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Connecting Hermes AI Agent to an MCP Gateway

Setup, breakdown, and actual use cases

Hermes AI Agent has been getting a fair amount of attention lately. People are calling it one of the more capable open-source agent runtimes available right now, and from what I have seen, that reputation holds. The planning layer is solid. Memory across sessions works the way you would want it to. The part that kept breaking for me was the tool layer.

Once my workflows touched three or four external systems, I was spending more time on auth configs, mismatched response formats, and per-tool retry logic than on the actual workflows I was trying to build. I fixed this by routing all external tool calls through a unified MCP gateway. The agent logic stayed the same. The integration complexity moved into one place I could actually manage.

Here is how I set it up, what changed, and where it has been genuinely useful.

How Hermes Runs Tasks

Hermes is an open-source, self-hosted agent runtime from Nous Research, released in February 2026 under the MIT license. It runs persistently on your own infrastructure and executes goals as structured, stateful workflows across four layers:

Planning breaks a goal into sequenced steps and adjusts them as intermediate results come in

Execution runs each step and fires tool calls when external data or action is needed

Memory stores task state and session history in SQLite with FTS5, so context carries across restarts

Skills captures completed workflows as reusable documents retrieved on future tasks

After a task finishes, Hermes writes a skill file with the procedure and known failure points, then stores it for retrieval the next time a similar task runs. Tool execution is embedded in the runtime loop. External capabilities come through MCP-based interfaces, which is exactly where the gateway plugs in.

What Breaks When Integrations Live Inside the Agent

In a standard MCP setup, each client connects one-to-one with a specific MCP server. That works fine with two or three tools. With ten, it becomes a maintenance problem that compounds with every addition, and I felt that pretty quickly.

A task spanning a web search, a product API, and a SERP scraper meant three separate auth setups, three response formats to parse, and three different error behaviors to account for. None of that is workflow logic. It just accumulates inside the workflow until something breaks.

Intermediate outputs in Hermes pass from one step to the next. When two tools return data in different shapes, the workflow needs transformation logic to bridge them. That logic breaks when an upstream API changes its response format. Debugging was slow because a failure could be in the execution logic or any integration underneath it, and I had no quick way to tell which.

What Routing Through an MCP Gateway Changed for Me

I connected Hermes to MCP360, an MCP gateway that sits between the agent and all external tools. That one change shifted how I manage integration complexity entirely.

Hermes now sends every tool request in one standardized format. MCP360 handles routing, authentication, rate limit backoff, and response normalization. Every tool response Hermes receives comes back in the same structure regardless of what produced it.

My workflow logic no longer carries parsing code for individual tools. Intermediate results pass between steps without transformation. When a credential expires or an API rate limits, that is handled at the gateway. Hermes gets a structured response either way.

Debugging became much cleaner. A failure is now either in the execution logic or in the gateway layer. Two places to look instead of ten. Adding a new tool through the gateway does not touch existing workflows, which is the part I appreciate most.

Setup Guide

Step 1. Copy Your MCP360 Gateway URL

Log in to your MCP360 dashboard and open an existing project or create a new one. From the left navigation menu, open MCP Servers. You can select a specific MCP server or use the Universal MCP Gateway, which gives access to all tools in your MCP360 workspace. Copy the MCP Gateway URL. You will use this when configuring tool access inside Hermes.

Step 2. Install Hermes AI Agent

On Windows, open PowerShell as Administrator and run the installation command.

On Linux, macOS, or WSL2, use the corresponding terminal command.

I have also used Codex to handle this step. The prompt I used was: Install Hermes AI Agent on this Windows machine using the official installation method.

Step 3. Start Hermes Chat and Connect MCP360

Open a new terminal window and start the Hermes chat interface. After adding the MCP360 Gateway URL and token, Hermes confirms the MCP connection is active and tools are loaded.

Step 4. Verify the Connection

Run hermes mcp test mcp360 to confirm the connection. If MCP360 appears in the output, the integration is active.

To confirm all connected MCP servers are registered, run hermes mcp list.

Real Workflows I Have Run with This Setup

Here are actual workflows you can run with this setup:

SEO research. Keyword data, Google Trends, SERP results, and competitor pages in one sequence instead of four separate exports. Hermes runs through them in order and outputs a single research brief.

Scheduled competitor monitoring. Hermes pulls search visibility data, crawls competitor URLs for content changes, and compiles a diff report on a set schedule. No manual re-runs on my end.

Product monitoring across Walmart and Amazon. One workflow queries both marketplaces, compares listings on price and positioning, and flags changes since the last run. Both platforms come through the same tool layer with no separate credential setup per platform.

