Morningstar Data MCP MCP Server Pricing 2026

The Stripe MCP server is Stripe's official Model Context Protocol integration, giving AI assistants direct access to your Stripe account through natural-language interactions. Built and maintained by Stripe as part of the stripe/agent-toolkit repository, this server exposes payment infrastructure as callable MCP tools: create and retrieve customers, generate payment intents, list products and prices, manage subscriptions, query invoice history, and look up charge details — all from within Claude, Cursor, or any MCP-compatible AI client. The Stripe MCP server is designed for indie developers, fintech teams, and SaaS operators who want to query payment data, draft refund workflows, debug failed charges, or generate revenue reports without opening the Stripe Dashboard. Authentication requires a Stripe Secret Key (sk_live_... for production, sk_test_... for sandbox testing). Real-world workflows include asking Claude to summarize yesterday's failed payments, list customers whose subscriptions expire this week, generate subscription cohort breakdowns, or draft dunning email copy based on at-risk MRR segments — all grounded in live Stripe data. Install via npm as part of the agent-toolkit package. Works with Claude Desktop, Cursor, VS Code, Windsurf, and Cline.

Query your ClickHouse database server for analytics workloads.

The Sentry MCP Server is Sentry's official Model Context Protocol integration, purpose-built for human-in-the-loop coding agents like Claude Code, Cursor, and Windsurf. Rather than exposing every Sentry API endpoint, it focuses tightly on developer debugging workflows: searching and triaging issues, pulling stack traces and event details, inspecting performance traces, and querying project/team/org metadata in natural language. The primary deployment is a hosted remote MCP server at mcp.sentry.dev, built on Cloudflare's remote-MCP infrastructure, so most users connect with zero local setup — just add the remote URL to their client. For self-hosted Sentry instances or local development, a stdio transport is also available via npx @sentry/mcp-server, authenticated with a Sentry User Auth Token scoped to org:read, project:read, project:write, team:read, team:write, and event:write. AI-powered search tools (search_events, search_issues) translate natural-language queries into Sentry's query syntax, but require a configured LLM provider (OpenAI, Azure OpenAI, Anthropic, or OpenRouter) — all other tools work without one. Claude Code users can also install it as a plugin (claude plugin install sentry-mcp@sentry-mcp) for automatic subagent delegation whenever a conversation touches Sentry errors, issues, or traces. This turns "why did this deploy break in production" into a direct conversational debugging session instead of tab-switching into the Sentry dashboard.

The Datadog MCP Server is Datadog's official Model Context Protocol integration that connects AI assistants directly to your Datadog observability platform — metrics, logs, APM traces, infrastructure, and monitors. Built and maintained by Datadog, the server uses your API and application keys to expose tools for querying live time-series metrics with full DQL expressions, searching log events with Datadog Log Management query syntax, retrieving distributed APM traces and service performance summaries, listing infrastructure hosts and their tags, and checking the status of Datadog monitors and downtime windows. This gives Claude real-time visibility into your production systems: ask "What's the p99 latency for the payments service over the last hour?" or "Find all ERROR-level logs from the auth service since the last deploy," and receive answers backed by live Datadog data rather than stale dashboards. Authentication requires a Datadog API key (DD_API_KEY) and an Application key (DD_APP_KEY) with appropriate scope — both available from Organization Settings > API Keys and Application Keys in the Datadog UI. Set DD_SITE to your Datadog region (e.g., datadoghq.com, datadoghq.eu, or us3.datadoghq.com). Works with Claude Desktop, Cursor, Windsurf, and any MCP-compatible client. Especially powerful for SRE, DevOps, and on-call workflows where engineers need AI to correlate metrics, logs, and traces during incident response without context-switching away from their conversation.

The official Grafana MCP server connects Claude and other AI assistants directly to your Grafana instance and its surrounding observability ecosystem, turning natural-language questions into dashboard lookups, incident investigations, and datasource queries. Dashboard tools cover search, retrieval, JSONPath-scoped property extraction, patch-based editing, and per-panel query/datasource introspection, with context-window-aware helpers like get_dashboard_summary so an agent never has to pull a full multi-megabyte dashboard JSON just to answer a simple question. Query tools speak PromQL against Prometheus (including histogram-percentile helpers), LogQL against Loki, and native query languages for InfluxDB, ClickHouse, CloudWatch, Graphite, Athena, Snowflake, Elasticsearch/OpenSearch, and Quickwit datasources — most gated behind opt-in --enabled-tools flags to keep the default tool surface lean. It also wraps Grafana Incident for creating and updating incidents, Sift for automated error-pattern and slow-request investigations, full alerting CRUD (rules, contact points, notification policies) across Grafana-managed and external Alertmanager sources, Grafana OnCall schedule/shift/alert-group management, RBAC-gated admin tools for teams/users/roles, deeplink generation so the LLM never has to guess a dashboard URL, annotations, snapshots, PNG rendering via the Grafana Image Renderer, and provisioning-repo validation for git-sync workflows. Ships as a Go binary or via uvx, authenticates with a Grafana service account token (Editor role or granular RBAC scopes), and every tool category can be individually disabled to control context-window usage.

Query and analyze your Axiom logs, traces, and all other event data in natural language.