Asserts data: SLO compliance, error rates, service health"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_17","uri":"capability://data.processing.analysis.pyroscope.profiling.data.querying.and.analysis","name":"pyroscope profiling data querying and analysis","description":"Dedicated tool set for Pyroscope profiling data querying that handles profile selection by service/app, time range filtering, and flame graph data retrieval. Implements tools for querying CPU profiles, memory profiles, and goroutine profiles with support for profile comparison and diff analysis. Enables AI assistants to investigate performance issues by querying profiling data, identifying hot functions, and correlating profile changes with system events.","intents":["Query CPU profiles to identify hot functions and performance bottlenecks","Analyze memory profiles to detect memory leaks or excessive allocations","Compare profiles across time windows to identify performance regressions"],"best_for":["Performance engineering teams investigating application bottlenecks","AI-powered performance analysis systems that correlate profiles with metrics","Incident response workflows analyzing performance degradation"],"limitations":["Profile data is limited by Pyroscope retention — historical profiles beyond retention are unavailable","Profile comparison requires profiles from different time windows — may not be available for recent issues","Flame graph data is large — may exceed context windows for detailed analysis","Profile sampling may miss infrequent but expensive operations"],"requires":["Pyroscope datasource configured in Grafana","Grafana API key with Pyroscope datasource query permissions","Applications instrumented with Pyroscope profiling"],"input_types":["Service/app name","Profile type: 'cpu', 'memory', 'goroutine'","Time range for profile query"],"output_types":["Flame graph data: {name, value, children}","Profile summary: {totalSamples, topFunctions, hotPath}"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_2","uri":"capability://search.retrieval.dashboard.search.and.metadata.retrieval","name":"dashboard search and metadata retrieval","description":"Exposes Grafana dashboard discovery through MCP tools that query the Grafana API's /api/search endpoint with support for filtering by tags, folder, starred status, and query strings. Returns structured dashboard metadata including UID, title, folder path, tags, and permissions, enabling AI assistants to locate relevant dashboards for incident investigation or metric exploration without manual navigation.","intents":["Find dashboards related to a specific service or incident by tag or name search","Retrieve dashboard UIDs and metadata for subsequent visualization or alert queries","Discover starred or frequently-used dashboards for quick access in incident response workflows"],"best_for":["Incident response teams using AI assistants to quickly locate relevant monitoring dashboards","On-call engineers investigating issues across multiple services with tag-based dashboard organization","Automation workflows that need to programmatically discover dashboards before querying their data"],"limitations":["Search is limited to dashboard metadata — does not index panel contents or query definitions","Folder-based filtering requires exact folder path matching; no fuzzy folder search","Results are paginated with default limit of 100 dashboards — large organizations may need multiple queries"],"requires":["Grafana API access with read permissions on dashboards","Grafana API key or OAuth token with 'dashboards:read' scope","Dashboards must be tagged or named with searchable keywords"],"input_types":["Search query string (e.g., 'kubernetes', 'payment-service')","Filter parameters: tags (array), folder (string), starred (boolean)"],"output_types":["Array of dashboard objects with: id, uid, title, tags, folderTitle, isStarred, url"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_3","uri":"capability://data.processing.analysis.datasource.querying.with.multi.backend.support","name":"datasource querying with multi-backend support","description":"Provides unified query execution across Grafana's supported datasources (Prometheus, Loki, Pyroscope, Elasticsearch, InfluxDB, etc.) through datasource-specific tool implementations. Each datasource type has dedicated tools that translate MCP query parameters into native query languages (PromQL, LogQL, etc.), execute via Grafana's /api/datasources/proxy endpoint, and return typed results. The tool management framework automatically discovers available datasources and exposes only compatible query tools based on datasource type.","intents":["Execute Prometheus queries (PromQL) to retrieve metrics without direct Prometheus access","Query logs from Loki using LogQL syntax through Grafana's unified interface","Run trace queries against Pyroscope for profiling data analysis","Perform multi-datasource queries for correlated metric and log analysis"],"best_for":["Incident response teams needing to query multiple observability backends through a single interface","AI-powered monitoring assistants that need to correlate metrics, logs, and traces","Organizations with Grafana as the central observability hub, avoiding direct datasource access"],"limitations":["Query execution is synchronous — long-running queries (>30s) may timeout depending on Grafana configuration","Datasource-specific