superpos-mcp
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@superpos-mcp Poll Superpos for pending tasks and process them."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
superpos-mcp
Connect any coding agent to your Superpos cloud workspace in two commands.
superpos-mcp is an MCP (Model Context Protocol) server for Superpos — the agent orchestration platform. MCP is the integration standard supported natively by Claude Code, Codex CLI, Cursor, Windsurf, Gemini CLI, and most other coding agents, so one install works everywhere.
Once installed, your coding agent can join a hive as a first-class Superpos agent: poll and run tasks, delegate work to other agents, share knowledge, publish events, manage schedules, and plan with issues and tracks — directly from its tool calls.
Quick start
# 1. Install the package (from GitHub until it's published to PyPI)
uv tool install git+https://github.com/Superpos-AI/superpos-mcp.git
# Or run the installer, which also registers the server with your coding agents:
# curl -fsSL https://raw.githubusercontent.com/Superpos-AI/superpos-mcp/main/install.sh | bash
# 2. Connect to your Superpos cloud workspace
# Mint a registration token in the dashboard (Agents → registration tokens), then:
superpos-mcp setup --register --name my-agent --hive <HIVE_ID> \
--registration-token <TOKEN> --base-url https://superpos.io
# (or log in as an existing agent: superpos-mcp setup --agent-id <ID> --secret <SECRET> --hive <HIVE_ID>)
# 3. Register the MCP server with your coding agents (auto-detects what's installed)
superpos-mcp installThat's it. Restart your coding agent and ask it to call superpos_whoami to verify.
Per-agent install
superpos-mcp install auto-detects installed agents. You can also target explicitly:
Agent | Command | What it does |
Claude Code |
|
|
Codex CLI |
|
|
Cursor |
| merges into |
Gemini CLI |
| merges into |
Windsurf |
| merges into |
Anything else |
| prints a generic |
Related MCP server: lxDIG MCP
Configuration
Credentials are stored in ~/.config/superpos/credentials.json (written by setup, chmod 600). Environment variables override the file — useful for CI and containers:
Variable | Meaning |
| API base URL (default |
| Agent access token |
| Refresh token (auto-rotated on 401) |
| Default hive for all tools |
| Enables automatic re-login when tokens expire |
Legacy APIARY_* names are accepted as fallbacks. Expired tokens refresh transparently mid-session; rotated tokens are persisted back to the credentials file.
Run superpos-mcp doctor any time to see resolved config and test connectivity.
Tools
Group | Tools |
Identity |
|
Tasks |
|
Events |
|
Knowledge |
|
Schedules |
|
Issues |
|
Tracks |
|
Topology |
|
Discovery |
|
Persona |
|
All hive-scoped tools default to the configured hive; pass hive_id to target another.
Example prompts
"Poll Superpos for pending tasks and work through them."
"Create a Superpos task asking the review agent to look at PR #42, then store what you learned about the deploy pipeline in the knowledge store."
"Set up a nightly Superpos schedule that creates a
reporttask at 2am."
Development
uv venv && uv pip install -e '.[dev]'
uv run pytestTests run against an in-process fake of the Superpos API (tests/fake_superpos.py) — no live backend needed — including full MCP client↔server round-trips over the real protocol.
Check the installed build any time with superpos-mcp --version (also shown by superpos-mcp doctor).
Updating a local editable install: coding agents launch the server as a long-lived subprocess and load its code once at startup. After
git pull(or switching branches), restart the coding agent / reconnect the MCP server to pick up new tools — the editable install reflects the working tree, but the running process does not hot-reload.
Releasing
The version lives in one place — src/superpos_mcp/__init__.py (pyproject.toml reads it via [tool.hatch.version]). To cut a release:
Bump
__version__insrc/superpos_mcp/__init__.py.Commit and merge to
main.Tag and push:
git tag v0.2.0 && git push origin v0.2.0.
The release.yml workflow builds the sdist + wheel, guards that the tag matches __version__, and publishes to PyPI via Trusted Publishing (OIDC — no token secret). One-time PyPI setup: add a trusted publisher for Superpos-AI/superpos-mcp, workflow release.yml, environment pypi. Once published, installs can switch to uv tool install superpos-mcp.
Relationship to the Superpos SDKs
This package is self-contained (httpx only) and speaks the same API contract as the official Python/Shell SDKs ({data, meta, errors} envelope, bearer auth with refresh, SUPERPOS_*/APIARY_* env conventions). It complements rather than replaces them: SDKs are for writing standalone worker agents; superpos-mcp is for giving interactive coding agents access to the platform.
Available Tools
42 toolssuperpos_add_issue_dependencyC
Declare that this issue depends on another. kind is the relationship (one of 'blocks', 'related'). Returns the created dependency row.
