What can you do with this server?

The scrumdo-mcp server provides an MCP interface connecting AI tools to ScrumDo project management boards, enabling comprehensive management through the following capabilities: Board & Project Management * List boards, get board details (columns, custom fields, statistics), list iterations/sprints, labels, and epics Card Operations * List, search, retrieve, create, update, move, archive cards * Move cards between columns or iterations, assign/unassign members, add/remove labels, set custom fields Blockers * List active blockers, block cards with a reason (optionally as show-stopper or external dependency), unblock/resolve blockers Tasks (Checklists) * List, create, update, complete, reopen, and delete tasks on cards Comments * List, add (with Markdown support), and delete comments on cards Custom Fields * List field definitions, get/set individual or all custom field values on cards Members * List organization/project members, find by name, username, or email Search * Full-text search across card summaries, descriptions, and comments; search by custom field value Activity Logging * Log structured activity entries to cards; retrieve per-card or workspace-wide activity logs Webhooks * List, create, and delete project webhooks Time Tracking * List time entries (per card or project-wide), log time spent on a card Spec Management * Get, set, and patch card specs; view spec history; check spec drift GitHub Integration * List repos, link PRs/commits/issues to cards Agent Runs & Loops * Start, get, list, approve, report progress on, and cancel agent runs * Start/pause/resume/cancel loops, manage verification loops, log loop steps, attach evidence Intelligence Features * Velocity forecasts, spec complexity analysis, behavior contract verification

Which integrations are available for this server?

Provides tools for linking GitHub PRs, commits, and issues to ScrumDo cards, as well as listing GitHub repositories connected to the project.

How do I use scrumdo-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@scrumdo-mcp What's the status of ENG-42?" 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.

scrumdo-mcp

Official

by ScrumDoLLC

Overview Schema Related Servers Score Discussions(1)

Python

Remote

scrumdo-mcp

Connect any MCP-compatible AI tool (Claude Code, Cursor, Windsurf, and others) directly to your ScrumDo boards.

Once installed, your AI assistant can read cards, move them, create tasks, post comments, and search across your board — without you copy-pasting anything.

Installation

pip install scrumdo-mcp

Related MCP server: kanban-mcp

Cursor quick-start

1 — Install

# To run the server only:
pip install scrumdo-mcp

# To run tests too:
git clone https://github.com/ScrumDoLLC/scrumdo-mcp.git
cd scrumdo-mcp
pip install -e ".[dev]"
pytest tests/ -v

2 — Get your token

3 — Add to ~/.cursor/mcp.json

{
  "mcpServers": {
    "scrumdo": {
      "command": "scrumdo-mcp",
      "env": {
        "SCRUMDO_TOKEN": "your-token-here",
        "SCRUMDO_ORG": "your-org-slug",
        "SCRUMDO_PROJECT": "your-default-project-slug"
      }
    }
  }
}

Your org and project slugs are the short names in your board URL: app.scrumdo.com/my-company/engineering

4 — Restart Cursor

Done. In any Cursor chat you can now ask:

"What cards are in the current sprint?"
"Move ENG-42 to In Review"
"Add a comment to ENG-42: PR is up for review"
"List all cards assigned to me"

Setup

Step 1 — Get your token

Copy the token — it is only shown once. This is your personal key; keep it private.

Step 2 — Configure your AI tool

Find your tool's MCP config file and add the scrumdo server entry:

Tool	Config file
Claude Code	`~/.claude/claude.json`
Cursor	`~/.cursor/mcp.json`
Windsurf	`~/.codeium/windsurf/mcp_config.json`

{
  "mcpServers": {
    "scrumdo": {
      "command": "scrumdo-mcp",
      "env": {
        "SCRUMDO_TOKEN": "your-token-here",
        "SCRUMDO_ORG": "your-org-slug",
        "SCRUMDO_PROJECT": "your-default-project-slug"
      }
    }
  }
}

Your org slug and project slug are the short names in your board URL: app.scrumdo.com/my-company/engineering

Step 3 — Restart your AI tool

Done. Your AI assistant now has direct access to your board.

