Your AI development stack, curated

The best AI coding tools, MCP workflows, and Claude Code skills — organized for developers. From editor setup to production integrations.

Build your AI stack

Tools, MCP servers, and skills that work together — from editor to production.

AI Coding Tools
8+ tools indexed
Editor extensions, code completion, pair programming tools. Cursor, Windsurf, Copilot, and more.
MCP Servers
6+ MCP servers indexed
Connect your AI to GitHub, databases, browsers, search, and production infrastructure.
Claude Code Skills
6+ skills indexed
Reusable workflow modules for debugging, refactoring, code review, and planning.

MCP Servers

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Mem0 MCP Server

Mem0 documents a hosted Model Context Protocol server at https://mcp.mem0.ai/mcp that exposes Platform memory tools (`add_memory`, `search_memories`, `get_memories`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`, `list_events`, `get_event_status`) to Claude, Claude Code, Codex, Cursor, Windsurf, VS Code, and OpenCode. Setup uses `npx mcp-add` with HTTP transport or manual JSON/TOML client configs; Codex requires `MEM0_API_KEY` as bearer token per docs.mem0.ai/platform/mem0-mcp. The cloud server needs a Mem0 Platform API key from the dashboard and Node.js for the installer—no local vector database required for the hosted path.

ClickHouse MCP Server

The open-source ClickHouse MCP server (PyPI package `mcp-clickhouse`, repository ClickHouse/mcp-clickhouse) exposes MCP tools such as `run_query`, `list_databases`, and paginated `list_tables` against ClickHouse clusters, defaulting to read-only SQL unless `CLICKHOUSE_ALLOW_WRITE_ACCESS` is enabled. Optional chDB extras add `run_chdb_select_query` for embedded queries over files and URLs. HTTP/SSE transports require authentication via `CLICKHOUSE_MCP_AUTH_TOKEN`, FastMCP OAuth/OIDC providers, or explicit `CLICKHOUSE_MCP_AUTH_DISABLED=true` for local dev; a `/health` endpoint supports orchestrator probes without credentials per README guidance.

Datadog MCP Server

Datadog documents a remote Model Context Protocol server at docs.datadoghq.com/bits_ai/mcp_server that connects AI agents in Cursor, Claude Code, Codex CLI, VS Code, Gemini CLI, and other MCP clients to observability data across APM, logs, metrics, monitors, dashboards, and security signals. Setup guides describe OAuth-based connection to Datadog's hosted MCP endpoint (distinct from the local-only Code Security MCP Server used for SAST/SCA scans). Fair-use limits listed in docs include 50 requests per 10 seconds burst and 50,000 monthly tool calls; Audit Trail records MCP actions with tool name, arguments, user identity, and client, while metrics `datadog.mcp.session.starts` and `datadog.mcp.tool.usage` tag usage by client and tool.

Composio MCP Server

Composio documents MCP server creation through its SDK and dashboard at docs.composio.dev: developers call `composio.mcp.create()` with toolkit names, auth config IDs, and an `allowed_tools` list, then generate per-user MCP URLs via `composio.mcp.generate(user_id, mcp_config_id)`. Hosted endpoints follow the pattern `https://backend.composio.dev/v3/mcp/{SERVER_ID}?user_id=...` and require an `x-api-key` header when `require_mcp_api_key` is enabled (default for new orgs). Docs show wiring these URLs into OpenAI Responses API, Anthropic MCP client beta, Mastra MCPClient, Claude Desktop, and Cursor. Composio notes that dynamic sessions are recommended for most use cases, while single-toolkit MCP configs suit fixed integration surfaces.

E2B MCP Gateway

E2B documents an MCP gateway that runs inside cloud sandboxes, exposing 200+ tools from the Docker MCP Catalog (Browserbase, Exa, Notion, Stripe, GitHub, and others) through a unified HTTP endpoint with bearer-token auth. Developers create a Sandbox with an `mcp` configuration map of server credentials, call `getMcpUrl()` / `getMcpToken()`, and attach the gateway to MCP clients such as Claude Code via `claude mcp add --transport http`. Sandboxes provide an internet-connected Linux environment where agents can install packages, run terminal commands, and execute generated code while MCP tools stay type-safe per E2B's overview at e2b.dev/docs/mcp.

