mcp-observatory

MCP Observatory

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Secure the MCP servers you're building. MCP Observatory is the CI-native security tool for teams shipping custom MCP servers. Test during development, catch schema drift, simulate attacks, and generate compliance evidence — before agents depend on your servers.

Also available in Simplified Chinese.

> Runtime enforcement: Use mcp-seatbelt to block dangerous MCP tool calls at runtime based on observatory scan results.

Get Started

No account. Local scan stays free.

npx -y @kryptosai/mcp-observatory@latest

Grades your configured MCP servers, or a packaged demo if you have none.

Next, sign in with GitHub and upload one hosted snapshot free. The upload completes before any optional upgrade is shown:

npx -y @kryptosai/mcp-observatory@latest cloud upload

Then keep the local check running in CI:

npx -y @kryptosai/mcp-observatory@latest setup-ci --all

MCP Observatory uses jurisdiction-aware product telemetry after showing the collection notice. Review the exact fields with `telemetry preview`, check the current policy with `telemetry status`, or stop collection at any time with `telemetry disable`, `MCP_OBSERVATORY_TELEMETRY=0`, or `DO_NOT_TRACK=1`. See the complete privacy and telemetry notice.

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

- uses: KryptosAI/mcp-observatory/action@v1

If this repo is an MCP package, that tests `npx -y `. Otherwise set `command:`.

Or generate a full workflow: `npx -y @kryptosai/mcp-observatory@latest setup-ci --all --command "npx -y my-mcp-server" --sarif`

Install: `npx -y @kryptosai/mcp-observatory@latest` · `brew tap kryptosai/mcp-observatory https://github.com/KryptosAI/mcp-observatory && brew install mcp-observatory` · `docker pull ghcr.io/kryptosai/mcp-observatory:latest` · `curl -fsSL https://raw.githubusercontent.com/KryptosAI/mcp-observatory/main/scripts/install.sh | sh`

docker run --rm ghcr.io/kryptosai/mcp-observatory:latest demo
docker run --rm ghcr.io/kryptosai/mcp-observatory:latest test npx -y @modelcontextprotocol/server-everything

Scanning a server on the host needs network and, for local files, a volume mount (`-v "$PWD:$PWD" -w "$PWD"`).

Agents: `npx -y @kryptosai/mcp-observatory@latest serve`

Default-deny connect: `npx -y @kryptosai/mcp-observatory@latest protect` rewrites every discovered client config (Cursor, Claude, VS Code, `.mcp.json`, …). Undo: `protect --undo`. HTTP/SSE servers are left alone.

Runtime deny-default: `npx -y @kryptosai/mcp-observatory@latest enforce --start-proxy` writes a Seatbelt policy from the scan and starts the proxy. Local scan stays free.

Proof: Safety Index. Upload one hosted snapshot free with `cloud upload`; Individual Pro is $29/month when you need retained history and hosted CI ingestion.

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`
• Profile-mapped audits with `audit --profile nsa-mcp`
• MCP receipts that package target, evidence, verdict, action, and reproduction commands
• MCP risk graphs that group servers by capability boundary, receipt state, CI posture, and recommended action
• Action receipts that say `allow`, `gate`, `rerun`, `quarantine`, or `escalate`
• 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 one-user hosted history, CI ingestion, and owner-ready Release Gate remediation

See GitHub Code Scanning for MCP servers, MCP Receipts, Troubleshooting, Safety Methodology, MCP Server Safety Index, MCP Observatory Contributors, hosted client contract, repository boundary, open core boundary, and commercial support.

Self-Assessment

We scan ourselves with mcp-observatory on every release. See results →

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.

Whether you're shipping one MCP server or running a fleet, MCP Observatory gives you CI-native security scoring, attack simulation, schema drift detection, SARIF/HTML/Markdown reports, and GitHub Code Scanning — from your first `npx` command to production deployment. Local development stays free; teams with a near-term production approval decision can use the fixed-scope MCP Release Gate Pilot.

Production Support

Local OSS use stays free under MIT. Teams running MCP in production can use the MCP Release Gate Pilot for safe-mode evidence, SARIF/Code Scanning setup, CI rollout, private reporting, and owner-ready remediation notes. The fixed public entry offer is `$15,000` for 1-3 critical MCP servers over ten business days; broader work is scoped after the release decision.

The open source repo is the portable evidence engine. Hosted authentication, retention, organization workflows, fleet coordination, and private intelligence stay outside the OSS package; see the repository boundary.

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

How It Compares

Quick Start

Run a first check. No arguments starts `demo` (your servers, or the packaged local demo):

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 to start `demo`. Use `--menu` for the interactive catalog.

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, replay it offline in CI, and verify nothing changed. Like VCR for MCP.

