MCP Observatory

  ███╗   ███╗ ██████╗██████╗
  ████╗ ████║██╔════╝██╔══██╗
  ██╔████╔██║██║     ██████╔╝
  ██║╚██╔╝██║██║     ██╔═══╝
  ██║ ╚═╝ ██║╚██████╗██║
  ╚═╝     ╚═╝ ╚═════╝╚═╝
     O B S E R V A T O R Y

= 20">

The GitHub-native CI, SARIF, and security gate for MCP servers before agents depend on them.

Agents should not depend on tools nobody tests. MCP Observatory turns a local MCP check into release-gate evidence maintainers already understand: GitHub Actions, security findings, schema drift detection, PR reports, score badges, agent-accessible diagnostics, and GitHub Code Scanning SARIF.

Start with one server:

npx @kryptosai/mcp-observatory test npx -y my-mcp-server

Then convert the passing check into CI with Code Scanning:

npx @kryptosai/mcp-observatory setup-ci --all --command "npx -y my-mcp-server" --sarif

See the launch page, GitHub Code Scanning demo, and GitHub Code Scanning for MCP servers.

Two more fast paths:

Cloned this repo? Start here: CLONED_THIS.md. Want to contribute? Add one server to the MCP Target Registry or use the Agent Task Pack.

Add MCP CI in one command:

npx @kryptosai/mcp-observatory setup-ci --all --command "npx -y my-mcp-server"

Upload normalized MCP findings to GitHub Code Scanning when you want a security-native release gate:

npx @kryptosai/mcp-observatory setup-ci --all --command "npx -y my-mcp-server" --sarif

Add Observatory as an agent-accessible MCP server:

claude mcp add mcp-observatory -- npx -y @kryptosai/mcp-observatory serve

Building an autonomous agent, OpenClaw-style productivity machine, MCP gateway, or bot runtime? Start with the agent runtime quickstart, copy the OpenClaw MCP reliability agent template, or point your agent at llms.txt and AGENTS.md.

Or test a server immediately:

npx @kryptosai/mcp-observatory test npx -y @modelcontextprotocol/server-everything

Use it as a CLI, a GitHub Action, or an MCP server that lets your AI agent scan, test, record, replay, and verify other MCP servers autonomously.

Why MCP Observatory

MCP servers are becoming production dependencies. If agents rely on them, teams need a way to catch broken tools, unsafe schemas, schema drift, slow responses, and security footguns before those failures reach users.

Observatory gives maintainers and teams:

• One-command CI setup with setup-ci --all
• GitHub PR comments for compatibility, drift, and security findings
• GitHub Code Scanning SARIF for normalized MCP findings
• Health score badges for public trust signals
• Record/replay/verify workflows for regression testing
• MCP server mode so agents can inspect other MCP servers directly
• Production support path for hosted history, private repo reporting, certification, support, and fleet visibility

See the launch page, GitHub Code Scanning for MCP servers, Code Scanning demo, target gallery, target registry, target contribution guide, Agent Task Pack, setup-ci --doctor, MCP server security field guide, Safety Methodology, MCP Server Safety Index, June 2026 safety field report, reference evaluations, MCP lock files, public proof, campaign attribution, local metrics dashboard, and commercial support.

For Security And Platform Teams

MCP servers are becoming part of the AI software supply chain. Agents need reliable, testable, auditable tools before those tools become dependencies in mission-critical workflows.

MCP Observatory gives security and platform teams MCP server CI, schema drift detection, security findings, SARIF/HTML/Markdown reports, GitHub Code Scanning upload, and a path toward certification or fleet visibility. Local OSS use stays free; production, private repo, and fleet usage can move through a paid MCP Readiness Review.

Production Support

Local OSS use stays free under MIT. Teams running MCP in production can use the MCP Readiness Review for CI rollout, SARIF/Code Scanning setup, private repo review, recurring security reports, certification review, support, and fleet visibility. The default package starts at $2,500.

Run npx @kryptosai/mcp-observatory cloud, open a pilot request from the issue chooser, or see COMMERCIAL.md. Also see privacy and telemetry, campaign attribution, and terms for production use.

Quick Start

Scan every MCP server in your Claude config:

npx @kryptosai/mcp-observatory

Go deeper — also invoke safe tools to verify they actually run:

npx @kryptosai/mcp-observatory scan deep

Test a specific server:

npx @kryptosai/mcp-observatory test npx -y @modelcontextprotocol/server-everything

Add it to Claude Code as an MCP server:

claude mcp add mcp-observatory -- npx -y @kryptosai/mcp-observatory serve

Or add it manually to your config:

{
  "mcpServers": {
    "mcp-observatory": {
      "command": "npx",
      "args": ["-y", "@kryptosai/mcp-observatory", "serve"]
    }
  }
}

Commands

Run with no arguments for an interactive menu:

What It Does

Check capabilities — connects to a server and verifies tools, prompts, and resources respond correctly.

Invoke tools — goes beyond listing. Actually calls safe tools (no required params / readOnlyHint) and reports which ones work and which ones crash.

npx @kryptosai/mcp-observatory scan deep

Detect schema drift — diffs two runs and surfaces added/removed fields, type changes, and breaking parameter changes.

npx @kryptosai/mcp-observatory diff run-a.json run-b.json

Recommend servers — scans your project for languages, frameworks, databases, and cloud providers, then cross-references the MCP registry to suggest servers you're missing.

npx @kryptosai/mcp-observatory suggest

Or ask your agent "what MCP servers should I add?" when running in MCP server mode.

Security scanning — analyzes tool schemas for dangerous patterns: shell injection surfaces, broad filesystem access, missing auth, and credential leakage in responses.

npx @kryptosai/mcp-observatory test --security npx -y my-mcp-server

Record / replay / verify — capture a live session, re