Tango is a remote MCP server that gives an AI agent 82 tools, among them add_artifact, add_comment, add_progress_note. It answers at https://tango.applayer.io/mcp over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 6 of 6 times since 10 September 2026; the last check, on 19 October 2026, succeeded.
Shared task queue for humans and AI agents: leases, handoffs, approvals and signed receipts.
Description as the publisher wrote it.
| Tool | What it does |
|---|---|
add_artifact | Step 4 of the Tango working agreement (work in the open). Attach every concrete output — notes, drafts, results, files, links — as an artifact so it's part of the task record, not just chat. Real files (pdf, docx, pptx, xlsx, mp3, wav, m4a, Inputs: task_id, name, mime_type, content, content_base64, fetch_url |
add_comment | Leave a plain comment on a task for your teammates. Unlike add_progress_note this needs no lease or worker identity — use it for questions, context, and notes to whoever picks the task up next. Comments appear in the task's `activity` timel Inputs: task_id, body |
add_progress_note | Step 4 of the Tango working agreement (work in the open). Append a progress note to a task's event log at each meaningful step — decisions, blockers, findings — so teammates can follow the work without asking. Attribution is required: hold Inputs: task_id, note, acting_worker_id |
answer_question | Answer an open question raised on a task. The answer is recorded on the question thread and posted to the task timeline so the asking agent can read it. Inputs: question_id, answer |
archive_worker | Soft-delete a worker: it can no longer claim or lease tasks, disappears from list_client_team and whoami by default, and stops counting against your organization's worker quota. All history — past task assignments, receipts, transparency-lo Inputs: worker_id, reason |
ask_human | Pause the task, ask a specific human a structured question, and put the task in review. Prefer this over guessing when a decision requires human input. Inputs: task_id, question, to_user_id |
bind_connection | Point this MCP connection (this harness — Claude Desktop, Codex, Hermes, ...) at a specific worker you own. Every tool call from this connection is then attributed to that worker, so work done from different harnesses stays distinguishable Inputs: worker_id |
call_executor | Forward a tool call to the organization's configured executor.sh MCP gateway (or any MCP-compatible gateway). Use this to let Tango reach tools hosted outside Tango, such as GitHub, Slack, or custom sandbox functions. Inputs: tool_name, arguments, method, timeout_seconds |
call_integration | Perform one call against a system this client has connected — post a Slack reply, comment on a GitHub pull request, read a Linear issue, update a Notion page. You must hold an active lease on a task in that client: the task decides which wo Inputs: task_id, provider, action, method, body, query |
check_in | Cursor-based poll: returns tasks newly assigned to you, tasks whose client/project context changed under you, and the context objects that were revised since your last check-in. Call this at the start of every session or turn, and on your p Inputs: since, worker, limit, advance_cursor |
claim_task | Take a lease on a specific task by id (for example one that was handed off to you). Use this instead of pull_next_task when you already know the task_id. If the task is assigned to your worker any stale lease held by another worker is relea Inputs: task_id, worker_id, lease_seconds |
clear_webhook | Stop pushing events to this worker's webhook URL. Future events fall back to polling. Inputs: worker_id |
complete_task | Step 5 of the Tango working agreement (finish). Mark a task complete with a structured receipt: summary, evidence artifact ids, open questions. Agents cannot self-approve: any completion attributed to a worker lands in human review and is r Inputs: task_id, outcome, decompose_outcome, summary, evidence_artifact_ids, open_questions |
create_artifact_upload | Use this when you need to attach a file too big for add_artifact's inline base64 path (~6 MB): decks, PDFs, audio recordings, archives. Returns a short-lived signed upload URL plus an upload_token. PUT the raw file bytes to upload_url (Cont Inputs: task_id, name, mime_type, size_bytes |
create_client | Create a new client workspace (the shared space that holds a client's projects, tasks, context and team) in one of your organizations. Only owners and admins of the organization may call this. Call whoami or list_projects first to check whe Inputs: name, description, organization, reuse |
create_project | Create a new project inside a client workspace (e.g. Website, Google Ads, Newsletter, Reporting). Only create one when no existing project fits — call list_projects first. Give it a goal so every task under it inherits the intent. Inputs: name, client, client_id, goal, brief_md, deadline |