LiteLLM MCP Gateway

LiteLLM Proxy documentation describes an MCP Gateway that exposes list-tools, call-tools, prompts, and resources operations through a fixed endpoint while enforcing access by API key, team, or organization. Supported transports listed on docs.litellm.ai include Streamable HTTP, SSE, and stdio; operators can register HTTP, SSE, or stdio MCP servers through the LiteLLM UI or config.yaml after enabling database storage (`store_model_in_db` / `STORE_MODEL_IN_DB`). Release notes cited in the docs state LiteLLM v1.80.18 aligns with MCP protocol version 2025-11-25 and namespaces tools by MCP server name per SEP-986 naming rules for newly added servers. The gateway is positioned as a way to use MCP tools alongside all LiteLLM-supported chat models from Cursor or other OpenAI-compatible clients pointed at the proxy.

Claude Code Skills

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Advanced chip roadmap claims due diligence review

Turns public semiconductor announcements into a verification checklist when vendors claim novel scaling laws, stacked logic architectures, or nanometer-class equivalence without independent benchmarks. Teams separate marketing nomenclature from manufacturing readiness by demanding yield, thermal, packaging, and third-party validation evidence—patterns highlighted when CNBC reported Huawei's LogicFolding and τ Scaling Law claims alongside analyst skepticism about true 1.4nm-class process breakthroughs without EUV access. The skill also maps export-control context (ASML EUV restrictions) and competitive implications for GPU vendors operating in constrained geographies.

AI economic benefit distribution readiness review

Converts public-policy and labor-relations guidance around AI-driven wealth into a planning checklist for organizations operating in semiconductor-heavy economies. Teams document how AI productivity gains translate—or fail to translate—into worker bonuses, public dividends, or reinvestment; assess concentration risk when chipmakers dominate equity indices; and prepare dialogue frameworks for recurring labor-management disputes as agentic automation scales. The skill cites CNBC reporting on South Korea's deputy prime minister urging that AI benefits reach the public amid Samsung strike negotiations, Kospi gains led by Samsung and SK Hynix, and debates over distributing AI-sector tax windfalls—without prescribing specific tax policies beyond verifying stakeholder messaging against cited facts.

Responsible AI accessibility data review

Turns Microsoft Learn responsible AI modules and accessibility remediation patterns into a checklist for teams shipping generative features that emit images, code, or UI copy. Practitioners verify training-data gaps (for example stereotypical depictions of disabled users), audit metadata labels on inclusive datasets, document human-in-the-loop fixes, and align with published principles that people remain accountable for AI outcomes. The skill references learn.microsoft.com training on responsible AI practices and real-world corrections such as purchasing supplemental multimodal data when model outputs misrepresent blind users—without skipping metadata-layer bias reviews emphasized by ML fairness practitioners.

Agentic coding vendor readiness review

Turns platform reliability and multi-vendor coding-agent guidance into a checklist before standardizing on a single AI coding stack. Teams inventory host-platform SLAs (for example GitHub availability incidents documented on githubstatus.com), compare primary and backup agents (GitHub Copilot, Cursor, Claude Code, Codex, etc.), verify observability hooks through Braintrust or similar tracing, and rehearse workflows when the code host or agent API is degraded. The skill cites public status pages and vendor billing changes—such as usage-based Copilot pricing announced on github.blog—so procurement and engineering sign off with eyes open about downtime, leadership churn, and feature parity gaps reported in trade media.

Multi-region LLM provider readiness review

Converts export-control and multi-vendor routing guidance into a planning checklist for teams that cannot assume a single geography or chip supplier will stay available. Practitioners document primary and contingency model routes (including gateways such as Helicone or LiteLLM Router configs), quantify revenue or latency exposure if a region is blocked, and set investor/customer messaging when leadership advises to "expect nothing" from a market—as publicly reported when semiconductor vendors discuss China licensing uncertainty. The skill cross-checks legal/compliance sign-off, drills failover to alternate regions or domestic stacks, and records evidence before production launches tied to geopolitically sensitive deployments.

LiteLLM Router fallback readiness review

Translates LiteLLM routing documentation into a pre-flight checklist before promoting multi-deployment LLM routes to production. Teams verify Router configuration covers primary and fallback model lists, retry policies, and load-balancing strategy documented at docs.litellm.ai/docs/routing, confirm proxy virtual keys and spend limits if traffic flows through LiteLLM Proxy, and rehearse provider outage drills using OpenAI-mapped exceptions (AuthenticationError, RateLimitError, APIError). The skill also points operators to enable `store_model_in_db` when MCP tools must persist alongside router definitions and to validate MCP server names comply with SEP-986 guidance referenced in LiteLLM v1.80.18 release notes.

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