What you can do

Once connected, just talk to your AI tool naturally:

"What's the status of ENG-42?"
"Move ENG-42 to In Review and add a comment saying the PR is up"
"List all cards assigned to me in the current sprint"
"Create a sub-task on ENG-42: write release notes"
"Search for cards about the login bug"
"What did the team work on this week?"
"Block ENG-42 — waiting on design approval"
"Move ENG-42 to the Sprint 14 iteration"
"Set the due date on ENG-42 to 2026-04-30"
"Assign ENG-42 to Sarah"

Available tools (132 total)

Group	Tools
Boards	`list_boards`, `get_board`, `get_board_cells`, `list_iterations`, `list_milestones`, `list_labels`, `list_epics`
Cards	`list_cards`, `get_card`, `card_schema`, `find_card`, `create_card`, `update_card`, `move_card`, `move_card_to_iteration`, `set_card_field`, `set_card_fields`, `archive_card`, `assign_card`, `add_card_label`, `remove_card_label`
Blockers	`list_blockers`, `block_card`, `unblock_card`
Tasks	`list_tasks`, `create_task`, `complete_task`, `reopen_task`, `update_task`, `delete_task`
Comments	`list_comments`, `add_comment`, `delete_comment`
Attachments	`add_attachment`
Fields	`list_custom_fields`, `get_card_field`, `get_all_card_fields`
Members	`list_members`, `find_member`
Search	`search_cards`, `search_by_field_value`
Activity	`log_activity`, `get_activity_log`, `get_workspace_activity`
Webhooks	`list_webhooks`, `create_webhook`, `delete_webhook`
Time	`list_time_entries`, `log_time`
Spec	`get_card_spec`, `set_card_spec`, `patch_card_spec`, `get_spec_history`, `list_card_spec_documents`, `set_card_spec_document`, `restore_spec_version` — get/set/patch operate on the primary (`requirements`) doc; the multi-doc trio lists per-`doc_type` documents, writes a specific one (human-only), and restores an accepted version forward
Spec proposals	`generate_spec_proposal`, `list_spec_proposals`, `accept_spec_proposal`, `reject_spec_proposal`, `request_spec_proposal_changes`, `revise_spec_proposal`, `get_decision_inbox`, `read_spec_proposal`, `preview_spec_decision`, `attest_spec_understood` — all human-only; deciding from an MCP session is gated: `get_decision_inbox` → `read_spec_proposal` (delivers the content; the delivery IS the evidence, so it records the evidence-open + viewed the gate requires) → `attest_spec_understood` → `preview_spec_decision` mints a `confirm_token` (bound to the proposal's current version, 10-min TTL) → pass it to `accept`/`reject`/`request_changes`. When the org has `governed_review_sessions_enabled`, the read + attestation are required — accept fails with `needs_understood` without them
GitHub	`get_github_repos`, `list_card_github_links`, `link_github_pr`, `link_github_commit`, `link_github_issue`
Cockpit commands	`cockpit_help` (network-free list of every governed command + the MCP tool that runs it — ask "what can I do?"), `invoke_cockpit_command` (governed dispatcher for ANY catalog command by id — executes `loop.status/pause/resume` + `skill.*`, governance-validates the rest), `research_card`, `run_card_tests`, `tasks_from_spec`, `get_card_memory`, `clear_card_memory` — every command in `get_effective_governance`'s catalog is reachable
Cockpit	`get_card_cockpit_context`, `get_effective_governance`, `get_mcp_capabilities`, `send_cockpit_chat`, `draft_spec_from_card` — the Card AI Cockpit bridge: one-call card context, the governed command policy for a card, this bridge's own tool/connection surface, plus human-only cockpit writes (chat a board agent, draft a spec doc). MCP writes are attributed to the cockpit timeline via `X-Spryng-Source: mcp`
Agents	`get_agent_identity`, `list_agent_accounts`
Agent runs	`start_agent_run`, `get_agent_run`, `list_agent_runs`, `approve_agent_plan`, `accept_proof`, `request_agent_replan`, `execute_task`, `report_agent_progress`, `cancel_agent_run` — `approve_agent_plan` / `accept_proof` / `request_agent_replan` / `execute_task` are human-only (run as a human principal); `execute_task` runs a spec-derived task with an agent (Todo→Doing→Reviewing)
Loops & verification	`start_loop`, `start_verification_loop`, `get_loop_status`, `list_active_loops`, `pause_loop`, `resume_loop`, `cancel_loop`, `get_loop_state`, `update_loop_state`, `get_verification_status`, `run_verifier`, `verify_card`, `log_loop_step`, `attach_evidence`, `route_to_agent`, `list_skills`, `load_skill`
Intelligence	`get_velocity_forecast`, `get_spec_complexity`, `check_spec_drift`, `verify_behavior_contract`
Shared memory	`get_handoff_brief` (call FIRST when picking a card up — "since you last touched this"), `read_blackboard`, `post_blackboard_note`, `drop_blackboard_note` (the card's live working notes; agents may post), `promote_blackboard_note`, `add_card_memory` (promote a note / add durable saved context — human-only), `get_room_context`, `add_room_context`, `curate_room_context`, `run_distiller` (the room/board standing-rules library; add/curate/distill are room-manager only), `list_memory_disputes`, `resolve_memory_dispute` (contradicting saved entries; resolving is human-only)
Notifications	`list_notifications`, `notification_counts`, `mark_notification` (`read` / `acknowledge`), `mark_all_notifications_read`, `wait_for_notifications` (long-poll push channel — blocks until something arrives or `timeout_s` elapses, so agents can wait on a decision instead of spinning)

