Tendem is a platform where human experts and AI agents complete high-stakes tasks. Submit a task in plain language. AI agents handle the volume. Human experts level up the the final output. What comes back is complete, accurate, and ready to act on. Built by Toloka.ai, a company that has spent more than a decade building human-in-the-loop quality systems for frontier AI labs. Trusted by founders, operators, and AI-native users who need reliable results.
This is the 2nd launch from Tendem by Toloka. View more
Tendem MCP Connector
Launching today
Tendem MCP Connector lets your AI call a human expert the same way it calls any other tool. AI alone is great until a task needs real judgment — and then it hands back work that's confidently wrong. Connect Tendem via MCP and your AI can pass that task to a vetted human: research that has to be right, fact-checking, data that needs correcting, a design or copy review — and the finished work comes straight back into the same chat. Hand off what AI can't finish alone, with confidence
Free
Launch Team / Built With
Tendem by Toloka
Hi everyone! Egor here, Head of Engineering at Tendem by Toloka 👋
You know that little “AI can make mistakes. Please double-check responses” line under every chat box? We built something for exactly that.
Your AI can now call a real human the same way it calls any other tool with the new Tendem MCP connector.
AI agents are great at planning and chaining tools together. But some tasks need real judgment and taste, and that’s where AI on its own falls short. So when your agent hits that wall, you just say “Use Tendem” and it hands the work off to a real human expert.
It’s a remote MCP server, not a local setup, so it runs right where you already work — Claude, Cursor, Codex, ChatGPT, OpenClaw, Hermes and more.
How it works: the AI scopes your task, routes it to a domain expert, and you get human-verified output back in the same chat. No switching tabs, no managing freelancers, no doing the last mile yourself.
The whole point is less slop: real human experts working inside your AI models and agents. As long as that “please double-check responses” disclaimer is still around, we think there’s a human who should be in the loop.
Happy to talk shop in the comments 🛠️
ConfettiTherapy.com
@elipski on my todo list to take it for a spin
Tendem by Toloka
Sharing a few lessons we picked up while building this connector. A technical note for anyone building MCPs - especially the async kind.
1. Boring protocol features win.
We deliberately skipped some shiny stuff - the async tasks SEP, elicitation, sampling. Host support for those is a patchwork that changes without warning. The lowest common denominator across every client is plain tools + polling, so we built on plain tools + polling. Less elegant, but runs everywhere. Additionally, some clients support scheduling, so polling becomes even more convenient for communication.
2. The agent talks to OUR agent (A2A).
`create_task` doesn't dump a ticket into a queue. It opens a conversation with our orchestrator, which asks clarifying questions like a decent PM would (including structured ones that are nicely displayed in the chat UI). The host agent answers from context on its own and only interrupts the user when money, scope, or a deadline is actually on the line. You can even ask "can you do this, and roughly how much?" before spending a cent. That capability-discovery step is the most underrated thing we built - knowing whether the work fits before actually paying.
3. Assume the model will retry, stall, and lose its place.
Models re-issue tool calls, so every mutation needs an idempotency key implemented in one way or another. Chat uses an offset cursor: send a message with a stale cursor and the server refuses to post it, handing back what you missed - so the agent re-reads and re-decides instead of talking over a reply it never saw. Race conditions on the agentic level :)
4. Spoon-feed the next step.
Never assume the model will derive state/next actions from a status enum. Enums describe "where the workflow is", but it's hard to tell "what to do next" from them alone. Every response carries next_action + when to poll + when to give up + a plain-language "guidance" string. The dumbest host and the smartest host follow the same breadcrumbs - they know exactly what's going on, what to do next, and when to escalate.
5. Money has no home in MCP.
There is no payment primitive in the protocol (some people would disagree, but it's true; there's no widely adopted one, yet). So approval and balance live in tools, the actual payment lives on our hosted page, and "insufficient balance" is NOT an error - it's a normal branch that returns a link which auto-approves the task once paid. Modeling it as an error would break every host that treats errors as dead ends.
6. The store review reshaped the product, not just the README.
To get into the Anthropic and OpenAI directories you don't just upload a manifest. You need public docs that match the connector exactly - every tool name, 3+ real example prompts, connection steps, troubleshooting, supported platforms, privacy policies, test accounts, and whatnot. Zero marketing language (literally: "describe what it does, not how great it is"). And the requirement that changed our positioning: you can't sell AI-generated output as the deliverable on these marketplaces (which is a bit questionable given the apps present in the ChatGPT marketplace). The human expert is the product; the AI only scopes and routes. We rewrote a lot of copy around that single line.
Bytes, by the way, never touch MCP - files move over pre-signed URLs, the server stays stateless, and "recovery" after a client restart is just "list my tasks," not "resume my socket." Uploading files from your agent for analysis is a bit harder than "downloading results," but it's already supported here as well.
ConfettiTherapy.com
@perrymason thanks sharing Viacheslav, very interesting stuff
Tendem by Toloka
I may be biased but.. I was really impressed by the simplicity of submitting my tasks to Tendem from Claude Cowork. I was literally just working in my session, typical workflow (needed to build a google ads campaign), got the Claude-generated results, and then when I needed to validate and refine them, I just asked Tendem to do it. It picked up my task, and what I got back a few hours later was better than I would have done it by myself. I felt confident that another human expert reviewed it and proposed structural improvements to the ad strategy. I saved hours of my time and it felt good.
@thisisgenya It's amazing to see new things 🚀
Tendem by Toloka
Guys, I'm telling you, once you wire this into your Claude, Cursor or ChatGPT you don't go back. Becoming more and more AI-native and shipping autonomous agents is the craze that's humming along, and "human in the loop" has basically become the base principle by now. Personally I want a future where AI gets things right — with a human QA layer at its center
ConfettiTherapy.com
congrats on the launch Team Toloka 👏
Tendem by Toloka
@thepetermick thanks for your support Peter!
MCP is a big value add for me, thank you! Having the human part join me where I'm already doing the AI part in my default place of work is super convenient!