syntax (PromQL, LogQL) must be generated by the AI or provided by the user — no automatic query translation between languages","Proxy queries inherit Grafana's datasource permissions — cannot query datasources the authenticated user lacks access to","No built-in query result caching — repeated identical queries execute against datasources each time"],"requires":["Grafana API key with datasource query permissions","Target datasources configured in Grafana and accessible to the API key's user","Knowledge of datasource-specific query syntax (PromQL for Prometheus, LogQL for Loki, etc.)","Network connectivity from mcp-grafana to Grafana instance"],"input_types":["Datasource UID or name","Query string in datasource-native language (PromQL, LogQL, etc.)","Time range parameters (start, end, step for metrics)"],"output_types":["Time series data (metrics): array of {metric, values: [[timestamp, value]]}","Log entries: array of {timestamp, message, labels}","Trace/profile data: structured profiling results with flame graph data"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_4","uri":"capability://data.processing.analysis.prometheus.specific.metric.querying.and.range.queries","name":"prometheus-specific metric querying and range queries","description":"Dedicated tool set for Prometheus querying that handles PromQL syntax, range vector operations, and time-series aggregation. Implements tools for instant queries (single point-in-time metric values), range queries (time-series data over intervals), and metric metadata retrieval (available metrics, labels, label values). Supports Prometheus-specific features like rate() calculations, aggregations, and histogram quantiles through pass-through PromQL execution.","intents":["Query CPU, memory, and request rate metrics for a service over a time window","Retrieve available metrics and their labels to construct dynamic PromQL queries","Calculate derived metrics like error rates, latency percentiles, and resource utilization"],"best_for":["SRE teams investigating performance degradation using metrics-first analysis","AI-powered alerting systems that need to query historical metrics for anomaly detection","Incident response workflows that correlate metric changes with event timelines"],"limitations":["PromQL syntax must be valid — AI must generate syntactically correct queries or provide user-written PromQL","Range queries are limited by Prometheus retention policy — historical data beyond retention window is unavailable","Cardinality limits in Prometheus may cause queries to fail if label combinations exceed configured thresholds","No automatic query optimization — inefficient PromQL (e.g., high-cardinality joins) may timeout or consume excessive resources"],"requires":["Prometheus datasource configured in Grafana","Grafana API key with Prometheus datasource query permissions","Valid PromQL syntax for range/instant queries","Understanding of Prometheus metric naming conventions and available labels"],"input_types":["PromQL query string (e.g., 'rate(http_requests_total[5m])')","Time range: start timestamp, end timestamp, step (interval for range queries)","Metric name or label matchers for metadata queries"],"output_types":["Instant query: single value with timestamp","Range query: time series array with [[timestamp, value], ...]","Metadata: available metrics list, label names, label values for metric"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_5","uri":"capability://data.processing.analysis.loki.log.querying.with.label.filtering.and.stream.selection","name":"loki log querying with label filtering and stream selection","description":"Dedicated tool set for Loki log querying that handles LogQL syntax, label-based stream selection, and log filtering. Implements tools for log range queries (logs over time intervals), instant log queries, and label/stream metadata retrieval. Supports Loki-specific features like pattern matching, metric extraction from logs, and label value enumeration for dynamic query construction.","intents":["Search application logs for error messages or specific request IDs across services","Retrieve logs for a specific time window filtered by service labels and log levels","Discover available log streams and labels to construct dynamic LogQL queries"],"best_for":["DevOps teams investigating application errors through log analysis during incidents","AI-powered log analysis systems that correlate logs with metrics for root cause analysis","Troubleshooting workflows that need to search logs by labels without knowing exact stream names"],"limitations":["LogQL syntax must be valid — complex pattern matching requires proper LogQL syntax or user input","Log retention is limited by Loki configuration — logs beyond retention period are unavailable","High-cardinality label values may cause performance issues — queries with many unique label combinations may timeout","No automatic log sampling — querying very large log volumes may return incomplete results or timeout"],"requires":["Loki datasource configured in Grafana","Grafana API key with Loki datasource query permissions","Valid LogQL syntax for log queries","Knowledge of available log stream labels and naming conventions"],"input_types":["LogQL query string (e.g., '{job=\"api-server\"} |= \"error\"')","Time range: start timestamp, end timestamp","Label matchers for stream selection"],"output_types":["Log entries: array of {timestamp, message, labels}","Available streams: list of label combinations","Label values: enumeration of values for a specific label"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_6","uri":"capability://search.retrieval.alert.rule.discovery.and.status.querying","name":"alert rule discovery and status querying","description":"Exposes Grafana's alerting system through tools that discover alert rules, retrieve their current state, and query alert history. Implements tools for listing alerts by status (firing, resolved, pending), retrieving alert rule definitions with conditions and notification channels, and querying alert state changes over time. Integrates with Grafana's unified alerting backend (/api/v1/rules endpoint) to provide real-time alert status and historical state transitions.","intents":["Retrieve currently firing alerts to understand active incidents","Query alert rule definitions to understand alert conditions and thresholds","Investigate alert state history to correlate alert changes with system events"],"best_for":["Incident response teams needing real-time alert status during active incidents","AI-powered incident detection systems that need to understand alert rule logic","On-call automation that correlates multiple alerts to identify root causes"],"limitations":["Alert history is limited by Grafana's retention configuration — historical alerts beyond retention are unavailable","Alert rule discovery returns only rules the authenticated user has permission to view","No alert rule modification through MCP — read-only access to alert definitions","Alert state transitions may have latency — real-time state may lag actual system state by seconds"],"requires":["Grafana unified alerting configured (not legacy alerting)","Grafana API key with 'alerts:read' permission","Alert rules defined in Grafana with active evaluation"],"input_types":["Alert status filter: 'firing', 'resolved', 'pending', or all","Optional label matchers to filter alerts by labels","Time range for historical alert queries"],"output_types":["Alert objects: {uid, title, state, labels, annotations, evaluatedAt, lastEvaluationTime}","Alert rule definitions: {uid, title, condition, data, noDataState, execErrState}","Alert state history: array of {timestamp, state, reason}"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_7","uri":"capability://search.retrieval.incident.and.oncall.integration.for.incident.management","name":"incident and oncall integration for incident management","description":"Exposes Grafana OnCall incident management capabilities through MCP tools that retrieve active incidents, incident details, escalation policies, and on-call schedules. Implements tools for querying incident state, retrieving incident timelines with event history, and accessing on-call rotation information. Integrates with Grafana OnCall's REST API to provide incident context during investigation and enable AI assistants to understand incident severity, assigned responders, and escalation status.","intents":["Retrieve active incidents and their current status during incident response","Query incident timeline to understand event sequence and responder actions","Check on-call schedules to identify who should be engaged for escalation"],"best_for":["Incident response teams using Grafana OnCall for incident management","AI-powered incident automation that needs to understand incident context and escalation","On-call coordination workflows that need to query current schedules and incident assignments"],"limitations":["Requires Grafana OnCall subscription — not available in open-source Grafana","Incident history is limited by OnCall retention configuration","No incident creation or modification through MCP — read-only access","On-call schedule queries return only schedules the authenticated user has permission to view"],"requires":["Grafana OnCall configured and integrated with Grafana instance","Grafana API key with OnCall integration permissions","Active incidents or on-call schedules in OnCall"],"input_types":["Incident status filter: 'firing', 'resolved', 'acknowledged'","Optional team or service filter for incident queries","Schedule name or ID for on-call queries"],"output_types":["Incident objects: {id, title, status, severity, assignee, createdAt, updatedAt, timeline}","Incident timeline: array of {timestamp, type, user, message}","On-call schedules: {name, members, rotations, nextRotation}"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_8","uri":"capability://automation.workflow.session.based.multi.tenant.context.management","name":"session-based multi-tenant context management","description":"Implements per-session configuration management through a SessionManager that maintains isolated context for each MCP client connection. Each session stores Grafana instance URL, API key, organization ID, and user preferences, enabling multiple users or clients to connect to different Grafana instances or organizations through a single mcp-grafana server instance. Session context is passed through the MCP tool execution chain, ensuring all queries execute against the correct Grafana instance and organization without cross-contamination.","intents":["Support multiple teams connecting to different Grafana instances through a single mcp-grafana deployment","Enable multi-organization Grafana instances where different users query different organizations","Isolate credentials and