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 →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.
LangSmith MCP Server
LangChain documents a LangSmith Model Context Protocol server that lets MCP clients read conversation threads, prompts, runs and traces, datasets, experiments, and billing usage from a LangSmith workspace. For LangSmith Cloud, docs recommend the OAuth-authenticated LangSmith Remote MCP (regional endpoints on api.smith.langchain.com and documented EU/APAC/AWS variants) with the same tool surface and no separate deployment. The standalone HTTP server at https://langsmith-mcp-server.onrender.com/mcp remains documented for API-key access via the LANGSMITH-API-KEY header, while self-hosted LangSmith users can run the open-source langsmith-mcp-server package with uvx, Docker HTTP on port 8000, or point LANGSMITH_ENDPOINT at private instances. Tools include get_thread_history, list_prompts, fetch_runs (with FQL filters and character-budget pagination), dataset/example readers, list_experiments, and get_billing_usage per the official tool table.
Snowflake-managed MCP Server
Snowflake documents a Snowflake-hosted Model Context Protocol (MCP) endpoint that fronts governed Snowflake data and Cortex workloads without provisioning a separate MCP bridge VM. Administrators declare tools with SQL (`CREATE MCP SERVER`)—for example Cortex Search queries, Cortex Analyst chat-style messages, Cortex Agent executions, parameterized SQL runners, or custom tools backed by Snowflake-native functions—and clients authenticate using Snowflake OAuth against the MCP revision pinned in Snowflake release notes.
Claude Code Skills
More →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.
LangSmith production trace investigation playbook
Turns LangSmith observability documentation into a repeatable incident workflow for LLM and agent outages: start from a failing run ID or thread, use the UI or LangSmith MCP tools (`fetch_runs`, `get_thread_history`) to reconstruct prompts, tool calls, and errors, then narrow scope with documented filters (run_type, is_root, FQL `filter` / `trace_filter` / `tree_filter`) before proposing code or prompt changes. The playbook cites official pagination rules (character-budget pages with `page_number` and `total_pages`) so investigators do not assume single-shot dumps, and it reminds teams to separate Cloud OAuth Remote MCP paths from self-hosted `LANGSMITH_ENDPOINT` configurations when collecting evidence.
OWASP GenAI LLM Top 10 (v1.1) threat review checklist
Maps the authoritative OWASP "Top 10 for Large Language Model Applications" (version 1.1) taxonomy—LLM01 Prompt Injection through LLM10 Model Theft—into an actionable readiness checklist for architects red-teaming Retrieval-Augmented Generation, Agents, plugins, training pipelines, or hosted inference gateways. Official project pages summarize each risk bucket (prompt injection bypassing safeguards, unchecked outputs enabling downstream exploits, poisoned corpora distorting reasoning, abusive workloads starving capacity, brittle supply-chain dependencies, sensitive data resurfacing inside generations, excessively privileged plugins/agents/autonomy, misplaced trust producing compliance failures, loss of proprietary model weights via API abuse). The skill pairs each category with tangible controls (policy, monitoring, toolchain limits) anchored to genai.owasp.org releases rather than anecdotes.