What is MCP, in one paragraph?

The Model Context Protocol is an open standard for connecting AI models to tools and data sources. Instead of writing framework-specific tool definitions, you run an MCP server that exposes tools, resources and prompts, and any MCP-capable client can use it. It decouples your integrations from whichever model and framework you happen to be using this quarter.

Do I need MCP for a simple agent?

No. If you have three tools and one client, native function calling is less machinery and perfectly sound. MCP earns its place when the same integration needs to serve several clients, several teams, or a product that will outlive its current framework choice.

Is browser automation a reasonable production strategy?

As a deliberate fallback, yes, with eyes open. Use it where no API exists, keep the surface small, screenshot every failure, alert on them, and make sure a human is between the agent and anything irreversible. Do not use it to avoid the work of an integration that does exist.

What's the biggest security mistake with agent tools?

Granting broad write access and trusting tool output. An agent that can read untrusted content and also delete records is one prompt injection away from an incident. Scope credentials to the minimum, separate read tools from write tools, and require explicit human confirmation for anything destructive.

Agent Library · Tools

Giving an agent hands: MCP servers and browser control

The Model Context Protocol (MCP) is the standard way to connect an AI agent to external tools and data, and the modelcontextprotocol/servers repository is the best reference implementation to learn from. For systems that expose no API at all, browser-use drives a real browser as a fallback. Both are free, open source, and the difference between a chatbot and an agent that does something.

Free. 2 repositories, no payment. One email address opens the list.

A model with no tools is a chatbot. The whole value of an agent is that it goes and does a thing: reads the calendar, files the ticket, pulls the order, updates the record.

So the tool layer isn't a detail at the end of the build. On our projects it's usually where most of the engineering actually goes, which surprises people who budgeted for prompt writing.

What changed recently is that this stopped being bespoke. Before MCP, every integration was a hand-rolled function definition, coupled to one framework and one model, rewritten whenever either changed. MCP made the connection a protocol, so a server you write once is reachable from anything that speaks it. Read two of the reference servers and you'll understand it better than from any explainer, including this one.

Then there's the other half of reality. The system your client depends on has no API, the vendor has no roadmap for one, and nobody is switching. That's where browser-use comes in: an agent driving a real browser. It works, we've shipped it, and I want to be straight about it. It is brittle by nature. A layout change breaks it. Budget for maintenance from day one and never put it anywhere silent failure costs someone money.

One security note, because it gets skipped. The moment your agent reads content from the open web or from a document a stranger uploaded, that content can contain instructions. Treat everything a tool returns as data, never as orders, and keep destructive actions behind a confirmation a human gives.

This week: list every tool your agent can call, and mark which ones can write. If that second list is longer than you expected, that's the finding.

Where to send the 2 repositories

Give me an email address and the full list opens on this page straight away, with a copy in your inbox you can search later, plus a note whenever we publish something new on AI and where mobile development is going. No payment, now or ever. That's the only trade.

Tools: the part that makes an agent useful

A model with no hands is a chatbot. This is how you give it hands, and how you keep the connection standard instead of bespoke.

2 repositories, with the notes on each. Enter your email above to read them.

The master list: all 100 repositories

The free list above is the short version, and every repository on it is in here too. The Gods Repo Library is the whole catalogue: 100 repositories across 12 categories, each with a written note on what it is for and when to reach for it, and it carries the AI optimisation half, inference, serving, quantisation and evaluation, that the free list barely touches. Every figure in it is read from the GitHub API rather than typed by hand, and nothing archived or idle survives the build. Verified 2026-09-23.

Orchestration: where the agent lives19
Coding agents you can point at your own repo6
MCP, tools and the integration layer10
Browser and computer control5
Retrieval: giving the model your data9
Memory and vector stores7
Evaluation, observability and guardrails11
Prompt and output optimisation5
Inference, serving and routing10
Fine-tuning, quantisation and compression7
Getting the data in5
Courses, cookbooks and reference6

$9.99once, for all 100 repositories, sent to your inbox

All 100 repositories with a written note on each, emailed to you
88 of them are beyond the free list above
A permanent link to the live version, refreshed against the GitHub API
No subscription and no account, just the library

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