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
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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.
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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
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