Skip to main content
Glama
cerase-ai

Cerase Tasks MCP

Official
by cerase-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

cerase-tasks-mcp

An MCP server that gives a Cerase assistant a board of projects and tasks for the work it does. It stores nothing: every tool is one HTTP call to the board endpoints of the Cerase control-plane, which keeps the board in its database and shows it in the console. Outside a Cerase appliance it has nothing to talk to.

Tools

Tool

What it does

Arguments

Returns

create_task

Files a task. It lands in the project "General" unless project names another, and a project named that way is created if it does not exist.

agent_id, title, optional project (name), phase, description

{id, title, status, project, hint}

set_status

Moves a task to backlog, todo, doing, review or done; the board records the elapsed time.

agent_id, task_id, status

{id, status, started_at, completed_at, hint}

list_tasks

Lists the open tasks: id, title, status, phase, project.

agent_id, optional project (name or id)

{tasks}

create_project

Creates a project. A name that already exists, ignoring case and surrounding spaces, returns that project.

agent_id, name

{id, name, created}

list_projects

Lists the projects with their open and total task counts and their workspace folder, if any.

agent_id

{projects}

rename_project

Renames a project. Refused when the new name belongs to another project; "General" keeps its name.

agent_id, project (name or id), name

{id, name}

merge_projects

Moves every task of source, and its workspace folder, into into, then retires source. "General" is emptied, not removed.

agent_id, source, into (names or ids)

{id, name, tasks_moved, source_kept, folder}

delete_project

Deletes a project that holds no tasks. "General" cannot be deleted.

agent_id, project (name or id)

{deleted}

project_folder

Returns the project's folder in the assistant's workspace, creating it on the first call.

agent_id, project (name or id)

{id, name, folder, created}

agent_id must not be empty, and every request carries it, so the control-plane answers with the calling assistant's own board. Inside Cerase the gateway fills it; the model cannot set it. The rules quoted above (name matching, refusals, "General") are enforced by the control-plane, not by this server.

The control-plane refuses a title longer than 300 characters. The tool descriptions tell the assistant to keep the title to a short name and to put what was asked, and what done means, in description. The hint field in the answers to create_task and set_status reminds the assistant to move the task to done before it replies.

Related MCP server: Agent Kanban

Settings

Variable

Default

Purpose

CERASE_CONTROL_PLANE_URL

http://cerase-control-plane:8000

Control-plane base URL; the default is its service name inside a Cerase appliance. Requests go to /api/internal/task-board/….

CERASE_INTERNAL_SECRET

empty

Bearer token for those requests; no Authorization header is sent when it is empty.

Installation

The connector is published in the Cerase Marketplace as studio.guidance/cerase-tasks (marketplace page). Every Cerase appliance installs it at boot, so its assistants have it without an install step. The workplan skill is what tells an assistant when to use the board.

The image ghcr.io/cerase-ai/cerase-tasks-mcp is built and published from the copy of these files kept in the Cerase appliance repository, which is private. This repository carries the same files byte for byte, so a change made only here does not reach the image.

Build and run locally

docker build -t cerase-tasks-mcp .
docker run --rm -p 3000:3000 \
  -e CERASE_CONTROL_PLANE_URL=http://<control-plane>:8000 \
  -e CERASE_INTERNAL_SECRET=<bearer> \
  cerase-tasks-mcp

server.py speaks MCP over stdio; the image runs it behind mcp-proxy, which serves Streamable HTTP at http://localhost:3000/mcp and SSE at http://localhost:3000/sse. The image's HEALTHCHECK runs scripts/healthcheck.py, an MCP client that completes the handshake and lists the tools over /mcp; its CERASE_HEALTHCHECK_* variables exist to point it at a stub in tests.

The tests replace the HTTP transport with a mock, so they need no control-plane:

pip install -r requirements.txt pytest
python -m pytest tests/

License

MIT. See LICENSE.

This server cannot be deployed

Pay for results, not tokens.Fund a bounty. Let AI agents compete and collaborate to solve your problems.agentbounties.appAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • pith

    SiluPanda
    A
    license
    Not graded
    quality
    D
    maintenance
    MCP server for task management that enables AI agents to read, create, update tasks, and track work sessions, allowing agents and humans to collaborate on the same task board.
    5 npm
    9
    MIT

  • Agent Kanban

    graywrk
    A
    license
    Not graded
    quality
    D
    maintenance
    An AI-native kanban board MCP server where agents pull tasks via the Model Context Protocol. It provides tools for task discovery, claiming, progress updates, and review workflows.
    4
    MIT

  • agents-board

    fmilioni
    A
    license
    Not graded
    quality
    B
    maintenance
    MCP server that provides a real-time project management board with cards, sprints, comments, and docs, enabling coding agents to query and edit project state.
    2
    MIT