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Implements MCP's tool-calling interface to expose Datadog's metric query endpoint, handling authentication via API key/app key pairs and returning time-series data with timestamps and aggregated values.","intents":["Query historical metrics from Datadog without writing direct API code","Retrieve time-series data for specific hosts, services, or custom metrics","Integrate Datadog metric retrieval into LLM-driven monitoring workflows","Build dashboards or reports by fetching raw metric data programmatically"],"best_for":["DevOps engineers building AI-assisted monitoring dashboards","SRE teams automating incident investigation with LLM agents","Platform teams integrating Datadog into internal AI tools"],"limitations":["Query complexity limited by Datadog API rate limits (default 300 requests/minute)","No built-in query result caching — repeated queries hit API each time","Time range queries require explicit start/end timestamps; no relative time parsing","Aggregation functions depend on Datadog metric type; custom rollups not supported"],"requires":["Datadog API key (DD_API_KEY environment variable)","Datadog application key (DD_APP_KEY environment variable)","Datadog account with metric data available","MCP client compatible with Node.js tool invocation"],"input_types":["metric query string (e.g., 'avg:system.cpu{host:prod}')","timestamp integers (Unix epoch seconds)","aggregation parameters (avg, sum, max, min, count)"],"output_types":["JSON time-series data with timestamp and value pairs","Aggregated metric values with metadata"],"categories":["tool-use-integration","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-winor30-mcp-server-datadog__cap_1","uri":"capability://tool.use.integration.datadog.event.creation.and.search.via.mcp.tools","name":"datadog event creation and search via mcp tools","description":"Creates custom events in Datadog and searches existing events through MCP tool invocation, translating event metadata (title, text, tags, priority) into Datadog API calls. 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Enables LLM agents to inspect which monitors are triggered, their thresholds, and associated metadata without direct API knowledge.","intents":["Check which monitors are currently alerting to understand active incidents","Retrieve monitor thresholds and conditions for incident triage","List all monitors in a specific environment or with specific tags","Correlate monitor alerts with metric data during root cause analysis"],"best_for":["On-call engineers using AI assistants for incident triage","Automated incident response systems that need monitor context","SRE teams building intelligent alert routing based on monitor metadata"],"limitations":["Monitor state is point-in-time; no historical state transitions exposed","Bulk monitor retrieval limited to 1000 monitors per request","Monitor edit/update operations not exposed — read-only in current implementation","Custom monitor types (composite, log-based) may have incomplete metadata"],"requires":["Datadog API key (DD_API_KEY environment variable)","Datadog application key (DD_APP_KEY environment variable)","Monitor IDs or tag filters to query","MCP client with tool invocation"],"input_types":["monitor ID (integer)","tag filters (key:value format)","monitor type filter (metric, log, apm, etc.)"],"output_types":["JSON monitor object with definition, thresholds, and current state","Array of monitor objects with alert status"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-winor30-mcp-server-datadog__cap_3","uri":"capability://tool.use.integration.datadog.host.and.infrastructure.inventory.retrieval","name":"datadog host and infrastructure inventory retrieval","description":"Retrieves host inventory, infrastructure metadata, and system information from Datadog through MCP tools, translating host queries into API calls that return host tags, metrics availability, and system details. Enables LLM agents to understand infrastructure topology and correlate hosts with metrics or alerts.","intents":["List all hosts in a specific environment or with specific tags","Retrieve host metadata (OS, agent version, installed integrations) for context","Correlate host information with metric queries for targeted monitoring","Understand infrastructure topology for incident impact assessment"],"best_for":["SRE teams automating infrastructure discovery for incident response","Platform teams building AI-driven capacity planning tools","DevOps engineers investigating host-level issues with AI assistance"],"limitations":["Host data is eventually consistent; newly added hosts may not appear immediately","Host list limited to 1000 results per query; no pagination exposed","Custom host attributes not queryable — only standard Datadog tags","Host status reflects agent connectivity; offline hosts may have stale metadata"],"requires":["Datadog API key (DD_API_KEY environment variable)","Datadog application key (DD_APP_KEY environment variable)","Datadog agent running on hosts to be discovered","MCP client with tool invocation"],"input_types":["host name or hostname pattern","tag filters (key:value format)","environment filter (prod, staging, dev)"],"output_types":["JSON host object with metadata, tags, and metrics","Array of host objects with agent status"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-winor30-mcp-server-datadog__cap_4","uri":"capability://tool.use.integration.mcp.protocol.server.implementation.for.datadog.api.abstraction","name":"mcp protocol server implementation for datadog api abstraction","description":"Implements a Model Context Protocol (MCP) server that exposes Datadog API capabilities as standardized tools, handling MCP message serialization, authentication token management, and error handling. Routes incoming MCP tool calls to appropriate Datadog API endpoints, manages session state, and returns structured responses compatible with MCP clients (Claude, LLM agents, etc.).","intents":["Enable any MCP-compatible client to query Datadog without custom integration code","Standardize Datadog API access through MCP's tool-calling interface","Simplify authentication by centralizing API key management in the MCP server","Build composable Datadog workflows by chaining MCP tool calls"],"best_for":["Teams standardizing on MCP for tool integration across multiple services","LLM application developers who want Datadog access without SDK boilerplate","Organizations building internal AI