# Record a session
npx @kryptosai/mcp-observatory record npx -y @modelcontextprotocol/server-everything

# Replay offline (no server needed)
npx @kryptosai/mcp-observatory replay .mcp-observatory/cassettes/latest.cassette.json

# Verify the live server still matches
npx @kryptosai/mcp-observatory verify cassette.json npx -y @modelcontextprotocol/server-everything

Watch for regressions — re-runs checks on an interval and alerts when something changes.

npx @kryptosai/mcp-observatory watch target.json

Scan locations

When you run `scan`, it looks for MCP configs in:

• `~/.claude.json` (Claude Code)
• `~/Library/Application Support/Claude/claude_desktop_config.json` (Claude Desktop, macOS)
• `%APPDATA%/Claude/claude_desktop_config.json` (Claude Desktop, Windows)
• `.claude.json` and `.mcp.json` (current directory)

Architecture

┌─────────────────────────┐
                    │   MCP Observatory CLI    │
                    │  npx @kryptosai/mcp-     │
                    │     observatory scan     │
                    └───────────┬─────────────┘
                                │
                    ┌───────────▼─────────────┐
                    │   Config Discovery       │
                    │  (Claude, Cursor, etc.)  │
                    └───────────┬─────────────┘
                                │
              ┌─────────────────┼─────────────────┐
              ▼                 ▼                  ▼
    ┌─────────────────┐ ┌──────────────┐ ┌──────────────────┐
    │   Security Scan  │ │  Attack Sim  │ │  Schema Drift    │
    │  (shell, creds)  │ │ (tool poison)│ │  (version diff)  │
    └────────┬────────┘ └──────┬───────┘ └────────┬─────────┘
             │                 │                   │
             └─────────────────┼───────────────────┘
                               ▼
                    ┌─────────────────────┐
                    │   Health Score       │
                    │  (0-100 + verdict)   │
                    └──────────┬──────────┘
                               │
              ┌────────────────┼────────────────┐
              ▼                ▼                 ▼
    ┌──────────────┐  ┌──────────────┐  ┌──────────────┐
    │  SARIF       │  │  Markdown    │  │  CI Gateway  │
    │  (Code Scan) │  │  Report      │  │  (setup-ci)  │
    └──────────────┘  └──────────────┘  └──────────────┘

CI / GitHub Action

Add Observatory to your MCP server's CI pipeline:

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

Check the adoption kit:

npx @kryptosai/mcp-observatory setup-ci --doctor

Successful `test`, `run`, and single-target `scan` checks also offer to convert the passing result into a CI adoption kit. That automatic conversion enables SARIF/Code Scanning and weekly scheduled checks by default; pass `--no-ci-sarif` when you only want a conservative workflow without Code Scanning upload.

Or create the workflow manually:

# .github/workflows/observatory.yml
name: MCP Server Check
on: [pull_request]

permissions:
  contents: read

jobs:
  observatory:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: KryptosAI/mcp-observatory/action@v1
        with:
          command: npx -y my-mcp-server
          deep: true
          security: true
          comment-on-pr: false
          set-status: false

GitLab CI

Runs the same `test` scan on merge requests and on `main`, publishing the SARIF as a GitLab SAST report so findings appear in the merge request security widget. This matches what `setup-ci --ci-provider gitlab-ci` generates.

# .gitlab-ci.yml
mcp-observatory:
  image: node:22
  rules:
    - if: $CI_PIPELINE_SOURCE == 'merge_request_event'
    - if: $CI_COMMIT_BRANCH == 'main'
  script:
    - npx @kryptosai/mcp-observatory test npx -y my-mcp-server --deep --security --sarif mcp-observatory.sarif
  artifacts:
    reports:
      sast: mcp-observatory.sarif

Or let the CLI write it for you:

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

Action inputs:

The action can comment on PRs and set commit statuses when the workflow grants write permissions. `setup-ci` generates read-only third-party-friendly workflows by default and lets maintainers opt into comments/statuses later. `init-ci` remains available as a backward-compatible alias. See `action/README.md` for all options.

Production teams with a near-term MCP approval decision can use the fixed-scope MCP Release Gate Pilot: an approve, gate, or defer decision for 1–3 servers in ten business days. See COMMERCIAL.md or request a decision at mcp-observatory.com/release-gate-pilot.

Evidence badges for MCP Observatory

MCP server maintainers can add a public compatibility/security signal to their README:

[![MCP Observatory](https://img.shields.io/badge/MCP%20Observatory-enabled-2563eb)](https://github.com/KryptosAI/mcp-observatory)

Or generate a score badge from a live check:

npx @kryptosai/mcp-observatory badge npx -y my-mcp-server --output docs/mcp-health.svg

See the evidence distribution loop for the GitHub Action template, maintainer PR body, and badge rollout playbook. A badge is a public evidence signal, not a certification or endorsement.

Generate a pilot-ready production/security report from local run artifacts:

npx @kryptosai/mcp-observatory enterprise-report \
  --account "Your Company" \
  --format html \
  --output observatory-enterprise-report.html

For clearer internal account attribution in CI, set:

MCP_OBSERVATORY_ORG=your-company.com

To deliberately associate a contact email and optional contact-channel slug with private telemetry analytics, run `mcp-observatory telemetry identify --email [--channel ]`. Setting an organization or identifying a contact does not authorize outreach.

