Connect Claude, Cursor, ChatGPT, Gemini, and Copilot to Onplana. 27 tools, OAuth + PAT auth.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Onplana.
list_projectsprojects in the org, filterable by status.
get_projectone project in full, with dates, owner and progress.
list_taskstasks for a project, or across projects.
get_taskone task with description, assignee, dates and recent comments.
list_my_taskstasks assigned to the calling user.
list_overduetasks past their due date.
Open-source TypeScript Model Context Protocol building blocks, extracted from Onplana's production MCP deployment. Two packages:
onplana-mcp-server: server
template. Streamable HTTP transport, Bearer auth, prompt-injection
containment, pluggable dispatcher.onplana-mcp-client: typed TypeScript
client SDK for calling the public Onplana MCP endpoint at
https://api.onplana.com/api/mcp/v1.The transport layer of an MCP server (Streamable HTTP wiring, stateless mode, scoped Bearer auth, prompt-injection containment) done well, separated from the platform-specific tool registry. Use the server template to build your own MCP server with security best practices baked in. Use the client SDK to drive Onplana's hosted MCP from your own code.
The patterns are extracted from Onplana's production deployment (public docs at onplana.com/mcp), the same layer that handles real Claude Desktop, Cursor, ChatGPT custom connector, and in-house agent traffic against the Onplana platform.
The MCP transport is the same for everyone. Most early MCP servers get the security primitives wrong:
"ignore previous instructions" in their own data and the
next agent that reads it follows along.Onplana solved these in production over six months of MCP-server work. Publishing the patterns is high-leverage:
The dispatcher implementation, tool catalog, plan-gate logic, audit infrastructure, and the rest of Onplana's ~600 LOC closed-source dispatcher stay in the closed monorepo because they encode platform business logic. If you build your own MCP server using this template, you write your own dispatcher. That's the work that matters and the work that's specific to your platform.
Install:
Wire an Express app:
Full quickstart in packages/server-template/README.md;
runnable demo in examples/in-memory/.
Install:
Use:
Full client docs in packages/client/README.md.
The hosted server at https://mcp.onplana.com/mcp exposes 285 tools,
spanning projects, tasks, sprints, milestones, earned value, risks,
issues, governance, change control, timesheets, wikis, whiteboards,
workflows and the Microsoft Graph integrations. The exact number a given
client sees is smaller, because tools are filtered by the caller's role
and the organization's plan before the catalog is served.
The 33 below are the ones worth knowing first, not the whole catalog.
Reads are annotated readOnlyHint; writes carry destructiveHint so a
client can gate them. Every call runs under the calling identity, is
checked against that user's permissions and the org's plan, and lands in
the audit trail.
Read (readOnlyHint: true)
list_projects: projects in the org, filterable by status.get_project: one project in full, with dates, owner and progress.list_tasks: tasks for a project, or across projects.get_task: one task with description, assignee, dates and recent comments.list_my_tasks: tasks assigned to the calling user.list_overdue: tasks past their due date.list_team_members: members of a project.list_org_members: members of the organization.list_risks: risks logged against a project.find_similar_projects: past projects resembling a description, for estimating.search_org_knowledge: hybrid BM25 and vector search over tasks, projects, wiki pages and comments.summarize_project: AI summary synthesized from the live plan.analyze_project_risks: AI risk detection across schedule, budget, scope and resources.generate_status_report: AI status report from the current schedule and activity.search: App Directory adapter, returns {id, title, snippet?, url?}.fetch: App Directory adapter, returns {id, title, content, url?, metadata?}.Write, additive (destructiveHint: false)
create_project: create a project.create_task: create a task, optionally under a parent.create_milestone: add a milestone to a project.create_comment: comment on a task, issue or project.create_sprint_with_tasks: create a sprint and pull tasks into it.submit_timesheet: log hours against a task.add_project_member: add an existing org member to a project.link_dependency: link two tasks, idempotent via a unique constraint.Write, mutating (destructiveHint: true)
update_project: change project fields such as status, dates or budget.update_task: change task fields such as status, progress or dates.bulk_update_tasks: apply one change across many tasks.assign_task: set a task's assignee.move_task_to_sprint: move a task into or out of a sprint.Leases (for agents that share a backlog)
next_task: pick the next available task and claim it in one call.
Listing and then claiming leaves a gap two agents can both land in.claim_task: take an exclusive lease on a specific task.renew_task_lease: extend a lease while the work is still running.release_task: hand the lease back; completing or blocking a task
releases it too, and ending a session releases everything that run holds.A lease is keyed to the RUN, not to the user. Two sessions of one client authenticate as the same agent persona, so a user-keyed lock would let one session release the other's work. Leases expire on their own, so a crashed agent frees its task instead of holding it.
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