Server data from the Official MCP Registry
Connect Claude, Cursor, ChatGPT, Gemini, and Copilot to Onplana. 27 tools, OAuth + PAT auth.
Connect Claude, Cursor, ChatGPT, Gemini, and Copilot to Onplana. 27 tools, OAuth + PAT auth.
Remote endpoints: streamable-http: https://mcp.onplana.com/mcp
Valid MCP server (2 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
Endpoint verified · Requires authentication · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
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Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-onplana-mcp-server": {
"url": "https://mcp.onplana.com/mcp"
}
}
}From the project's GitHub README.
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.
onplana-mcp-server/
├── packages/
│ ├── server-template/ # onplana-mcp-server (npm)
│ │ ├── src/
│ │ │ ├── transport.ts # Streamable HTTP wiring
│ │ │ ├── auth.ts # Bearer auth pattern
│ │ │ ├── promptInjection.ts # wrapUserContent + escape
│ │ │ ├── dispatcher.ts # Pluggable Dispatcher interface
│ │ │ └── index.ts
│ │ ├── tests/ # promptInjection + auth + transport
│ │ └── README.md
│ └── client/ # onplana-mcp-client (npm)
│ ├── src/
│ │ ├── client.ts # OnplanaMcpClient class
│ │ ├── types.ts # Public type surface
│ │ └── index.ts
│ ├── tests/ # client.test.ts (stub fetch)
│ └── README.md
├── examples/
│ └── in-memory/ # Runnable demo with 3 toy tools
└── .github/workflows/
├── ci.yml # tsc + vitest on PR
└── publish.yml # npm publish on tag v*
Install:
npm install github:Onplana/onplana-mcp-server @modelcontextprotocol/sdk express
Wire an Express app:
import express from 'express'
import {
createMcpPostHandler,
createMcpMethodNotAllowedHandler,
requireBearerAuth,
type Dispatcher,
} from 'onplana-mcp-server'
const dispatcher: Dispatcher = {
async listTools(ctx) { /* return your tool descriptors */ return [] },
async callTool(name, input, ctx) { /* dispatch to your tools */ return { output: {} } },
}
const auth = async (token: string) => {
// Validate against your token store. Return AuthContext or null.
return { userId: 'u', scopes: ['MCP_AGENT'] }
}
const app = express()
app.use(express.json())
app.use('/api/mcp/v1',
requireBearerAuth({ auth, requiredScope: 'MCP_AGENT' }),
)
app.post('/api/mcp/v1', createMcpPostHandler({ dispatcher }))
app.get('/api/mcp/v1', createMcpMethodNotAllowedHandler())
app.delete('/api/mcp/v1', createMcpMethodNotAllowedHandler())
app.listen(3000)
Full quickstart in packages/server-template/README.md;
runnable demo in examples/in-memory/.
Install:
npm install github:Onplana/onplana-mcp-server
Use:
import { OnplanaMcpClient } from 'onplana-mcp-client'
const client = new OnplanaMcpClient({
url: 'https://api.onplana.com/api/mcp/v1',
token: process.env.ONPLANA_PAT!,
})
const projects = await client.listProjects({ status: 'ACTIVE' })
// The differentiator vs other PM-tool MCPs: hybrid semantic + lexical
// search across your org's indexed content (projects, tasks, risks,
// goals, comments, wiki pages).
const { matches } = await client.searchOrgKnowledge({
query: 'rationale for the 3-week design phase',
scope: 'all',
limit: 5,
})
Full client docs in packages/client/README.md.
The template + SDK get you running. Add these on top:
aiMonthlyCostCapUsd with WARN / BLOCK modes.actorType: 'mcp_agent' so admins can see what AI
agents did in their tenant separately from human activity.Each of those is platform-specific. The template gives you the seam
where they plug in (Dispatcher.callTool); your dispatcher
implements them however your platform encodes those concepts.
fetch).@modelcontextprotocol/sdk@^1.29.0express@^4.18.0 or express@^5.0.0Tested against:
~/.cursor/mcp.json)~/.gemini/settings.json).vscode/mcp.json)The repo ships a gemini-extension.json manifest at the root, so
Gemini CLI installs Onplana with one command:
export ONPLANA_PAT=pat_paste-your-token-here # mint at app.onplana.com/integrations
gemini extensions install https://github.com/Onplana/onplana-mcp-server
Restart the gemini CLI (or reload your VS Code / JetBrains window
if you're using Gemini Code Assist). The Onplana tools appear in
/mcp and your GEMINI.md context picks up the usage hints
shipped in this repo.
Issues + PRs welcome. The repo is small by design — the goal is for
the transport patterns to be obvious, well-tested, and stable.
Major-version bumps are reserved for breaking changes to the
exported Dispatcher / BearerAuth / handler factory shapes.
Patches and minors are for prompt-injection containment refinements,
new helper utilities, additional test coverage.
MIT — © 2026 Onplana
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