Pre-call research. Domain records, DNS data, website metadata, and search visibility across 20 to 30 accounts would take most of a morning to pull manually. Hermes sequences the requests and outputs one briefing document per company.

Multi-source market research. Search data, trend signals, competitor content, and public web sources worked through in one session. The output is a structured draft rather than a set of open tabs.

Closing

Hermes handles execution logic. The gateway handles external connectivity. Keeping those two responsibilities separate is what makes the system maintainable as tools are added. If adding a new tool currently means editing existing workflow logic, moving integrations into a dedicated layer is worth doing before that pattern gets harder to reverse.

Questions about the setup or specific workflows are welcome in the comments.

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Best MCP Servers for Teams in 2026

AI agents are being used for research, project management, customer support, software development, and operational tasks. Their usefulness depends on the systems they can access and the actions they can perform.

MCP (Model Context Protocol) servers provide a standardized way to connect AI clients with business applications, databases, documentation platforms, communication tools, and external services. Instead of building separate integrations for every tool, teams can use MCP to give agents access to the resources they need through a consistent interface.

Today, teams can choose from a wide range of MCP platforms. Some focus on workflow automation, others on governance and access control, while some provide a single gateway to hundreds of external tools and services.

In this guide, we’ll compare the best MCP servers for teams in 2026, including their strengths, limitations, pricing, and the use cases they’re best suited for.

1. Composio

Composio solves a narrower but very common problem: letting an agent take action inside SaaS tools without your team building a custom integration for each one. It sits as a managed layer between the agent and over 500 APIs, covering CRMs, dev tools, and communication platforms, and plugs directly into LangChain, LlamaIndex, and most other agent frameworks without extra setup. The real time saver is OAuth and token handling. Composio manages the entire token lifecycle, including refresh, across every connected service. Scoped MCP servers also let you expose only the subset of tools a given agent needs rather than dumping the full catalog into its context window.

The pre-built tools are closed source, though. If one doesn’t behave the way you need, there’s no way to modify it, you have to build a replacement outside the platform. Observability is also fairly thin, so diagnosing a production failure can take longer than you’d like. Plans start at $29 a month.

Best for: internal productivity agents acting across common SaaS tools where the existing catalog already covers what you need and deep customization isn’t a requirement.

2. MCP360

MCP360 positions itself as the single connection point an agent needs to reach dozens of external services at once. Instead of configuring a separate tool for search, another for scraping, another for SEO data, you connect once and the gateway routes to whichever underlying tool the task calls for. The unified search and execute model means an agent doesn’t need to know in advance which of the 100+ marketplace tools to call. It searches for what it needs and the gateway resolves it. There’s also a custom MCP builder for turning your own REST APIs into callable tools, plus isolated custom servers for separating workflows by team.

The catalog changes often, so there’s some ongoing adjustment, and the setup overhead isn’t worth it if you only need one tool for one job. Plans start at $16 a month.

Best for: agents pulling from multiple external data sources in a single session, particularly SEO research, market intelligence, and data aggregation work.

3. Workato

Workato comes from the enterprise iPaaS world and extended into MCP infrastructure as that need became unavoidable for larger organizations. Any AI agent touching real business systems, Salesforce, Zendesk, Jira, Slack, needs to do so under the same governance controls a human employee would be subject to, and Workato built its MCP servers around that requirement. Role-based permissions, rate limiting, and full audit logging apply to every single MCP call, which matters in regulated industries where knowing exactly what an agent did and why isn’t optional. Existing Workato recipes can also be converted into custom MCP servers, so internal systems outside the standard connector library can still be exposed under the same governed framework.

The constraints are real, though. Workflow execution times out around 90 minutes, which rules out anything long-running, and it’s not built for heavy file transfers or large data payloads. Pricing is custom and enterprise-only.

Best for: organizations where agent actions need full audit trails and identity-aware access controls, particularly in regulated industries.

4. Pipedream

Pipedream sits closer to the developer end of the spectrum. It’s event-driven by design, with triggers off webhooks, schedules, or API events, and runs its own MCP server covering more than 2,500 apps including Slack, Gmail, Salesforce, HubSpot, and GitHub. What separates it from a pure no-code tool is custom code execution at any step, dropping in Node.js or Python wherever the prebuilt actions don’t cover what you need. Credentials are managed server-side and never exposed to the model itself, and step-level logging means you can see exactly where a workflow broke rather than guessing across an opaque chain.

Getting real value out of the custom code steps requires actual engineering skill, this isn’t built for non-technical teams. The dev environment also caps out at 10 users, which limits testing room before something ships. Plans start at $29 a month.