For a governed verification loop, an orchestrator agent calls start_verification_loop (by VerificationProfile slug or inline proof_requirements/verifier_agent), the Maker (Grok/Codex) implements and calls run_verifier against the accepted spec (never self-verifies), and log_loop_step / attach_evidence write the audit trail to the card. When an agent runs inside a loop (SPRYNG_LOOP_ID set), the loop-scoped tools default to that loop, so loop_id is optional.

Environment variables

Variable	Default	Description
`SCRUMDO_TOKEN`	—	Required. API token from Settings → API Tokens (or an agent's token, for AI Agent runs)
`SCRUMDO_BASE_URL`	`https://app.spryng.io`	API base URL
`SCRUMDO_ORG`	—	Your organization slug
`SCRUMDO_PROJECT`	—	Default project slug
`SCRUMDO_AGENT_RUN_ID`	—	Optional. AI Agent run id this MCP is driving. When set, every write sends the `X-Spryng-Agent-Run` header so the run's audit trail attributes the write (`change_source='agent_run'`). Requires `SCRUMDO_TOKEN` to be that agent's own token, and the run to belong to it.
`SCRUMDO_CLIENT_NAME`	`mcp`	Optional. The host tool driving this bridge (`codex` / `claude-code` / `cursor`). Sent as `X-Spryng-Client` alongside `X-Spryng-Source: mcp` so the Card AI Cockpit timeline can show "via MCP ()". (`SPRYNG_CLIENT_NAME` accepted as an alias; `SCRUMDO_CLIENT_VERSION` optionally adds a version.)
`SPRYNG_LOOP_ID`	—	Optional. The governed loop this MCP is running inside. When set, writes carry the `X-Spryng-Loop` header (attributed to the loop's timeline) and the loop-scoped tools (`log_loop_step`, `attach_evidence`, `get_verification_status`) default their `loop_id` to it — so in-loop agents call them without an id. (`SCRUMDO_LOOP_ID` is accepted as an alias.)

Token scope

Your API token is restricted to your organization's board data only — cards, tasks, comments, members, iterations. It cannot access billing, account settings, or any other organization's data. Revoke it at any time from Settings → API Tokens.

What is MCP?

Model Context Protocol is an open standard for connecting AI tools to external services. Claude Code, Cursor, Windsurf, and other AI editors support it natively. Install the server once; any MCP-compatible tool can use it.

License

MIT

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

2wRelease cycle

9Releases (12mo)

Commit activity

Resources

GitHub Repository

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