About Tendem MCP server
The Tendem MCP plugin lets an AI assistant delegate judgment-heavy work, such as research, writing and data cleaning, to vetted human experts. Tasks are submitted in plain English and results return to the chat as files or reports. This repository holds the client-side plugins and skills, while the MCP server is hosted externally.
Key features
- 01Task submission in plain English
- 02Transparent quotes and scope negotiation
- 03File upload and download for results
- 04Built-in polling and spend safety checks
- 05Plugins for multiple MCP clients
Use cases
- 01Delegating competitive analysis and market research
- 02Having experts review or edit copy
- 03Cleaning and analysing data that needs human judgment
MCP support
The repository provides client-side plugins that connect to the hosted MCP server at https://mcp.tendem.ai/mcp and guide the assistant through the create, approve and fetch task lifecycle.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mcp.tendem.ai/mcp?utm_hash=b093faed05claude mcp add --transport http toloka-tendem https://mcp.tendem.ai/mcp?utm_hash=b093faed05{"mcpServers":{"toloka-tendem":{"type":"http","url":"https://mcp.tendem.ai/mcp?utm_hash=b093faed05"}}}Open in Cursor{"servers":{"toloka-tendem":{"type":"http","url":"https://mcp.tendem.ai/mcp?utm_hash=b093faed05"}}}Open in VS Code{"mcpServers":{"toloka-tendem":{"type":"http","url":"https://mcp.tendem.ai/mcp?utm_hash=b093faed05"}}}{"mcpServers":{"toloka-tendem":{"serverUrl":"https://mcp.tendem.ai/mcp?utm_hash=b093faed05"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMETendem MCP
Delegate tasks to vetted human experts — straight from your AI assistant. 🧑🔬🤝🤖
Tendem gives your AI direct access to vetted human experts for the work an agent alone can't nail: judgment-heavy research, competitive analysis, copywriting and editing, design review, data cleaning, and complex multi-step tasks. You (or your agent) submit a task in plain English — Tendem scopes the work, quotes a transparent price, and delivers verified results back into the chat as files, reports, or structured outputs. Agent plus expert raises the quality ceiling of what you can ship well beyond what either does alone.
Ideal for founders, creators, operators, and consultants who need reliable expert output without managing freelancers.
Get started → agent.tendem.ai.
This repo is the official distribution home for the Tendem MCP plugin — the part that lives in your AI client. Install it and your assistant arrives already knowing how to drive the full Tendem lifecycle: create → scope → approve → fetch, with clean polling and spend safety built in.
The MCP server itself is hosted at
https://mcp.tendem.ai/mcp— it is not in this repo. This repo contains the client-side plugins, skills, and rules that teach your AI client to use the hosted server well.
Start using Tendem
1. The web interface
Sign in at agent.tendem.ai to create your account, top up your balance, and watch tasks run end-to-end. The full output of every task — markdown plus downloadable files — is always available there, no matter which client you launched it from.
2. Install the plugin in your AI client
The hosted server URL is the same everywhere: https://mcp.tendem.ai/mcp
(streamable HTTP, OAuth on first use). Pick your client:
Claude Code
/plugin marketplace add Toloka/tendem-mcp
/plugin install tendem@tendem-mcp
OpenAI Codex / ChatGPT
codex plugin marketplace add Toloka/tendem-mcp
codex plugin add tendem@tendem-mcp
Complete the MCP server's OAuth login on first tool use.
GitHub Copilot CLI
copilot plugin marketplace add Toloka/tendem-mcp
copilot plugin install tendem@tendem-mcp
Gemini CLI
gemini extensions install https://github.com/Toloka/tendem-mcp
Then authenticate inside Gemini with /mcp auth tendem. The catalog entry is
gemini-extension.json at the repo root.
Kiro
Kiro Powers register through the IDE. Point Kiro at the
tendem-power/ steering bundle in this repo, then connect the
tendem MCP server when prompted.
Manual (any MCP-compatible client)
Add to your client's MCP config and sign in when prompted:
{
"mcpServers": {
"tendem": {
"type": "http",
"url": "https://mcp.tendem.ai/mcp"
}
}
}
Build agentic pipelines (API-key auth)
For headless, CI, or programmatic use — where the interactive OAuth flow isn't viable — authenticate with a Tendem API key instead: sign in at agent.tendem.ai/mcp and create one under the Agent builders tab.
Python / LangChain / LangGraph: skip the raw MCP wiring and use
langchain-tendem — four price-capped
tools that let an agent create a task, answer the service's questions, and
collect the verified result, with all polling in plain Python (no LLM, no
tokens).
For every other stack, a native "type": "http" remote server triggers the
interactive OAuth flow, so
to pass a static API key instead, bridge the connection through
mcp-remote — a local stdio process
that connects to the hosted URL and injects your Authorization: ApiKey header:
{
"mcpServers": {
"tendem": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://mcp.tendem.ai/mcp",
"--transport",
"http-only",
"--header",
"Authorization: ApiKey <your-tendem-api-key>"
]
}
}
}
Keep the token out of source control (inject it from an environment variable or
secret store). Your custom agent loop drives the same eleven tools the plugin
uses — create_task, get_task, get_contract, get_task_result,
list_tasks, approve_task, cancel_task, send_message, read_chat,
get_account, get_file_upload_url — to implement the create → scope →
approve → poll → fetch lifecycle yourself. The server ships a
tendem-quickstart prompt with a full walkthrough.
Spend safety:
approve_taskis what triggers a charge. In an automated pipeline, gate it behind your own approval logic and checkget_accountfirst — an insufficient balance returns a task-bound top-up URL, not an error to retry.
What's in this repo
| Path | What it is |
|---|---|
plugins/tendem/ | The plugin for Claude Code, Cursor, and GitHub Copilot CLI — skill, slash commands, background watcher agent, notification hook, and the MCP connector |
codex/tendem/ | The plugin for OpenAI Codex / ChatGPT — explicit $-skills and Codex-specific file-upload guidance |
tendem-power/ | The Kiro Power (steering + MCP config) |
gemini-extension.json | The Gemini CLI extension manifest (root install) |
.claude-plugin/marketplace.json | This repo's Claude Code marketplace |
.agents/plugins/marketplace.json | This repo's Codex marketplace |
server.json | MCP Registry manifest (remote server) |
llms.txt | LLM discovery index |
What the plugin teaches your assistant
Installing does more than wire up the connector — it ships steering so your AI handles Tendem correctly out of the box:
tendem-tasksskill — the end-to-end path (create → scope → approve → fetch), silent long-polling without busy-loops, scope negotiation, and live-verified file upload/download mechanics. Loads only when work actually involves Tendem./tendem-task,/tendem-status,/tendem-result— explicit entry points to submit, check, and fetch a task (in Codex these are$tendem-tasketc.).tendem-watcheragent (Claude / Copilot) — a background watcher that polls a running task at a slow pace and pings you the moment it needs you.- Notification hook — a desktop notification when a task transitions to needing you (quote ready, top-up needed, input needed, result ready).
For contributors
- AGENTS.md — guide for AI agents working in this repo
- CONTRIBUTING.md — how to contribute
- Per-client manifests: Claude Code, OpenAI Codex, Cursor, GitHub Copilot CLI, and Gemini CLI
Links
- Product: tendem.ai · App: agent.tendem.ai
- Overview: Connect your AI agent to human experts via MCP
- Legal: Privacy Policy · Terms of Use
- Support: Help center
Tendem MCP is part of the Model Context Protocol ecosystem.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.