Testing Feishu/Lark integrations? See the Feishu/Lark MCP guide.

Lock Files

$ npx @kryptosai/mcp-observatory lock              # Snapshot all server schemas
$ npx @kryptosai/mcp-observatory lock verify        # Verify no drift since last lock

Lock files are the package-lock for AI tools: commit the MCP contract, then make every tool, schema, prompt, or resource drift visible in CI. See MCP lock files.

Trend Tracking

$ npx @kryptosai/mcp-observatory history            # Show health trends over time

Nightly Scans

$ npx @kryptosai/mcp-observatory ci-report          # Generate regression report for CI

MCP Server Mode

No other testing tool is itself an MCP server. Add Observatory as a server and your AI agent can autonomously test, diagnose, and monitor your other MCP servers.

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

Your agent gets 10 tools:

An AI tool that checks other AI tools. It is a tool testing tools that serve tools.

Security

The MCP server runs inside AI hosts where an LLM chooses which tools to call. To prevent prompt-injection attacks:

• Command allowlist: Only `npx`, `node`, `python`, `python3`, `uvx`, `docker`, `deno`, `bun` are permitted as base executables. The CLI has no restrictions.
• Path validation: File-reading tools are constrained to the runs/cassettes directories.
• No arbitrary execution: Use the CLI for unrestricted commands.

CLI vs MCP: Intentional Differences

Compatibility

Works with any MCP server that uses standard transports:

Servers needing API keys work via `env` in the target config. Python servers work via `uvx`. See the full compatibility matrix for tested servers and known issues.

Target config files

For more control (env vars, metadata, custom timeout):

{
  "targetId": "filesystem-server",
  "adapter": "local-process",
  "command": "npx",
  "args": ["-y", "@modelcontextprotocol/server-filesystem", "."],
  "timeoutMs": 15000,
  "skipInvoke": false
}

npx @kryptosai/mcp-observatory run --target ./target.json

HTTP / SSE targets

{
  "targetId": "my-remote-server",
  "adapter": "http",
  "url": "https://mcp.example.com/mcp",
  "authToken": "${MCP_SERVER_TOKEN}",
  "headers": {
    "X-Api-Key": "$MCP_SERVER_API_KEY"
  },
  "timeoutMs": 15000
}

Target configs support `${VAR}`, `$VAR`, and `env:VAR` references in `authToken`, `headers`, and local-process `env` values.

How It Compares

Each tool has strengths. Observatory focuses on regression detection and CI-friendly workflows. mcp-recorder is great as a transparent proxy. MCPBench is the go-to for performance benchmarking. mcp-jest is ideal if you're already in a Jest workflow.

Prior Art

The record/replay/verify pattern is inspired by:

• VCR (Ruby) — pioneered cassette-based HTTP record/replay
• Polly.js (Netflix) — HTTP interaction recording for JavaScript
• mcp-recorder — MCP-specific traffic recording proxy
• MCPBench — MCP server benchmarking
• mcp-jest — Jest-style testing for MCP servers

Limitations

• Servers requiring interactive OAuth (e.g., Google Drive) need pre-authentication before Observatory can connect
• Custom WebSocket transports (e.g., BrowserTools MCP) are not supported
• A few servers time out or close before init — see known issues and compatibility

Works with mcp-seatbelt

Scan before you trust. Then run `npx -y @kryptosai/mcp-observatory@latest enforce --start-proxy` — Observatory writes a deny-default mcp-seatbelt policy and starts the proxy. Observatory validates; seatbelt enforces.

Works with agent-obs

Secure your servers with Observatory. Trace your agents with agent-obs — an open-source agent execution tracer that records every tool call, computes A-F session grades, and shows you exactly where your agents spend time, burn tokens, and hit errors. Observatory tells you if a server is safe. agent-obs tells you what your agent did with it. Free, local-first, `npm install -g agent-obs`.

Contributors ✨

Thanks to these amazing people who have contributed:

• leemeo3 — 3 Safety Index targets (Git, Chrome DevTools, Filesystem MCP)
• albatrossflyon-coder — GitHub MCP Safety Index (#201)
• tanishxdev — Legacy CLI deprecation warnings (#187)
• sansynx — CLI format validation (#182)

See all contributors →

Contributing

We welcome contributors! This project follows a Contributor Covenant Code of Conduct. The fastest way to get involved:

git clone https://github.com/KryptosAI/mcp-observatory.git && cd mcp-observatory && npm install && npm test

The most common first contribution is adding an MCP server to the Safety Index (10-15 minutes). See CONTRIBUTING.md for full guidelines, code standards, and the contributor recognition ladder.

If Observatory saved you a broken deploy, consider giving it a star. It helps others find the project.