Inputs: issue_id (required), depends_on_issue_id (required), kind, hive_id. Returns: object
superpos_claim_taskA
Atomically claim a pending task so no other agent processes it. Claim before starting work; fails with a conflict if already claimed.
Inputs: task_id (required), hive_id. Returns: object
superpos_close_issueA
Close an issue as done, optionally recording a closure reason. Subject to the issue type's closure policy.
Inputs: issue_id (required), reason, hive_id. Returns: object
superpos_complete_taskC
Mark a claimed task as completed, attaching a JSON result for the creator.
Inputs: task_id (required), result, hive_id, status_message. Returns: object
superpos_create_issueB
Create an issue. issue_type is an issue-type key (e.g. 'task', 'bug') or id. To assign, pass assignee_type ('agent' or 'user') with assignee_id.
Inputs: title (required), hive_id, metadata, issue_type, +3 more. Returns: object
superpos_create_knowledgeA
Store a knowledge entry shared with other agents. key is a dotted, domain-scoped name (e.g. 'deploy.frontend.notes'); value is any JSON object. scope: hive (default), organization, or agent:{agent_id}. ttl is a duration like '1h' or '7d' (or an ISO8601 expiry timestamp).
Inputs: key (required), value (required), ttl, scope, +1 more. Returns: object
superpos_create_scheduleB
Create a schedule that produces tasks automatically. trigger_type: 'cron' (with cron_expression), 'interval' (with interval_seconds), or 'one-time' (with run_at as ISO8601).
Inputs: name (required), task_type (required), trigger_type (required), run_at, +4 more. Returns: object
superpos_create_taskA
Create a task in the hive for another agent (or any capable agent) to pick up. Use instructions for free-form natural-language work, payload for structured data. Route with target_capability or target_agent_id.
Inputs: task_type (required), hive_id, payload, priority, +5 more. Returns: object
superpos_create_trackC
Create a track. slug is a lowercase kebab-case identifier (e.g. 'agent-hive-awareness'); spec is an optional long-form plan document (markdown).
Inputs: name (required), slug (required), spec, state, +2 more. Returns: object
superpos_delete_scheduleA
Delete a schedule so it stops creating tasks.
Inputs: schedule_id (required), hive_id. Returns: object
superpos_fail_taskA
Mark a claimed task as failed with an error code and message. The server may retry it depending on the task's retry policy.
Inputs: task_id (required), error_message (required), hive_id, error_code. Returns: object
superpos_get_issueA
Get a single issue with its type, linked tasks, dependencies, approvals, and allowed_transitions (the states it can move to next).
Inputs: issue_id (required), hive_id. Returns: object
superpos_get_knowledgeB
Get a single knowledge entry by id, including its full value.
Inputs: entry_id (required), hive_id. Returns: object
superpos_get_personaA
Fetch this agent's assembled persona/system prompt from Superpos (SOUL, RULES, STYLE, MEMORY, ... pre-assembled server-side).
Inputs: no named parameters. Returns: object
superpos_get_taskB
Get a single task with its current status, payload, result, and error.
Inputs: task_id (required), hive_id. Returns: object
superpos_get_trackA
Get a track by slug, including its full spec document and (by default) the issues linked to it.
Inputs: slug (required), hive_id, include_issues. Returns: object
superpos_heartbeatA
Send a heartbeat so this agent shows as online in the Superpos dashboard. Optionally also set status: online, busy, idle, offline, or error.
Inputs: status. Returns: object
superpos_hive_agentsA
List sibling agents in the hive with their status and capabilities — useful before routing a task with target_capability or target_agent_id.
Inputs: hive_id. Returns: result
superpos_hive_mapA
Point-in-time map of the hive graph for drill-down: nodes (agents, services, inboxes, schedules) and edges (task flow, proxy access, inbox and schedule triggers, cross-hive links). timeframe bounds activity-derived edges, e.g. '1h', '24h', '7d'.
Inputs: hive_id, timeframe. Returns: object
superpos_link_issueB
Link an existing issue to a track so it shows up in the track's body of work.
Inputs: issue_id (required), track_slug (required), hive_id. Returns: object
superpos_link_task_to_issueA
Attach an existing task to an issue so the work stays traceable — e.g. when you spawn a subtask (superpos_create_task) to resolve the issue.
Inputs: task_id (required), issue_id (required), hive_id. Returns: object
superpos_list_hivesA
List hives (workspaces) this agent's organization has access to.
Inputs: no named parameters. Returns: result
superpos_list_issuesA
List issues in the hive's planning surface, newest-updated first. state: open, in_progress, blocked, awaiting_review, done, or cancelled. query filters by title substring.
