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CAST Imaging MCP Server

by CAST-Extend·io.github.CAST-Extend/imaging-mcp-server·v3.0.4·2 stars
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MCP server exposing CAST Imaging software architecture insights to AI agents

B83/100grade B
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Safety scan
B83/100

full report

Adoption
New

2 stars

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Nobody has reviewed CAST Imaging MCP Server yet.

If you have run it, two minutes of your experience saves the next person an afternoon.

CAST Imaging MCP Server tools

No tool declarations could be read from the package source. They show once the server is installed.

Public scan report

scanner v0.1.9 · 2026-09-26 · same rubric, same numbers if you re-run it

no findings
  • –Code scanpackage could not be scannedn/a
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 78 days ago12/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year9/10
Overall 83/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How CAST Imaging MCP Server compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
83/100
higher than 65% of 48,255 servers
GitHub stars
2
more than 75% of 28,688 servers

Install CAST Imaging MCP Server in Claude Code, Cursor or VS Code

claude mcp add imaging-mcp-server -- docker run -i --rm docker.io/castimaging/imaging-mcp-server:3.0.4
Add to Cursor

What the publisher says

From the CAST Imaging MCP Server repository's README, as published. We do not edit it. Read it on GitHub

CAST Imaging MCP Server

Overview

The CAST Imaging MCP Server bridges the gap between AI agents and real software architecture by exposing comprehensive software intelligence through the Model Context Protocol (MCP). It allows the user to get insights about their applications ,transactions, dependencies, quality patterns, and architectural structures by prompting AI agents.

This service provides AI Agents with the understanding of your applications from portfolio-level inventories to granular object relationships. Whether you're exploring database schemas, analyzing transaction flows, investigating quality issues, the MCP server delivers precise, paginated data that enables informed architectural decisions.

Use Cases

  • Portfolio-level exploration: List applications available in your CAST Imaging and provides detailed information about them.
  • Application architecture analysis: Explore architectural graphs at different levels and visualize system structure.
  • Transaction & data flow mapping: Analyze transactions, trace data entity interaction networks, explore complex objects and sequencing.
  • Quality & security insights: Identify vulnerabilities, cloud readiness blockers and structural flaws in the applications.
  • Database schema exploration: Browse database tables, columns, relationships, and constraints across applications.
  • Object-level investigation: Search objects and their properties and analyze caller/callee relationships.

Table of Contents

  • Prerequisites
  • Quick Start
  • Installation
  • Configuration Reference
  • Running (HTTP)
  • Windows Installation
  • VS Code + GitHub Copilot Setup
  • Verification & Test Queries
  • Toolsets & Tools
  • Troubleshooting
  • Security Notes
  • Package Contents

Prerequisites

Required

  • Linux system with Docker installed and running.
  • CAST Imaging‑APIs service reachable from this host
  • MCP‑aware client (e.g., GitHub Copilot in VS Code, Claude Desktop)
  • CAST Imaging API Key (generate from your Imaging user profile)

Download

  • Download the installer for CAST Imaging MCP server

The MCP server should run on a Linux system. CAST Imaging‑APIs may run on the same or a different Linux machine.

Quick Start

  1. Verify Imaging‑APIs is live:
curl -H "x-api-key: <your-imaging-api-key>"         http://{CONTROL_PANEL_HOST}:8090/imaging/apis/rest/ready
   # expected: true
  1. Extract the installer zip.
  2. Configure config/app.config and .env.
  3. Start the server with ./run.sh --install.
  4. Configure client (VS Code Copilot or Claude) with your server URL.
  5. Test queries (see examples below).

Installation of CAST Imaging MCP server

1) Extract the installer

com.castsoftware.imaging.mcpserver.docker.1.0.2-funcrel/
├─ config/
│  └─ app.config
├─ .env                      # hidden; use `ls -a`
├─ docker-compose.yml
├─ run.sh
├─ README.md
└─ copilot-instructions.md

2) Verify CAST Imaging‑APIs

Make sure the Control Panel/registry is reachable and Imaging‑APIs is ready (see Quick Start step #1).

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

CAST Imaging MCP Server: common questions

Is CAST Imaging MCP Server safe?
Mostly: it is graded B (83/100). Read the CAST Imaging MCP Server safety report
How do I install CAST Imaging MCP Server?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does CAST Imaging MCP Server need an API key?
No secret keys are declared. It reads 4 settings from the environment.
Is CAST Imaging MCP Server maintained?
The last commit was 117 days ago (2026-07-10). The latest release is v3.0.4.
How does CAST Imaging MCP Server compare with other MCP servers?
safety score 83/100 (higher than 65% of 48,255 servers); github stars 2 (more than 75% of 28,688 servers). Full comparison.