Best for: developer teams that want broad app coverage with the option to write custom logic at specific steps, particularly for webhook-driven backend workflows.

5. Tray.ai

Tray.ai is built for organizations that need workflow automation, API management, and agent deployment under one roof rather than three separate vendors. Its Agent Gateway for MCP gives teams a governed path for creating and publishing MCP servers, supporting both a no-code builder called Merlin and a fully code-based path. The standout feature is composite tools, bundling several underlying API calls into a single action an agent can invoke, which cuts token usage meaningfully across multi-step workflows. With access to over 700 connectors and 10,000 tools, plus SOC 2, HIPAA, and GDPR compliance with regional hosting, this is built for companies standardizing how agents across departments touch business systems.

The overhead is real, though. Every plan is custom-priced, so there’s a sales process before you know what anything costs, and getting governance configured correctly generally requires dedicated technical staff.

Best for: regulated environments running complex, cross-system agent workflows where unmanaged tool access isn’t an option.

6. Nango

Nango takes the most code-first approach on this list. It’s open source, and instead of picking integrations from a fixed catalog, your engineers write the integration logic themselves as TypeScript running inside Nango’s serverless runtime. The reason teams choose this path is control over the data model. Nango’s MCP server uses strict typed input and output schemas for tools your team builds and exposes to agents inside your own product, which matters when a pre-built tool’s generic shape doesn’t match how your application structures data. White-label OAuth across more than 800 APIs and durable data sync for keeping external SaaS data consistent with your internal database round out the picture, with self-hosting available on the enterprise tier.

There’s no way around the tradeoff. This requires real engineering investment, no visual builder, no no-code shortcut, every tool written by hand. Plans start at $50 a month.

Best for: product teams shipping AI agents directly to customers, where integration logic has to match a proprietary data model rather than a generic template.

7. Mint MCP

Mint MCP describes a category, lightweight, purpose-built MCP servers that individual developers build to wrap one specific API as a callable tool. There’s no central company behind it, no unified support line, no shared roadmap.

That’s actually the appeal for a certain kind of team. If you need exactly one external API exposed to your agent and nothing else, standing up a full integration platform is overkill. A scoped, narrow MCP server gets the job done with no subscription and no platform account required. The obvious tradeoff is that you’re on your own. Documentation quality, reliability, and how gracefully a server handles upstream API changes all depend entirely on whoever built that particular implementation, and there’s no fallback support channel when something breaks.

Best for: narrow, single-API use cases where a purpose-built server makes more sense than onboarding a full platform.

Final Thoughts

The platforms in this list solve different parts of the MCP stack. Some focus on tool access and connectivity, such as MCP360 and Composio. Others are built around workflow orchestration, including Make, Pipedream, and Tray.ai. Platforms like Workato emphasize governance and control, while Nango and Apideck are aimed at teams building integrations into their own products. Mint MCP sits at the opposite end of the spectrum, offering lightweight, purpose-built servers for narrow use cases.

As MCP adoption moves beyond experimentation, teams are placing more attention on integration management, permissions, workflow design, and operational overhead. The platforms covered here reflect those different priorities, making the selection process less about finding the platform with the most features and more about finding one that matches how your team works.

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Top 7 MCP Servers for Teams in 2026

Most real work is not individual. Code is reviewed across branches. Designs move between designers and developers. Databases are shared, credentials are rotated, and context that matters to one person needs to be accessible to everyone else.

MCP (Model Context Protocol) makes it possible to connect AI directly to the tools and systems a team already operates, so queries and actions run against shared sources of truth rather than isolated, single-session context. Not every MCP server is built with teams in mind, though. Some are designed for local machines. Others have governance gaps that are acceptable friction for a solo developer but become real problems at team scale.

This post covers the seven best MCP servers for teams in 2026, with a focus on what each one does and where it fits best in a team environment.

1. MCP360

MCP360 is a unified gateway for connecting AI workflows to multiple external services. Rather than configuring a separate MCP connection for each tool, everything is registered and managed through one dashboard. Authentication, retries, access controls, and response formatting are all handled in one place. It supports standard third-party services as well as custom internal APIs.

For teams, it solves a coordination problem that quietly absorbs engineering time: who owns the credentials, who updates them when they rotate, which workflows have access to which tools, and where to look when a connection fails.

Best For

Engineering and operations teams running AI workflows across five or more services, including a mix of databases, internal APIs, third-party integrations, and communication tools. Especially valuable when multiple people need access to the same connections or credentials rotate regularly.

2. Playwright MCP

Playwright MCP connects an AI agent to a live browser using Microsoft's Playwright library. It navigates pages, clicks elements, fills forms, and reads content from fully rendered pages after JavaScript has loaded. Pages are processed through accessibility snapshots rather than screenshots, keeping things fast without requiring a vision model. Every step is logged with a pass or fail result, and the browser session stays open between messages so multi-step workflows run in full.

