Palette MCP

agentcentral is a hosted MCP server for Amazon sellers that connects Claude, ChatGPT, and other AI clients to Amazon Ads, Seller Central, inventory, orders, catalog, rankings, finance, and fulfillment data. It provides fast reads from pre-materialized Amazon data, scoped API keys, OAuth-based Amazon connections, encrypted tokens, per-tenant isolation, and guarded write tools with audit logs where write access is enabled.

One API for Search, Crawling, and Sitemaps

The Milvus MCP server enables AI applications to interact with Milvus vector databases using natural language commands. It allows AI models to perform vector searches, manage collections, and retrieve data without writing custom database queries. This integration facilitates seamless access to vector data, enhancing the capabilities of AI tools like Claude Desktop and Cursor.

A Model Context Protocol (MCP) server that integrates with Jina AI Search Foundation APIs.

An MCP service designed for deploying HTML content to EdgeOne Pages and obtaining an accessible public URL.

Find your first SaaS users on Reddit, X, YouTube, and TikTok. Lead-discovery MCP for solo founders.

PQS is the fastest way to get better output from any AI model. Score any prompt before it hits the model. Get a grade (A-F), score out of 80, percentile, and dimension breakdown across 8 quality dimensions. Built on PEEM, RAGAS, MT-Bench, G-Eval, and ROUGE frameworks. Pre-flight, not post-hoc. The AI input quality problem is real. PQS solves it. MCP Tools: - score_prompt: Free. Grade and percentile for any prompt. No API key needed. - optimize_prompt: $0.025 USDC. Returns optimized prompt with full dimension breakdown. - compare_models: $1.25 USDC. Side-by-side scoring across multiple models. HTTP API (x402-native on Base): - /api/score/free: Free. Grade and percentile, no payment required. - /api/score: $0.025 USDC. Single score, pay-per-call. - /api/score/full: $0.125 USDC. Grade, percentile, dimension breakdown, and rewrite. - /api/score/batch: $0.25 USDC. Score multiple prompts in a single call. - /api/score/compare: $1.25 USDC. Multi-model side-by-side scoring. - /api/preflight: $0.05 USDC. Lightweight pre-flight quality check. Paste a prompt. Get an optimized version. Ship better work. Cheaper than one bad prompt.

Channel management and messaging capabilities

Flag features directly from chat in your code editor, including VS Code, Cursor, Windsurf, Claude Code—any IDE with MCP support.

An MCP server that provides a tool for sending transactional emails via Mailtrap

test

Convert URLs to PDF, screenshot, or markdown — and convert document and image files — directly from Claude Code,Cursor, Windsurf, and Claude Desktop

Bridge between Ableton Live and AI models. Inspect tracks, analyze audio (LUFS, spectrograms), and control mixing parameters in real-time via MCP.

Playwright MCP server

MCP server for interacting with Neon Management API and databases

MCP Advisor & Installation - Use the right MCP server for your needs

Manage your GoodBarber app via natural language across eCommerce, Community, and Membership. Hosted MCP with OAuth — products, orders, promo codes, push broadcasts, customer subscriptions, traffic analytics. Connect from Claude Desktop, Cursor, VS Code, or any MCP-compatible client and pair with the 30 ready-to-use skills at github.com/goodbarber/goodbarber-skills.

Browser automation and web scraping

Manage your own AdButler Ad Server directly from your AI Agent. Create Ad Campaigns, Manage Inventory, Forecasting, Zones, Reporting and more.

BenchGecko MCP Server A Model Context Protocol (MCP) server that provides access to AI model benchmarks, inference pricing, and provider data. Built on data from BenchGecko, the data layer of the AI economy. Features Model Rankings - Search and explore thousands of AI models across 128 evaluation suites Cross-Provider Pricing - Find the cheapest inference for any model, compared across every major provider Provider Directory - Every major AI inference provider with availability and pricing details Benchmark Scores - Comprehensive evaluation suite tracking covering coding, math, reasoning, and more

Coordination protocol for real-world tasks (cleaning, plumbing, painting, gardening, repair) in the Netherlands. AI agents create tasks via natural language through POST /do endpoint. AstraNL handles provider matching, transparent pricing, Stripe Connect escrow (99% to provider, 1% AstraNL fee), evidence chain, dispute resolution. Free first 3 tasks per agent. KvK 88449335, EU AI Act compliant. Supports 10 languages.

GBOX provides environments for AI Agents to operate computer and mobile devices. Mobile Scenario: Your agents can use GBOX to develop/test android apps, or run apps on the Android to complete various tasks(mobile automation). Desktop Scenario: Your agents can use GBOX to operate desktop apps such as browser, terminal, VSCode, etc(desktop automation). MCP: You can also plug GBOX MCP to any Agent you like, such as Cursor, Claude Code. These agents will instantly get the ability to operate computer and mobile devices.

Automatically create a remote browser to complete your specified tasks, developed based on Browser Use + Sandbox. 自动创建一个远程浏览器，完成你指定的任务，基于Browser Use + Sandbox开发。

An MCP server implementation for retrieving information from the AWS Knowledge Base using the Bedrock Agent Runtime.

BlenderMCP connects Blender to Claude AI through the Model Context Protocol (MCP), allowing Claude to directly interact with and control Blender. This integration enables prompt assisted 3D modeling, scene creation, and manipulation.

scratchpad-mcp is an MCP server that gives AI agents persistent, token-efficient storage. It solves a specific waste problem: agents constantly re-read files they've already seen, re-summarize documents they've already processed, and re-load context they've already understood. Every one of those round-trips burns tokens for no new information. This server fixes that with eight tools designed around how agents actually work: Versioned writes. write_file automatically versions every write and keeps the 10 most recent versions per file. Storage is append-only on success and atomic on failure partial writes can't corrupt state. Structured diffs. read_file accepts a since_version parameter and returns a JSON line-diff against that prior version instead of the full content. Agents that have already seen v1 can ask "what changed in v3?" and get a small structured payload they can reason about, not the entire file again. Append-only logs. append_log and read_log give agents an event-stream they can replay. Cursor-based pagination (since_entry + last_entry_id + has_more) means an agent can checkpoint where it left off and resume cheaply. On-demand summaries. summarize_file calls Claude Haiku to summarize files over ~2000 estimated tokens. Summaries are cached per file version, so repeat calls on an unchanged file cost nothing. The threshold is enforced server-side you can't accidentally pay to summarize something small. Per-agent isolation. Every operation is scoped by an agent_id parameter, so one server instance can serve many agents without leaking state between them. Storage limits. 1 MB per file write, 64 KB per log entry, 1000 files / 100k log entries / 100 MB total per agent sane multi-tenant guardrails out of the box. Backed by a single SQLite file (Postgres migration is on the roadmap). All SQL is parameterized, paths are validated against a strict allowlist, and the security model is documented honestly it's safe for one-user-per-process deployments today, and the V2 plan derives agent_id from the caller's API key for true multi-tenancy. Build agents that remember what they've already seen.