Skip to main content
Glama

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 install

That'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

superpos-mcp install claude

claude mcp add --scope user superpos -- superpos-mcp serve

Codex CLI

superpos-mcp install codex

codex mcp add (or appends to ~/.codex/config.toml)

Cursor

superpos-mcp install cursor

merges into ~/.cursor/mcp.json

Gemini CLI

superpos-mcp install gemini

merges into ~/.gemini/settings.json

Windsurf

superpos-mcp install windsurf

merges into ~/.codeium/windsurf/mcp_config.json

Anything else

superpos-mcp install print

prints a generic mcpServers JSON snippet

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

SUPERPOS_BASE_URL

API base URL (default https://api.superpos.ai)

SUPERPOS_TOKEN

Agent access token

SUPERPOS_AGENT_REFRESH_TOKEN

Refresh token (auto-rotated on 401)

SUPERPOS_HIVE_ID

Default hive for all tools

SUPERPOS_AGENT_ID / SUPERPOS_AGENT_SECRET

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

superpos_whoami, superpos_heartbeat

Tasks

superpos_create_task, superpos_poll_tasks, superpos_claim_task, superpos_get_task, superpos_task_progress, superpos_complete_task, superpos_fail_task

Events

superpos_publish_event, superpos_poll_events

Knowledge

superpos_search_knowledge, superpos_list_knowledge, superpos_get_knowledge, superpos_create_knowledge, superpos_update_knowledge

Schedules

superpos_list_schedules, superpos_create_schedule, superpos_delete_schedule

Issues

superpos_list_issues, superpos_get_issue, superpos_create_issue, superpos_update_issue, superpos_transition_issue, superpos_close_issue, superpos_list_issue_types, superpos_link_task_to_issue, superpos_request_issue_approval, superpos_add_issue_dependency, superpos_remove_issue_dependency

Tracks

superpos_list_tracks, superpos_get_track, superpos_create_track, superpos_update_track, superpos_transition_track, superpos_link_issue, superpos_unlink_issue

Topology

superpos_hive_map

Discovery

superpos_list_hives, superpos_hive_agents

Persona

superpos_get_persona, superpos_update_memory

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 report task at 2am."

Development

uv venv && uv pip install -e '.[dev]'
uv run pytest

Tests 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:

  1. Bump __version__ in src/superpos_mcp/__init__.py.

  2. Commit and merge to main.

  3. 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 tools
superpos_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_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_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

    A3.7/5.0

    Scored across 42 tools

    Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count4/5

    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.

    Completeness5/5

    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.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Related MCP Connectors

    Related MCP Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      MCP server that enables AI coding agents to communicate, share state, and coordinate work in real time via MCP tools or REST API.
      84 npm
      10
      MIT
    • A
      license
      B
      quality
      D
      maintenance
      MCP server that gives AI coding assistants persistent memory, structural code graph analysis, and safe multi-agent coordination, enabling them to answer architectural questions, track decisions across sessions, and coordinate safely in multi-agent workflows.
      39
      4
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      An enterprise-grade MCP server that enables AI coding assistants to securely connect with external tools, APIs, databases, and cloud services through a unified interface, offering structured engineering workflows and multi-client support.
      MIT