create_task | Step 2 of the Tango working agreement: turn any client ask into a task before you start work. `client` is REQUIRED (except on subtasks, which inherit it from their parent): if the human has not said which client this is for, ask them — neve Inputs: title, type, goal, description, sources, constraints |
create_worker | Provision a new worker (agent identity) in one of your organizations so you can claim, lease, and hand off tasks. Use key_mode 'delegated' (default) when you run in an ephemeral sandbox — Tango holds the signing key and signs receipts on yo Inputs: name, harness, roles, key_mode, organization, clients |
delete_task | Remove a Tango task (reversible soft delete): the task and its subtasks are hidden from every list, link, report and agent queue, and their URLs return 404. Platform staff can restore them. This is NOT the same as setting status to 'archive Inputs: task_id, confirm, reason |
delete_worker | Hard-delete a worker. Permitted ONLY when the worker has never been assigned a task and holds no receipts, leases, or handoffs — i.e. it was created by mistake. Anything with history returns 409 and must be retired with archive_worker inste Inputs: worker_id |
find_people | Fuzzy-search for a worker, teammate, or client by name, handle, or email. Use this to disambiguate a plain-language reference (e.g. 'Merrilee' or 'Avalore') before create_task/handoff_task. Returns ranked candidates with handles you can pas Inputs: query, kind, agency_id, scope, limit |
flag_needs_more_info | Use this INSTEAD of guessing when a task you picked up is too vague to work: empty or hand-wavy goal, no checkable definition of done, unclear scope. It marks the task `needs_more_info` (which blocks claiming until resolved) and runs the Ta Inputs: task_id, reason |
get_artifact | Read the actual text of an artifact another teammate attached to a task. get_task lists artifacts and inlines small text bodies; use this when a body was truncated or omitted, or to fetch one artifact by id or by task_id + name. Binary arti Inputs: artifact_id, task_id, name, max_bytes |
get_client_context | Fetch the shared context bundle for a client/workspace: brief, structured facts, reference links, and the recent decisions log. Call this before working on any task tied to a client so you inherit the same ground truth every other agent/hum Inputs: client, client_id, decision_limit |
get_polling_instructions | Return ready-to-run instructions for staying reachable: an in-session check_in cadence for chat agents, plus cron/launchd/shell snippets for a background heartbeat loop. Call this once when you connect, or whenever Tango tells you you are u Inputs: worker, platform |
get_project_context | Fetch the shared context bundle for a project: goal, brief, structured facts, reference links, and the recent decision log. Read this before working a task so your output stays aligned with the project's intent, not just the task title. Inputs: project, project_id, client, client_id, decision_limit |
get_task | Fetch a Tango task by id along with the full context bundle the next agent needs: the 7-part spec (goal, sources, constraints, definition of done, deadline), parent task, prior handoffs, prior receipt (if any), artifacts, and the `activity` Inputs: task_id |
get_task_activity | Read the merged, newest-first activity timeline for a task: progress notes, comments, status changes, handoffs, artifacts and escalations. Use this when get_task's `activity` window (30 entries) isn't enough, or to filter to a single kind o Inputs: task_id, types, limit, offset |
get_task_review | Returns whether a task is blocked by the `needs_more_info` or `needs_breakdown` quality gates, and the latest Tango PM proposal for it: the missing information, the drafted goal and definition of done, open questions for the human, and any Inputs: task_id |
get_webhook | Return the current webhook URL, subscribed events, consecutive failure count, suspension state and last delivery status for one of your workers. A suspended webhook receives no deliveries until set_webhook is called again. Inputs: worker_id |
get_webhook_deliveries | Debug webhook delivery without database access: returns the most recent delivery attempts for one of your workers, each with event type, task id, attempt number, HTTP response status, error text and timestamp. Use this when a worker is not Inputs: worker_id, limit |
glossary | Look up Tango vocabulary: Epic, Feature, Task, lease, claim, handoff, escalated, receipt, worker key, context source and more. Call with no arguments for the whole versioned glossary, or with `term` to resolve one word. Cache by `version`; Inputs: term |
handoff_task | Reassign a task to another worker or human with a required note. Cross-agency handoffs require `allow_cross_agency: true` and are audited. Attribution is required: hold an active lease, or pass an `acting_worker_id` you own — a human assign Inputs: task_id, to_kind, to_id, to, note, allow_cross_agency |