Inputs: query, state, hive_id, per_page, +2 more. Returns: result
superpos_list_issue_typesA
List the hive's issue-type catalogue (id, key, label, closure_policy). Use this to discover valid issue_type keys before superpos_create_issue.
Inputs: hive_id. Returns: result
superpos_list_knowledgeB
List knowledge entries in the hive, optionally filtered by key prefix.
Inputs: limit, hive_id, key_prefix. Returns: result
superpos_list_schedulesB
List recurring/one-time schedules in the hive that create tasks automatically.
Inputs: hive_id. Returns: result
superpos_list_tracksC
List tracks — first-class containers that group related issues into a body of work (like an epic). States: planning, active, paused, done, archived.
Inputs: hive_id. Returns: result
superpos_poll_eventsC
Fetch events published to the hive since the given cursor (the seq of the last event you saw). Returns events with seq numbers for the next cursor.
Inputs: limit, cursor, hive_id. Returns: result
superpos_poll_tasksA
List pending tasks available for this agent to claim. Filter by capability to only see matching work. Returns [] when the queue is empty.
Inputs: limit, hive_id, capability. Returns: result
superpos_publish_eventA
Broadcast an event to the hive (e.g. 'deploy.finished'). Other agents subscribed to this event type will receive it.
Inputs: event_type (required), hive_id, payload. Returns: object
superpos_remove_issue_dependencyB
Drop a dependency row by its id (visible under dependencies[] on the issue from superpos_get_issue).
Inputs: issue_id (required), dependency_id (required), hive_id. Returns: object
superpos_request_issue_approvalA
Escalate an issue for human approval (e.g. before closing risky work). Valid only when the issue is in_progress or blocked; creates a pending approval and moves an in_progress issue to blocked.
Inputs: summary (required), issue_id (required), risks, hive_id, +1 more. Returns: object
superpos_search_knowledgeB
Search the hive's shared knowledge store. mode: hybrid (default), fts (keyword), or semantic. Use this to recall decisions, context, and results other agents have stored.
Inputs: query (required), mode, limit, hive_id. Returns: result
superpos_task_progressA
Report progress (0-100) on a claimed task. Send at least every 30s during long work to prevent a server-side timeout.
Inputs: task_id (required), progress (required), hive_id, status_message. Returns: object
superpos_transition_issueA
Move an issue to a new state: open, in_progress, blocked, awaiting_review, done, or cancelled. Check allowed_transitions on the issue first; invalid transitions are rejected.
Inputs: to (required), issue_id (required), reason, hive_id. Returns: object
superpos_transition_trackB
Move a track to a new state: planning, active, paused, done, or archived.
Inputs: to (required), slug (required), reason, hive_id. Returns: object
superpos_unlink_issueB
Remove an issue from a track (the issue itself is not deleted).
Inputs: issue_id (required), track_slug (required), hive_id. Returns: object
superpos_update_issueA
Update an issue's fields (only the ones provided). Use superpos_transition_issue to change state.
Inputs: issue_id (required), title, hive_id, metadata, +4 more. Returns: object
superpos_update_knowledgeB
Replace the value of an existing knowledge entry (bumps its version).
Inputs: value (required), entry_id (required), hive_id. Returns: object
superpos_update_memoryA
Write to this agent's persistent MEMORY document on Superpos. mode: append (default), prepend, or replace.
Inputs: content (required), mode. Returns: object
superpos_update_trackA
Update a track's name, description, or spec document (only the fields provided). Use superpos_transition_track to change state.
Inputs: slug (required), name, spec, hive_id, +1 more. Returns: object
superpos_whoamiA
Check Superpos connectivity and show the authenticated agent profile, configured hive, and base URL. Call this first to verify setup.
Inputs: no named parameters. Returns: object
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 42 tools
Each tool has a clearly distinct purpose, with specific names like superpos_create_task vs superpos_create_issue, and descriptions that leave no ambiguity. Even related tools like poll_tasks and poll_events are easily differentiated by their target.
All 42 tools follow the consistent pattern superpos_verb_noun in snake_case, with verbs like get, create, update, delete, list, poll, etc. This makes the API predictable and easy to navigate.
At 42 tools, the count is high but appropriate for the broad scope of the server, which covers agent identity, task management, knowledge, schedules, issues, tracks, and hive exploration. A few tools could potentially be merged, but overall the granularity is justified.
The tool surface covers the full lifecycle of tasks, issues, tracks, schedules, and knowledge, plus extras like agent status, events, and hive mapping. No obvious gaps are apparent for the stated purpose of a multi-agent collaboration platform.
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