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Dashboard

CallTelemetry Dashboard OpenAPI-driven MCP server

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About Dashboard

Dashboard is an MCP server in the Analytics category: callTelemetry Dashboard OpenAPI-driven MCP server.

The repository has 4 stars and 0 forks.

Install

Claude Code
claude mcp add dashboard -- npx -y @calltelemetry/dashboard-mcp
npx
npx -y @calltelemetry/dashboard-mcp

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README

mcp-dashboard

🧱 TARGET STACK (same goal)

Web Admin Dashboard (React) ↓ FastAPI (HTTP) ↓ AI (later) ↓ MCP Server (Python)

STEP 1 — Create Conda Environment conda create -n mcp-dashboard python=3.11 -y conda activate mcp-dashboard

Verify:

python --version

STEP 2 — Project Structure mkdir mcp-dashboard cd mcp-dashboard

mkdir backend frontend

STEP 3 — BACKEND (FastAPI) 3.1 Install backend dependencies cd backend pip install fastapi uvicorn

3.2 Create FastAPI app mkdir api cd api

Create main.py:

from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware

app = FastAPI(title="MCP Admin API")

app.add_middleware( CORSMiddleware, allow_origins=[""], allow_methods=[""], allow_headers=["*"], )

@app.get("/analyze") def analyze(barangay: str): # TEMP: later replaced by AI → MCP return { "barangay": barangay, "floodLevel": "High", "evacuationCenters": [ "Barangay Elementary School", "Covered Court" ] }

Run backend:

uvicorn main:app --reload

STEP 4 — FRONTEND (React Dashboard) 4.1 Go to frontend folder

Open a new terminal (keep backend running):

conda activate mcp-dashboard cd mcp-dashboard/frontend

4.2 Create React app (Vite) npm create vite@latest .

Choose:

React

TypeScript

Install deps:

npm install npm install axios

Run dev server:

npm run dev

4.3 Clean App.tsx

Edit src/App.tsx:

import Dashboard from "./Dashboard";

export default function App() { return ; }

4.4 Create Dashboard UI

Create src/Dashboard.tsx:

import { useState } from "react"; import axios from "axios";

export default function Dashboard() { const [barangay, setBarangay] = useState(""); const [result, setResult] = useState(null);

const analyze = async () => { const res = await axios.get("http://127.0.0.1:8000/analyze", { params: { barangay } }); setResult(res.data); };

return ( <div style={{ padding: 24, maxWidth: 600 }}> Evacuation Analysis Dashboard

  <input
    value={barangay}
    onChange={(e) => setBarangay(e.target.value)}
    placeholder="Enter barangay"
    style={{ width: "100%", padding: 8 }}
  />

  <button onClick={analyze} style={{ marginTop: 12 }}>
    Analyze
  </button>

  {result && (
    <div style={{ marginTop: 24 }}>
      <p><b>Barangay:</b> {result.barangay}</p>
      <p><b>Flood Level:</b> {result.floodLevel}</p>
      <ul>
        {result.evacuationCenters.map((c: string, i: number) => (
          <li key={i}>{c}</li>
        ))}
      </ul>
    </div>
  )}
</div>

); }

Open:

http://localhost:5173

🎉 You now have a Web Admin Dashboard built from scratch using Conda

MCP Creation

STEP 1 — Activate Conda Environment conda activate mcp-dashboard

Confirm:

python --version

STEP 2 — Install MCP pip install mcp

✅ This gives us the MCP server tools.

STEP 3 — Create MCP Server Folder cd backend mkdir mcp cd mcp

STEP 4 — Create MCP Server File (server.py)

Here’s an example MCP server with PokéAPI tool and a mock evacuation tool:

from mcp.server import Server from mcp.server.stdio import stdio_server import requests

Initialize MCP server

server = Server("poke-mcp")

Tool 1: Get Pokémon info from PokéAPI

@server.tool() def get_pokemon_info(name: str) -> dict: """ Fetch Pokémon information by name from PokéAPI. """ url = f"https://pokeapi.co/api/v2/pokemon/{name.lower()}" response = requests.get(url)

if response.status_code != 200:
    return {"error": f"Pokemon '{name}' not found"}

data = response.json()
return {
    "name": data["name"],
    "id": data["id"],
    "height": data["height"],
    "weight": data["weight"],
    "types": [t["type"]["name"] for t in data["types"]],
}

Tool 2: Find evacuation centers (mock)

@server.tool() def find_evacuation_centers(barangay: str) -> list: mock_data = { "Manila": ["Manila Elementary School", "Barangay Covered Court"], "Quezon City": ["QC High School", "Barangay Hall QC"] } return mock_data.get(barangay, ["No evacuation centers found"])

Run MCP server

if name == "main": stdio_server(server)

STEP 5 — Run MCP Server

python server.py

MCP server is now live in stdio mode

Exposes two tools:

get_pokemon_info(name)

find_evacuation_centers(barangay)

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Dashboard

How do I install Dashboard?

Run claude mcp add dashboard -- npx -y @calltelemetry/dashboard-mcp, then add the server to your MCP client's configuration.

Is Dashboard safe to use with an AI agent?

Its trust score is 37 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Dashboard still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.