platforms with pluggable integrations"],"limitations":["MCP server must be running as a separate process; adds deployment complexity","Tool definitions are static at server startup; dynamic tool registration not supported","Error handling relies on MCP error format; Datadog-specific error details may be lost","No built-in rate limiting — depends on Datadog API quotas and client behavior"],"requires":["Node.js 16+ runtime","MCP SDK (mcp package)","Datadog API credentials (DD_API_KEY, DD_APP_KEY)","MCP-compatible client (Claude, custom LLM agent, etc.)"],"input_types":["MCP tool call messages with JSON parameters","MCP initialization handshake"],"output_types":["MCP tool result messages with JSON data","MCP error messages for failed calls"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-winor30-mcp-server-datadog__cap_5","uri":"capability://safety.moderation.datadog.api.authentication.and.credential.management","name":"datadog api authentication and credential management","description":"Manages Datadog API authentication by reading API key and application key from environment variables, constructing authenticated HTTP requests with proper headers, and handling authentication failures gracefully. Implements credential validation at server startup and includes error handling for missing or invalid credentials.","intents":["Securely pass Datadog credentials to the MCP server without hardcoding","Validate credentials at startup to fail fast if authentication is misconfigured","Automatically include authentication headers in all Datadog API requests","Handle authentication errors and provide clear error messages to clients"],"best_for":["DevOps teams deploying MCP servers in containerized environments","Security-conscious organizations requiring environment-based credential management","Teams using CI/CD pipelines that inject secrets via environment variables"],"limitations":["Credentials must be set as environment variables; no support for credential files or vaults","No credential rotation mechanism — requires server restart to update credentials","API key and app key stored in process memory; no encryption at rest","No audit logging of authentication attempts or API calls"],"requires":["DD_API_KEY environment variable set with valid Datadog API key","DD_APP_KEY environment variable set with valid Datadog application key","Datadog account with API access enabled","Network access to Datadog API endpoints (api.datadoghq.com or regional variant)"],"input_types":["environment variable strings (DD_API_KEY, DD_APP_KEY)"],"output_types":["HTTP Authorization headers for Datadog API requests","Authentication validation status (success/failure)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-winor30-mcp-server-datadog__cap_6","uri":"capability://safety.moderation.datadog.api.error.handling.and.response.normalization","name":"datadog api error handling and response normalization","description":"Intercepts Datadog API responses, normalizes error formats into MCP-compatible error messages, and handles rate limiting, authentication failures, and malformed responses. Translates Datadog-specific error codes and messages into structured errors that MCP clients can understand and act upon.","intents":["Provide clear error messages when Datadog API calls fail","Handle rate limiting gracefully without crashing the MCP server","Distinguish between client errors (bad query) and server errors (Datadog outage)","Enable LLM agents to retry or adjust queries based on specific error types"],"best_for":["Robust MCP server deployments that need fault tolerance","LLM agents that need to handle API failures intelligently","Teams debugging integration issues between MCP clients and Datadog"],"limitations":["Error details are limited by Datadog API response format; some context may be lost","No automatic retry logic — clients must implement their own retry strategies","Rate limit headers are parsed but not exposed to clients for backoff calculation","Timeout handling depends on HTTP client configuration; no built-in timeout management"],"requires":["HTTP client library with error handling (built into Node.js)","Knowledge of Datadog API error codes and response formats","MCP client capable of handling error messages"],"input_types":["HTTP response status codes from Datadog API","JSON error payloads from Datadog API","HTTP headers (rate limit, retry-after)"],"output_types":["MCP error messages with error code and description","Normalized error objects for client-side handling"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Datadog API key (DD_API_KEY environment variable)","Datadog application key (DD_APP_KEY environment variable)","Datadog account with metric data available","MCP client compatible with Node.js tool invocation","Event title and text fields (required for creation)","MCP client with tool invocation support","Monitor IDs or tag filters to query","MCP client with tool invocation","Datadog agent running on hosts to be discovered","Node.js 16+ runtime"],"failure_modes":["Query complexity limited by Datadog API rate limits (default 300 requests/minute)","No built-in query result caching — repeated queries hit API each time","Time range queries require explicit start/end timestamps; no relative time parsing","Aggregation functions depend on Datadog metric type; custom rollups not supported","Event creation requires explicit timestamp; no automatic 'now' inference","Event search limited to 100 results per query; pagination not exposed","Custom event aggregation not supported — only basic tag-based filtering","Event retention depends on Datadog plan; older events may be unavailable","Monitor state is point-in-time; no historical state transitions exposed","Bulk monitor retrieval limited to 1000 monitors per request","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4978059736137633,"quality":0.24,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":15417,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=winor30-mcp-server-datadog","compare_url":"https://unfragile.ai/compare?artifact=winor30-mcp-server-datadog"}},"signature":"tqXIgdap+HwY3IFh1jpV4vKjFPGNSrQ8X8eFCKl0DlBCp4K5HxW8B1FMyMAleoaRDEjWAWd4UHD2RBFnL4eiCw==","signedAt":"2026-06-22T01:52:16.983Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/winor30-mcp-server-datadog","artifact":"https://unfragile.ai/winor30-mcp-server-datadog","verify":"https://unfragile.ai/api/v1/verify?slug=winor30-mcp-server-datadog","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"}}