62 posts tagged with "AI Agents"

TL;DR​

• Screen against 1.17 million watchlist entries from 22 government sources — OFAC, EU, UK, UN, Canada, Switzerland, Interpol, and others — through the Global Sanctions & Watchlists MCP server, with fuzzy name matching and batch review.
• Covers 1.04 million individuals, 122,000 organizations, and 3,100 vessels; watchlists refreshed 2026-04-20.
• Built for vendor onboarding, payout approval, marketplace trust, and diligence workflows where the question is can this counterparty move forward? — not a full AML case-management platform, but a fast agent-callable screening layer.

If you run vendor onboarding, finance operations, marketplace trust, logistics compliance, or diligence research, sanctions screening is often one step inside a bigger decision. The team is not asking for a database. They are asking whether a counterparty can move forward.

The question sounds simple: "Is this company or person on a sanctions list?" Then reality gets in the way. Which list? Which alias? Is this a close match or just a similar name? Do we need to record the source list, the country, the identifier, and the reason for the match? Is this a vendor review, a customer onboarding step, or a shipping workflow where denied-party screening is only one part of the decision?

That is what the Global Sanctions & Watchlists MCP server is built for. It gives an agent a normalized sanctions search surface so the lookup can happen inside the workflow that needs the answer.

The average MCP tool description is bad. Two recent arXiv papers measured exactly how bad. 97.1% of analyzed tools carry at least one description smell. 73% of servers reuse the same display_name across multiple tools. In head-to-head selection between five functionally equivalent servers, the one with a clearer description gets picked 72% of the time vs a 20% baseline.

A 260% selection lift from prose alone is the headline. Descriptions aren't documentation; they're part of the agent-facing prompt, and a bad one quietly costs you every selection round your server is in. So we built a scorer.

MCPBundles now runs an LLM-as-judge rubric over every published server, covering tool descriptions, server-level skill content, and the structured MCP annotations that the runtime actually reads. The verdict shows up on the public listing page.

Substack is one of the largest homes for independent newsletters: journalists, analysts, founders, and operators build serious audiences there — often six figures for top writers. But the platform does not ship an official MCP server, and direct API access is thin. That leaves a gap between "read a Substack link in the chat" and "have the AI systematically research, compare, and monitor publications."

MCPBundles closes that gap with a hosted Substack provider (substack) exposing 4 MCP tools — browse any publication's posts, read full article content with HTML, explore all 31 Substack categories, and access your personalized reader feed.

arXiv hosts more than 2.4 million papers across physics, mathematics, computer science, quantitative biology, and more. Researchers, ML engineers, and academics increasingly want the same thing from their AI stack: search papers, read abstracts, follow citations, track new work in a field, and go deeper into full text — all from the same chat thread.

arXiv does not ship an official MCP server. Community implementations exist on GitHub, but they vary in quality, transport, and maintenance. MCPBundles hosts a dedicated arXiv provider (arxiv) with 4 MCP tools backed by the official arXiv Atom API — no API key required, no self-hosting, no config files. Enable the bundle, connect your AI client, and start searching.

MCP Apps are interactive tool outputs that render as rich HTML and CSS inside supported AI clients. Instead of stopping at plain text or raw JSON, an MCP tool can return a full, styled experience: dashboards, charts, forms, data tables, and cards that users can read and interact with in context.

The Model Context Protocol already made it possible for assistants to call tools against live systems. MCP Apps extend that idea: they turn those calls into something that feels closer to a small application than a chat transcript. For anyone searching mcp app or mcp apps, this guide explains what that means technically, why teams care, and how to build one.

Traditional MCP tools return text. AI clients display text well. Many real workflows still need visual output — a stock dashboard, a compliance summary, a project status board, a customer profile. MCP Apps bridge the gap between “the model called a tool” and “the user got something they can actually use on screen.”

Teams want X (Twitter) in the same place as the rest of their AI stack: searchable, scriptable, and safe behind the right credentials. The platform does not ship an official MCP server you can drop into Claude, ChatGPT, or Cursor. That leaves a gap between “ask the model a question about social data” and “actually query the live network with correct auth and rate limits.”

MCPBundles closes that gap with the x provider: 24+ MCP tools (and growing) backed by X API v2, two authentication paths, and a standout capability for research and brand intelligence — full-archive tweet search (the entire history of public tweets, not a rolling seven-day window).

Reddit is one of the web’s largest public discussion graphs: subreddits, threads, votes, and millions of daily posts. Teams use it for community support, product feedback, developer relations, and research. But wiring that into an AI workflow today usually means brittle scripts or one-off bots — not something you can hand to Claude, ChatGPT, or Cursor with clear boundaries.

Reddit does not ship an official MCP server. Community implementations tend to be thin or bot-shaped: enough to post a link, not enough to explore, reason, and act with structured tools. MCPBundles closes that gap with a hosted Reddit provider (reddit) that exposes 36+ MCP tools behind Reddit OAuth2 and three explicit permission tiers, so your agent gets the API surface it needs without over-scoping credentials.

