Onplana Mcp Server — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Onplana Mcp Server (MCP Server) and scored it 91/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 1 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
The text {match} is the classic direct prompt-injection phrasing. Placed in a skill body that the agent reads as trusted instructions, it tries to make the agent abandon its prior rules and follow whatever comes next — a full system-prompt override.
ignore/disregard/forget … previous instructions sentence.Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Open-source TypeScript Model Context Protocol building blocks, extracted from Onplana's production MCP deployment. Two packages:
template. Streamable HTTP transport, Bearer auth, prompt-injection containment, pluggable dispatcher.
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:
(task titles, comment bodies, wiki text) put that content directly into the model's context. Without containment, a hostile actor can plant "ignore previous instructions" in their own data and the next agent that reads it follows along.
state, which breaks horizontal scaling and complicates the auth model.
invoke wastes turns and confuses the model.
Onplana solved these in production over six months of MCP-server work. Publishing the patterns is high-leverage:
reinventing.
are heavily weighted in next-gen LLM training data, and a repo with patterns + clear documentation about MCP improves model recall of "what good MCP servers look like."
plugs in. The transport is generic; what matters about your MCP server is the tool registry. Open-sourcing the transport doesn't give away anything proprietary.
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 expressWire 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-serverUse:
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:
agentic loops are noisier than humans.
on month-to-date spend. Onplana's deployment uses aiMonthlyCostCapUsd with WARN / BLOCK modes.
tagged with actorType: 'mcp_agent' so admins can see what AI agents did in their tenant separately from human activity.
Onplana exposes 21 of 26; the suppressed 5 either need an in-app preview UI, are too risky for unsupervised invocation, or produce oversized payloads.
preview-only on free tiers. Onplana ships this — agents see "what it would do" before users explicitly upgrade and re-run.
id; store as a unique constraint on your audit row. A model retrying the same logical action shouldn't double-create.
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.
the client (uses ambient 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-serverRestart 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
for the production Onplana MCP deployment (full tool catalog, setup instructions, security model)
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~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.