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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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.

n8n MCP Server Trigger

The MCP Server Trigger is a first-party n8n core node that turns an n8n workflow into a Model Context Protocol server endpoint. Instead of chaining conventional trigger nodes, it connects only to tool nodes so remote MCP clients can list tools and invoke them over long-lived Server-Sent Events or streamable HTTP transports (stdio is explicitly unsupported). Each node exposes separate test and production MCP URLs, optional bearer or header authentication, and documentation explains how to proxy Claude Desktop through `npx mcp-remote` plus queue-mode caveats for multi-replica webhook deployments.

DuckDB MCP community extension (`duckdb_mcp`)

The DuckDB-distributed community extension `duckdb_mcp` embeds MCP client and server capabilities directly inside DuckDB. Installers load it via `INSTALL duckdb_mcp FROM community` followed by `LOAD duckdb_mcp`, after which SQL can attach remote MCP servers (stdio/TCP/WebSocket transports), enumerate resources (`mcp_list_resources`), invoke remote tools (`mcp_call_tool`), and wrap responses with `read_csv`/`read_json`/`read_parquet` URIs routed through `mcp://`. In reverse direction, DuckDB can publish tables, queries, and execution-bound tools (`mcp_publish_table`, `mcp_publish_query`, `mcp_publish_execution_tool`) while `mcp_server_start` exposes them to external MCP-compatible clients.

Neon MCP Server

Official Neon MCP integration exposes Neon Postgres projects to MCP-capable assistants via Streamable HTTP (`https://mcp.neon.tech/mcp`), legacy SSE (`https://mcp.neon.tech/sse`), or a locally launched `@neondatabase/mcp-server-neon` package. Documentation lists tools for project and branch lifecycle, SQL execution, migration rehearsal branches, slow-query diagnostics, Neon Auth provisioning, Data API setup, and embedded Neon docs retrieval—each mapped to Neon API operations.

Claude Code Skills

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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.

Postmortem trigger and root-cause taxonomy

Distills Appendix C (“Results of Postmortem Analysis”) from Google’s SRE workbook: it explains why Google catalogs standardized postmortem fields—linking outages to observable triggers versus deeper root-cause categories—so reliability leaders can prioritize systemic fixes rather than anecdotal fixes. The appendix cites a multi-year corpus (labeled 2010–2017 in the workbook) highlighting that binary pushes accounted for roughly 37% of outage triggers while configuration pushes were about 31%, with additional slices for user-behavior spikes, pipelines, upstream providers, performance decay, capacity, and hardware. A companion table correlates outages with qualitative root causes such as faulty software (~41%), development-process gaps (~20%), emergent complexity (~17%), deployment planning weaknesses (~7%), and network failures (~3%). Teams use these distributions to sanity-check whether their incident queues skew differently and to steer investment into the failure classes that statistically dominate historically.

Example SLO document authoring

Operationalizes Appendix A from Google’s SRE workbook by translating the illustrative “Example Game Service” SLO dossier into a checklist teams can mimic: articulate the user-facing workload, nominate rolling measurement windows (the appendix uses four weeks), pair each subsystem with tightly defined SLIs (availability from load balancers excluding 5xx, latency percentile gates, freshness for derived tables, correctness via probers, completeness for pipelines), cite explicit numerator/denominator language, rationalize rounding policies, quantify per-objective error budgets, and cite the sibling error budget policy for enforcement.

Error budget policy drafting

Translates Google’s worked example error-budget policy into a repeatable playbook for tying release tempo to measured reliability: define goals (protect users from repeated SLO misses while preserving innovation incentives), spell out what happens when the rolling window consumes its budget (freeze changes except urgent defects or security work), codify outage investigation thresholds, and document escalation paths when stakeholders disagree about budget math.

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