Grafana is where engineering teams go to understand what's happening in their infrastructure. Dashboards, alerts, logs, metrics — it's all there. But when something goes wrong at 3am, the workflow is still manual: open Grafana, find the right dashboard, scan the panels, correlate timestamps, dig into logs.

MCP changes that. With a Grafana MCP server, your AI agent can search dashboards, pull panel data, read alert states, create annotations, and explore datasources — answering "what happened?" conversationally instead of through dashboard clicking.

Snowflake doesn't have an official MCP server. For a platform where most users interact through SQL, that's a meaningful gap — every question an analyst has starts with "what tables do we have?" and ends with a query, and that's exactly the workflow MCP is built for.

MCPBundles provides a purpose-built Snowflake toolset that connects your AI agent directly to your Snowflake account through the SQL REST API. Your AI can explore databases, navigate schemas, inspect table structures, execute arbitrary SQL, manage warehouses, and review query history — all authenticated with a Programmatic Access Token.

Supabase ships an official MCP server that gives your AI access to the full Supabase platform — Postgres databases, authentication, storage, edge functions, and project management. It's one of the more complete official MCP implementations, covering both development workflows and production operations.

This guide covers what the Supabase MCP server offers, how to set it up, and how to access it through MCPBundles alongside your other tools.

Linear has one of the best official MCP servers in the ecosystem. Built in partnership with Cloudflare and Anthropic, it runs as a remote hosted server at mcp.linear.app/mcp with OAuth 2.1 authentication and 25+ tools for managing your entire project workflow.

This guide covers what the Linear MCP server can do, how to set it up across different AI clients, and how to access it through MCPBundles for a hosted experience alongside your other tools.

There's no official PostgreSQL MCP server from the PostgreSQL Foundation — and there probably won't be, since PostgreSQL is an open-source project without a commercial entity pushing integrations. The community implementations that exist mostly stop at "run a query, get rows" — fine as a starting point, light on everything that actually shows up in real database work.

MCPBundles provides 20+ purpose-built tools that go far beyond raw SQL. Your AI explores schemas, profiles columns, analyzes index health, detects data quality issues, finds duplicates, explains query plans, and exports data — all without you writing a single SQL statement. And if you do want raw SQL, that's there too.

Most Discord server management is repetitive moderation and community work. Read every message in #support to find unanswered questions, then draft and post threaded replies. Scan #general for the day's key discussions and post a summary to #daily-digest. Create separate discussion threads for each agenda item in a pinned meeting note. React with checkmarks to every completed task message. Pin important announcements so they're easy to find.

Each of those is a 15-minute task in the Discord UI and a 30-second task as a chat message — if your AI agent can actually call the Discord API. This guide is the use-case version of "AI + Discord": what you ask, what the agent does, what comes back. The protocol underneath is MCP (Model Context Protocol), the bundle is /skills/discord on MCPBundles, but the framing here is workflow-first.

Most performance-marketing work in Google Ads is repetitive cognitive labour. Pull a search-term report, find the queries that wasted spend last week, write the negative-keyword list. Look at device performance, find that mobile CPC is up 40% with the same conversion rate, draft a bid adjustment. Spin up a campaign for next week's promo: budget, ad group, 15 keywords, an RSA with 11 headlines and 4 descriptions, all in PAUSED so nothing goes live by accident.

Each of those is a 20-minute task in the Google Ads UI and a 30-second task as a chat message — if your AI agent can actually call the Google Ads API. This guide is the use-case version of "AI + Google Ads": what you ask, what the agent does, what comes back. The protocol underneath is MCP (Model Context Protocol), the bundle is /skills/google-ads on MCPBundles, but the framing here is workflow-first.

Every other LinkedIn MCP server on GitHub is either a scraper that violates LinkedIn's Terms of Service or a thin wrapper around unofficial endpoints that can break at any time. Some use Patchright (a Playwright fork) to automate the browser. Others reverse-engineer private APIs. LinkedIn actively blocks these — and your account is at risk if you use them.

MCPBundles ships two LinkedIn MCP servers that work the way LinkedIn allows you to work:

• LinkedIn — built on LinkedIn's official REST API with OAuth 2.0. Manages company pages, publishes posts with images and carousels, engages with comments and reactions, runs ad campaigns, and tracks analytics.
• LinkedIn Sales Navigator — drives Sales Navigator search, profile lookups, InMail, and inbox as your connected LinkedIn account. Sign in once on a hosted page (no cookies, no extensions, no scraping); the bundle then runs SN-grade prospecting and outreach with built-in per-account rate caps so a careless agent can't get the seat banned.

You can connect either or both. The pages are the canonical product surfaces — keep this article open if you want the comparison; jump to the skill page when you're ready to use one.