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.

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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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Elastic Agent Builder MCP Server

Elastic documents the recommended Agent Builder Model Context Protocol endpoint at `{KIBANA_URL}/api/agent_builder/mcp` (or `{KIBANA_URL}/s/{SPACE_NAME}/api/agent_builder/mcp` for custom Kibana spaces) per elastic.co/docs/explore-analyze/ai-features/agent-builder/mcp-server. The MCP server exposes built-in and custom Agent Builder tools to Claude Desktop, Cursor, VS Code, and other MCP clients via `npx mcp-remote` with an `Authorization: ApiKey` header. API keys must include the Kibana application privilege `feature_agentBuilder.read` or clients receive HTTP 403. Elastic notes the legacy `elastic/mcp-server-elasticsearch` project is deprecated in favor of this endpoint on Elastic 9.2+ and Elasticsearch Serverless; docs recommend least-privilege index scopes and API key expiration.

MotherDuck MCP Server

MotherDuck documents a remote Model Context Protocol server at `https://api.motherduck.com/mcp` (fully managed, read-write) that lets Claude, ChatGPT, Cursor, Claude Code, Codex, and other MCP clients query MotherDuck cloud databases via OAuth or Bearer tokens with zero local install per motherduck.com/docs/sql-reference/mcp. Remote tools include read-only `query` and read-write `query_rw` plus schema exploration helpers documented in the MCP workflows guide. For local DuckDB files, S3 paths, or custom limits, the open-source `mcp-server-motherduck` package (motherduckdb/mcp-server-motherduck on GitHub, PyPI `mcp-server-motherduck`) runs via `uvx` with flags such as `--db-path`, `--motherduck-token`, and `--read-write` for self-hosted stdio or HTTP transports.

Langfuse MCP Server

Langfuse documents two MCP surfaces: a hosted Prompt Management server at `https://cloud.langfuse.com/api/public/mcp` (streamable HTTP, built into the platform per langfuse.com/docs/prompt-management/features/mcp-server) with tools such as `createTextPrompt`, `createChatPrompt`, `getPrompt`, and `updatePromptLabels`; and a public Docs MCP at `https://langfuse.com/api/mcp` for semantic search over Langfuse documentation (`searchLangfuseDocs`) without authentication. The legacy `langfuse/mcp-server-langfuse` Node server remains for local command-based setups with LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY. Langfuse positions MCP for agentic onboarding—IDE agents can add tracing to OpenAI, Anthropic, or LangChain codebases from the Get Started agentic tab.

Anthropic Remote MCP Connector

Anthropic documents remote Model Context Protocol connectors in Claude Help Center and platform.claude.com docs: users add publicly reachable MCP server URLs so Claude clients (claude.ai, Claude Desktop, Cowork, mobile) connect from Anthropic cloud infrastructure rather than the local machine. Team and Enterprise admins add connectors in Admin settings; individuals enable them under Settings > Connectors. The Messages API MCP connector (beta header anthropic-beta: mcp-client-2025-11-20) accepts mcp_servers entries with type url, name, and https URL, paired with mcp_toolset tools entries; servers must support streamable HTTP/SSE and be internet-accessible to Anthropic IP ranges.

dbt MCP Server

dbt Labs documents an official Model Context Protocol server at docs.getdbt.com/docs/dbt-ai/about-mcp (repository dbt-labs/dbt-mcp) that exposes governed access to dbt project metadata, lineage, CLI actions, and dbt Platform APIs for Claude, Cursor, and custom MCP clients. Local mode runs via `uvx dbt-mcp` with environment variables such as DBT_PROJECT_DIR, DBT_HOST, DBT_TOKEN, DBT_PROD_ENV_ID, and DBT_USER_ID; remote mode connects over HTTP/SSE to a managed dbt Platform MCP endpoint with OAuth. Documented tool groups include product-doc search (`search_product_docs`, `get_product_doc_pages`) and server metadata helpers, with additional development and deployment tools synced from the GitHub README per release.

Milvus MCP Server

The zilliztech/mcp-server-milvus project (documented at milvus.io/docs/milvus_and_mcp.md) exposes Milvus vector-database operations to MCP clients such as Claude Desktop and Cursor. The recommended launch path is `uv run src/mcp_server_milvus/server.py --milvus-uri http://localhost:19530` without a separate install step, with optional `MILVUS_URI`, `MILVUS_TOKEN`, and `MILVUS_DB` environment variables. Tools listed in Milvus docs include `milvus-text-search`, `milvus-hybrid-search`, `milvus-multi-vector-search`, `milvus-query`, and `milvus-count` for collection management, semantic retrieval, filtered hybrid search, and entity counts.