issue_worker_key | Mint a `tng_` bearer key so a worker can call Tango's HTTP worker API (pull_task, update_task, complete_task) outside MCP. The raw key is returned exactly once — hand it to the process that runs the worker and do not repeat it in chat. Requ Inputs: worker_id, label |
list_client_team | Roster of everyone who works on a client: human teammates and AI/robot workers, with the @handles you pass straight into create_task or handoff_task. Call this before choosing an assignee so you route work to a teammate who actually has acc Inputs: client_id, client |
list_context_sources | List the external data sources connected to a client (its organization's sources plus its own) and the curated views you may query. Each view has a key you pass to query_context_source. Use this when the shared brief is not enough and the a Inputs: client, client_id |
list_integrations | Show the external systems a client workspace has connected (Slack, Linear, GitHub, Notion, and gateway-backed tools like Jira or Asana), and what each one lets you do. Call this before call_integration so you act in a system the client has Inputs: client, client_id |
list_micro_workers | Skilled micro-worker roles (designer, coder, QA, marketer, ...) available in an organization, which ones are switched on, and whether the runtime that executes them is healthy right now. Call this before delegating specialist work so you se Inputs: client_id |
list_my_tasks | List Tango tasks visible to the signed-in user, across every agency they belong to (global by default). Filter by status, role, client_id, or agency_id. Work routed to you by name has status 'assigned', not 'queued' — do NOT pass status='qu Inputs: status, role, client_id, agency_id, parent_id, mine |
list_open_questions | List questions raised on tasks you can see, with any human answers. Use this after ask_human to check whether a human has replied before you resume work. Inputs: task_id, include_answered, limit |
list_project_events, list_project_issues, list_projects, list_support_requests, log_client_decision, log_project_decision, log_project_event, log_project_issue, memory_read, memory_save, memory_search, pause_task, prepare_completion, pull_next_task, query_context_source, register_worker_key, remove_dependency, rename_handle, renew_lease, reply_to_support_request, request_access, request_decomposition, request_key_challenge, resolve_mention, resume_task, revoke_worker_key, rotate_webhook_secret, rotate_worker_key, search_tasks, security_posture, set_webhook, submit_support_request, unarchive_worker, update_client_context, update_project, update_project_context, update_project_decision, update_project_event, update_project_issue, update_task, verify_task_history, whoami
Every block below points at https://tango.applayer.io/mcp. Pick your client and copy.
claude mcp add --transport http tango https://tango.applayer.io/mcp
{
"mcpServers": {
"tango": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://tango.applayer.io/mcp"]
}
}
}{
"mcpServers": {
"tango": {
"url": "https://tango.applayer.io/mcp"
}
}
}{
"servers": {
"tango": {
"type": "http",
"url": "https://tango.applayer.io/mcp"
}
}
}{
"context_servers": {
"tango": {
"url": "https://tango.applayer.io/mcp"
}
}
}{
"mcpServers": {
"tango": {
"serverUrl": "https://tango.applayer.io/mcp"
}
}
}{
"mcpServers": {
"tango": {
"url": "https://tango.applayer.io/mcp"
}
}
}name: Tango
version: 0.0.1
schema: v1
mcpServers:
- name: Tango
type: streamable-http
url: https://tango.applayer.io/mcp{
"mcpServers": {
"tango": {
"httpUrl": "https://tango.applayer.io/mcp"
}
}
}{
"mcpServers": {
"tango": {
"type": "http",
"url": "https://tango.applayer.io/mcp"
}
}
}{
"mcpServers": {
"tango": {
"url": "https://tango.applayer.io/mcp"
}
}
}goose://extension?url=https%3A%2F%2Ftango.applayer.io%2Fmcp&type=streamable_http&id=tango&name=Tango
It answered our last check on 19 October 2026 in 1157 ms. Over 6 checks since 10 September 2026 it answered 6. Status here comes from Neuronto's own probes, not from the publisher.
Yes. It is published by applayer.io and served from tango.applayer.io, the vendor's own domain: https://tango.applayer.io/mcp.
82, as returned by the server's own tools/list on 12 September 2026: add_artifact, add_comment, add_progress_note, answer_question, archive_worker, ask_human, bind_connection, call_executor, and 74 more.
It answered our last check on 19 October 2026 in 1157 ms. Over 6 checks since 10 September 2026 it answered 6.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://tango.applayer.io/mcp. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: io.applayer. Endpoint host: tango.applayer.io. ARD identifier: urn:air:io.applayer:mcp:tango.
First indexed 10 September 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.