Best For

QA engineers and developer teams building, testing, or documenting browser-based workflows. Useful for drafting Playwright test scripts in plain language, verifying flows, scraping rendered content, and debugging multi-step interactions without writing automation code from scratch.

3. Context7 MCP

Context7, built by Upstash, fetches current documentation for third-party libraries and adds the relevant sections to each request before any code is written. When a library is mentioned, Context7 identifies it and retrieves the matching documentation automatically. It covers over 9,000 libraries, runs as a hosted service with nothing to install locally, and scopes retrieval to what was actually asked.

For teams, the value is consistency. Senior engineers stay current on the libraries they use regularly, but junior engineers working outside their usual stack often do not. Context7 levels that out so everyone codes against actual documentation for the version in use, not training data that may be months or years behind.

Best For

Engineering teams of mixed seniority working against third-party libraries, especially across multiple languages or frameworks. High value in onboarding contexts where newer engineers benefit from accurate documentation being available on every request.

4. Obsidian MCP

Obsidian MCP gives an AI agent access to a local Obsidian vault, a collection of plain markdown notes connected by wikilinks. It can search the vault by title, content, tags, or frontmatter, follow links between notes, create new files that fit the existing structure, and return linked content rather than just the file requested.

Shared team use requires a sync layer such as Obsidian Sync, a shared cloud folder, or a self-hosted solution, with everyone pointing at the same vault location. It works well when the vault is actively maintained and consistently structured.

Best For

Small teams or departments that have already built a shared knowledge base in Obsidian and use it as a primary reference. Most useful when the vault is well-linked and the team follows consistent conventions around frontmatter and folder structure.

5. PostgreSQL MCP

PostgreSQL MCP connects an AI agent to a live PostgreSQL database through a connection string. Read and write permissions are configured independently, meaning SELECT access can be open for analysts while INSERT, UPDATE, and DELETE remain locked. Before writing any query, the agent reads the full schema so every query starts from the actual database structure.

Within a session, it can write a query, inspect the execution plan, identify slow joins or missing indexes, adjust, and rerun without switching tools.

Best For

Data teams, backend engineers, and analysts querying or exploring a shared PostgreSQL database. The permission separation makes it practical to give analysts read access over production data while keeping write operations restricted. Useful for query development, data quality checks, schema exploration, and identifying performance issues.

6. Figma MCP

Figma's official MCP server connects an AI agent to design files through OAuth. It accesses actual component data, including names, properties, spacing, design tokens, and variable collections, directly from the file rather than interpreting a screenshot. Color, typography, and spacing from variable collections flow through into generated code. Where Figma components have Code Connect mappings to their code equivalents, those mappings are used directly.

The design-to-engineering handoff is inherently a team workflow. Developers get the real values from the file, not estimated ones reverse-engineered from an image. A desktop version is available for enterprise environments with stricter data handling requirements.

Best For

Design and engineering teams working on design-to-code handoffs where the Figma file has proper component structure, named tokens, and Code Connect mappings in place. Output quality depends on how well the design file is organised.

7. Excalidraw MCP

Excalidraw MCP connects an AI agent to Excalidraw, a browser-based diagramming tool with a hand-drawn visual style. Describe the system or flow in plain language and the agent handles placement, connections, and layout. Changes appear in the open Excalidraw tab as they happen. Existing Mermaid diagrams can be converted directly into Excalidraw elements. It covers the common diagram types: architecture, flowchart, entity relationship, and sequence.

For distributed teams, the live update behaviour is particularly useful. A shared Excalidraw session means the diagram is visible to everyone as it is built or revised.

Best For

Engineering and architecture teams that need to produce shareable system diagrams quickly during early planning. Especially useful for distributed teams collaborating asynchronously, where describing a system in words and having a diagram generated immediately is faster than one person manually laying out shapes for others to review.

Conclusion

MCP adoption at the team level is still early, and that is precisely when the decisions matter most. Teams that establish clean integration patterns now, around shared credentials, consistent tool access, and reliable sources of truth, will not need to retrofit discipline onto workflows that have already calcified.

The right question is not which servers are available but where the team's work actually lives. The tool should follow the workflow, not precede it. Start with the server that connects to the system the team trusts most. From there, each additional connection should answer one question: what context is currently missing that slows the team down?

The teams that will get the most from MCP are not the ones with the most integrations. They are the ones that chose deliberately, structured their data well, and built habits around shared context. The infrastructure is here. What remains is the judgment to use it well.