Claude Code Skills

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Agentic AI orchestration efficiency claims due diligence

Turns CEO and vendor narratives about agentic AI efficiency into a procurement and strategy checklist. The workflow separates quoted efficiency metrics (for example token- or energy-per-user framing) from product launch facts, orchestration architecture claims, and third-party valuation context in the same article. It references CNBC reporting on June 3, 2026 that Perplexity CEO Aravind Srinivas told CNBC's Elaine Yu the long-term AI winner will maximize what he called the "most taken value per watt per user" by balancing accuracy, latency, cost, privacy, and intelligence; that Perplexity is emphasizing agentic orchestration with Perplexity Computer (announced February) and Personal Computer on Windows (announced the prior Tuesday, with Mac already available); that Srinivas said Personal Computer routes processing between device and cloud; that Perplexity was last reportedly valued at $20 billion versus Anthropic near $1 trillion and OpenAI just over $850 billion with Anthropic confidentially filing for a U.S. IPO that week; and that Srinivas cited tripled annualized revenue since the start of the year tied to integrated Anthropic model improvements—without treating media valuations or CEO efficiency slogans as internal benchmarks.

Public equity AI infrastructure financing due diligence

Structures verification of public-company AI infrastructure financing headlines into a finance and strategy checklist. The workflow separates announced equity program components (underwritten offerings, at-the-market programs, private placements) from previously disclosed capex guidance and debt-market context cited in the same coverage. It references CNBC reporting on June 1, 2026 that Alphabet plans to sell $80 billion in stock—including a $10 billion Berkshire Hathaway private placement—to fund AI compute infrastructure while stating demand exceeds available supply; that Alphabet revised 2026 capex guidance to $180–$190 billion (up from $175–$185 billion); CEO Sundar Pichai cited compute capacity constraints; CNBC noted hyperscaler combined capex expectations above $700 billion in 2026 and prior Alphabet bond issuances—without treating media capex totals as your internal budget.

AI subscription monetization claims due diligence

Converts consumer-AI subscription announcements into a planning checklist for product, finance, and partnerships teams. The workflow separates test-market scope (countries, price tiers, free-tier continuity) from analyst revenue extrapolations and capex guidance cited in the same news cycle. It references CNBC reporting on May 30, 2026 that Meta will test Meta AI subscriptions at $7.99 and $19.99 per month starting next month in Singapore, Guatemala, and Bolivia while keeping a free tier; that nearly 98% of Meta's $56.3 billion Q1 revenue still came from ads; Zuckerberg said a cloud business is "definitely on the table"; Meta raised 2026 AI capex guidance to $125–$145 billion; and Wolfe Research analysts estimated subscriptions could reach about $3 billion in 2027 revenue growing to $16 billion by 2030—without treating media projections as internal forecasts.

Private AI funding and valuation claims due diligence

Structures verification of headline private-market AI funding rounds into an evidence checklist for strategy, finance, and partnerships teams. The workflow separates announced valuation, round size, lead investors, previously committed capital, and revenue run-rate figures from independently confirmable filings or issuer press releases. It cites CNBC reporting on May 28, 2026 that Anthropic announced a $65 billion Series H at a $965 billion valuation led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital—including $15 billion of previously committed investments with $5 billion from Amazon—surpassing OpenAI's reported $852 billion valuation after its March funding round, while Anthropic cited a $47 billion revenue run rate and releases of Claude Opus 4.8 and Claude Mythos Preview—without treating media valuations as internal planning numbers.

Hyperscaler cloud commitment due diligence review

Turns announcements of multi-year cloud spend commitments and earnings-day infrastructure deals into a finance-and-platform checklist. Teams separate headline dollar totals (for example five-year AWS purchase obligations) from average annual run rates, prior amended agreements, and what is actually earmarked for AI GPUs versus general-purpose silicon. The workflow maps public claims to internal FinOps data before revising data-platform budgets or agentic-AI roadmaps. It cites CNBC reporting on May 27, 2026 that Amazon disclosed a $6 billion five-year Snowflake commitment covering Graviton and AI GPUs alongside Snowflake's fiscal Q1 beat ($1.39 billion revenue, 39-cent adjusted EPS vs analyst expectations) and an undisclosed Natoma acquisition—without treating media figures as procurement instructions.

AI memory and HBM supply-chain claims due diligence

Structures verification of public claims about AI-driven memory shortages, high-bandwidth memory (HBM) demand, and trillion-dollar memory-chip valuations into an evidence checklist for finance, procurement, and platform teams. The workflow separates analyst price-target moves, year-to-date equity rallies, and vendor statements about agentic-AI workloads from independently observable supply signals (long-term agreements, stated capacity constraints, peer pricing power). It cites CNBC reporting that Micron crossed a $1 trillion market cap on May 26, 2026 after UBS raised its price target from $535 to $1,625, and that SK Hynix joined the trillion-dollar club on May 27, 2026 with shares up roughly 250% year to date amid AI chip demand lifting South Korea's Kospi—without endorsing any single stock call.

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