context per client connection for security and multi-tenancy"],"best_for":["SaaS platforms offering Grafana integration as a managed service with multiple customers","Enterprise deployments with multiple Grafana instances or organizations","Teams requiring isolated credential management per client connection"],"limitations":["Session state is in-memory — sessions are lost on server restart; no persistence layer","No automatic session expiration — long-lived sessions may accumulate in memory","Session configuration must be provided by client at connection time — no dynamic reconfiguration during session","HTTP transports require session identifier in request headers — adds complexity vs stdio transport"],"requires":["HTTP or SSE transport (stdio transport is single-user, no multi-tenancy)","Client capability to provide Grafana URL and API key per session","Grafana instances accessible from mcp-grafana server"],"input_types":["Session initialization: Grafana URL, API key, organization ID","Session identifier in subsequent requests"],"output_types":["Session context: validated Grafana instance, organization, user permissions"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-grafana__cap_9","uri":"capability://automation.workflow.opentelemetry.tracing.and.observability.instrumentation","name":"opentelemetry tracing and observability instrumentation","description":"Integrates OpenTelemetry tracing throughout mcp-grafana's execution path, instrumenting MCP tool invocations, Grafana API calls, and datasource queries with distributed traces. Exports traces to OpenTelemetry-compatible backends (Jaeger, Tempo, etc.) configured via environment variables. Provides visibility into tool execution latency, API call performance, and error propagation, enabling operators to debug performance issues and monitor mcp-grafana's own observability.","intents":["Monitor mcp-grafana's performance and identify bottlenecks in tool execution","Debug slow queries by tracing execution through Grafana API calls and datasource queries","Correlate mcp-grafana traces with application traces for end-to-end observability"],"best_for":["Operations teams running mcp-grafana in production and needing performance visibility","Debugging complex incident response workflows to identify latency sources","Organizations with existing OpenTelemetry infrastructure wanting to integrate mcp-grafana observability"],"limitations":["Tracing adds ~5-10ms overhead per tool invocation due to span creation and export","Trace export is asynchronous — high-volume tracing may impact memory usage","OpenTelemetry backend must be configured and accessible — missing backend causes trace loss without errors","Trace sampling must be configured to avoid overwhelming backend with high-volume tool invocations"],"requires":["OpenTelemetry SDK configured via environment variables (OTEL_EXPORTER_OTLP_ENDPOINT, etc.)","OpenTelemetry-compatible backend (Jaeger, Tempo, Datadog, etc.)","Network connectivity from mcp-grafana to trace backend"],"input_types":["Environment variables: OTEL_EXPORTER_OTLP_ENDPOINT, OTEL_EXPORTER_OTLP_HEADERS, OTEL_SDK_DISABLED"],"output_types":["OpenTelemetry traces: spans with attributes for tool name, Grafana API endpoint, query type, latency, errors"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude Desktop, custom MCP client, or compatible AI assistant)","Go 1.19+ for building from source","Network connectivity to target Grafana instance","Go 1.19+ for building","For stdio: direct process spawning capability in client","For HTTP/SSE: network connectivity and optional TLS certificates","Port availability (default 8080 for HTTP transports)","Prometheus scraper configured to scrape /metrics endpoint","Network access to mcp-grafana's metrics endpoint (default port 8080)","Grafana API key with sufficient permissions to introspect datasources and rules"],"failure_modes":["Protocol overhead adds ~50-100ms per tool invocation due to MCP serialization and deserialization","Tool discovery is static per session — dynamic tool registration requires server restart","No built-in request batching — multiple sequential queries generate separate MCP calls","Stdio transport limited to single client per process instance — requires separate processes for multi-user scenarios","SSE transport is unidirectional (server-to-client only) — requires separate HTTP channel for client requests","HTTP transports require TLS configuration for production; plaintext HTTP not recommended for sensitive observability data","Metrics are in-memory — reset on server restart; no persistence","High-cardinality labels (e.g., per-user metrics) may cause Prometheus scrape performance issues","Metrics endpoint must be exposed and scraped by Prometheus — requires separate monitoring configuration","No built-in metrics retention — Prometheus retention policy applies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-06-17T09:51:03.041Z","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=grafana","compare_url":"https://unfragile.ai/compare?artifact=grafana"}},"signature":"mXL644bfvqmzp6JhE/l+GEBvrO+z+cJERhN4LqfDqUBNcIO7fgstoOjC5pqoZ9F/G3VqFi8/o5BKmIvSoS9jCg==","signedAt":"2026-06-22T01:51:23.167Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/grafana","artifact":"https://unfragile.ai/grafana","verify":"https://unfragile.ai/api/v1/verify?slug=grafana","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"}}