Build your AI stack
Tools, MCP servers, and skills that work together — from editor to production.
AI Coding Tools
View all →ChatGPT
ChatGPT is a large language model-based chatbot developed by OpenAI, launched in November 2022. It uses the GPT-4 architecture to generate human-like text responses across conversation formats. The model supports multi-modal inputs including text, images, and voice interactions. A free tier is available with GPT-3.5, while ChatGPT Plus provides access to GPT-4 with faster response times and plugin capabilities. It serves as a versatile tool for writing, analysis, coding assistance, and creative tasks.
Gemini
Gemini is Google's family of multimodal AI models designed to compete with OpenAI's GPT series. Formerly known as Bard, it rebranded to Gemini in 2024 and directly integrates with Google services. The Ultra 1.0 model achieved state-of-the-art performance on multiple benchmarks. Gemini is available through the Google AI app, web interface, and integrates with Gmail, Docs, and other Google Workspace applications.
Claude
Claude is Anthropic's AI assistant based on the Constitutional AI and RLHF-aligned methodology. Launched in 2023, Claude emphasizes helpful, harmless, and honest interactions. It supports extremely long context windows of up to 200K tokens, making it effective for analyzing lengthy documents. Claude 3.5 Sonnet represents the mid-tier model with strong coding and reasoning capabilities. The iOS app and web interface provide easy access across devices.
DeepSeek
DeepSeek is a Chinese AI company that gained prominence in 2025 with its DeepSeek-V3 model, achieving performance comparable to leading US models at significantly lower training costs. The company released DeepSeek-R1 in January 2025, an open-source reasoning model that competes with OpenAI's o1. DeepSeek's models are available through their web interface, API, and have been integrated into various applications. Their open-source approach has democratized access to frontier-level AI capabilities.
Cursor
Cursor is an AI-first code editor built on VS Code, launched in 2023 by Anysphere. It integrates AI capabilities directly into the coding workflow with features like code completion, natural language commands, and pair programming. Cursor 0.5 introduced Agent capabilities that can autonomously modify codebases. The editor supports Python, JavaScript, TypeScript, Go, Rust, and other major languages. It offers a free tier with 1000 code completions and paid plans for extended usage.
GitHub Copilot
GitHub Copilot is Microsoft's AI coding assistant integrated directly into IDEs like VS Code, JetBrains, and Vim. Powered by OpenAI's GPT-4 and specialized code models, it provides real-time code suggestions, entire function implementations, and documentation generation. Copilot Chat enables conversational debugging and code explanation. Launched in 2021 as a technical preview, it became generally available in 2022. Business plans offer team management, policy controls, and SAML SSO integration.
Windsurf
Windsurf is an AI-powered code editor by Codeium, launched in 2024. Its signature feature is Cascade, a chat interface that maintains project context across editing sessions. Windsurf distinguishes itself with SUPERCLINE, a context engine that tracks cursor position and project state for highly relevant suggestions. The editor is built on the same foundation as Codeium's enterprise tooling, emphasizing speed and privacy. A free tier exists alongside Pro and Enterprise plans.
Midjourney
Midjourney is an independent AI image generation lab that operates primarily through a Discord bot. Launched in 2022, it produces highly artistic and stylized images from text prompts. Users interact via Discord commands, with generation happening on Midjourney's servers. Version 6 (V6) released in late 2024 offers improved coherence, text rendering in images, and photorealism capabilities. The platform has developed a distinctive aesthetic that has influenced digital art and design communities.
MCP Servers
More →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.
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.
Claude Code Skills
More →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.
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.