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ClickHouse MCP Server is ClickHouse's official MCP server (ClickHouse/mcp-clickhouse) that connects Claude, Cursor, and other MCP clients to a ClickHouse cluster for fast analytical querying over natural language. Its primary tool, run_query, executes arbitrary SQL against your cluster in read-only mode by default (CLICKHOUSE_ALLOW_WRITE_ACCESS=false) so an AI assistant can explore tables, aggregate billions of rows, and answer analytics questions without risk of mutating data — writes can be enabled explicitly when needed. Companion tools list databases and tables and return schema metadata (including the full create_table_query, with an option to omit per-column detail for lighter responses). A second tool set embeds chDB, ClickHouse's in-process engine, via run_chdb_select_query, letting the assistant query files, URLs, and external databases directly without an ETL step (enabled with the optional mcp-clickhouse[chdb] extra). Destructive statements are gated a second time: even with writes enabled, DROP and TRUNCATE require CLICKHOUSE_ALLOW_DROP=true as well. The server supports both stdio and HTTP/SSE transports; on HTTP/SSE authentication is required rather than optional — startup fails unless a static bearer token (CLICKHOUSE_MCP_AUTH_TOKEN), a FastMCP OAuth/OIDC provider (Azure Entra, Google, GitHub, WorkOS via FASTMCP_SERVER_AUTH), or an explicit local-development opt-out (CLICKHOUSE_MCP_AUTH_DISABLED) is configured. Connection is configured through CLICKHOUSE_HOST, CLICKHOUSE_PORT, CLICKHOUSE_USER, and CLICKHOUSE_PASSWORD, with ClickHouse Cloud, self-hosted, and the public SQL playground all supported.

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, vendor-hosted Model Context Protocol endpoint — not a package. Each Datadog site has its own URL of the form https://mcp.<your-site>/api/unstable/mcp-server/mcp (US1: mcp.datadoghq.com, EU1: mcp.datadoghq.eu), and OAuth 2.0 is the recommended way in; a Personal or Service Access Token as an Authorization bearer header is the documented fallback for CI, with DD_API_KEY plus DD_APPLICATION_KEY headers as a third option. Datadog ships first-party client integrations rather than expecting hand-written config: a Claude Code plugin (/plugin install datadog@claude-plugins-official, then /ddsetup and /ddtoolsets), a Claude connector from the Connectors Directory, plugins for Cursor, VS Code/Copilot, JetBrains and OpenCode, and a ChatGPT app in Preview for US1. Tools are grouped into toolsets selected with a ?toolsets= query parameter, and only `core` — logs, metrics, traces, dashboards, monitors, incidents, hosts, services, events, notebooks — loads by default; two dozen more cover alerting, DBM, DDSQL, RUM, profiling, security, Kubernetes, error tracking, feature flags, cost management and data observability, with apm, cases, code-exec and remote-actions in Preview and excluded from toolsets=all. Access requires the mcp_read or mcp_write role permission in addition to the normal resource permission, which is why a working connection can still return no data. Limits at time of writing are 50 requests per 10 seconds of tool-call burst and 50,000 tool calls per month, and the server is not GovCloud compatible.

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. Authentication is a Grafana service account token (Editor role, or granular RBAC scopes) passed as GRAFANA_SERVICE_ACCOUNT_TOKEN alongside GRAFANA_URL, and every tool category can be individually disabled to control context-window usage. On install, the recommended route is uvx: `uvx mcp-grafana` pulls the PyPI package mcp-grafana, which is published by Grafana Labs from this same repository — so despite the server being written in Go, the copy-paste command most Claude Desktop and Cursor configs use is a Python-tooling one, not a binary download. The alternatives are `go install github.com/grafana/mcp-grafana/cmd/mcp-grafana@latest` for a real binary, or the grafana/mcp-grafana container — `-t stdio` for local clients, or the default HTTP mode on :8000 (add `-t streamable-http`) with MCP_GRAFANA_SERVER_TOKEN set to authenticate callers when you expose it.

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

There are three different Figma MCP servers and the install command on most listings belongs to the one Figma no longer leads with, so start with which you are choosing. **Figma's recommended server is now the hosted remote one at `https://mcp.figma.com/mcp`** — nothing to install, OAuth in the client, attached with `claude mcp add --transport http figma https://mcp.figma.com/mcp`. It is also the only one with the write and cross-file tools (`use_figma`, `generate_figma_design`, `generate_diagram`, `create_new_file`, `search_design_system`, asset upload and download). **The Dev Mode desktop server ships inside the Figma Desktop app** — enable it in Dev Mode (Shift+D) from the MCP server section of the inspect panel and the app opens a local endpoint at `http://127.0.0.1:3845/mcp`. Figma's docs now describe the desktop server as being for specific organisation and enterprise cases and recommend the remote one instead. Because it runs in the app, it reads whatever you have selected on the canvas: exact colors, typography, spacing, auto-layout constraints, component variants, and — the part a token-based reader cannot do — your Code Connect mappings, so it emits your component names rather than generic divs. It requires a Dev or Full seat on a paid plan and the desktop app must be open. The repository linked here is `figma/mcp-server-guide`, Figma's own setup guide; Figma does not publish the server's source. **The community alternative is Figma-Context-MCP by GLips** (15,500+ stars), installed as `npx figma-developer-mcp --figma-api-key=YOUR_FIGMA_ACCESS_TOKEN`. It authenticates with a Personal Access Token from Figma Settings → Personal Access Tokens and calls the REST API, so it needs no desktop app, works headlessly in CI, and can read any file your account can open — including from a free plan. It exposes the document JSON, node lookup by ID, component listing, text extraction, and rendered-image download. The practical split: pick the official server when design-to-code fidelity and Code Connect matter and you are already paying for Dev Mode; pick GLips when you need automation, a free plan, or a machine with no Figma app installed. Both let an assistant translate a frame into accurate React, Tailwind, or plain HTML/CSS instead of guessing from a screenshot.