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VeriSwarm MCP Server — Trust infrastructure for AI agents via Model Context Protocol
VeriSwarm MCP Server — Trust infrastructure for AI agents via Model Context Protocol
Well-architected SDK with strong security controls for credential handling, path traversal prevention, and redirect attacks. Code demonstrates thoughtful security practices including HTTPS enforcement, auth header stripping on redirects, and proper input validation. Minor code quality issues and broad exception handling do not materially impact security posture. Permissions align with the server's trust infrastructure purpose. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
4 files analyzed · 8 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-veriswarm-veriswarm-mcp": {
"args": [
"veriswarm-mcp"
],
"command": "uvx"
}
}
}From the project's GitHub README.
Official client libraries, MCP server, and plugins for VeriSwarm — trust infrastructure for AI agents.
| Package | Language | Install | Description |
|---|---|---|---|
| Python SDK | Python | pip install veriswarm | REST client with Workflows, credential issuance, scoring profiles, LangChain adapter |
| Node.js SDK | JavaScript | npm install @veriswarm/sdk | ESM client for decisions, events, Workflows, agent management |
| MCP Server | Python | pip install veriswarm-mcp | 89 MCP tools for trust scoring, Guard, Passport, Vault, Workflows, Compliance (OWASP/EU AI Act/NIST/ISO 42001), Cedar policies + ABAC attributes, SRE, context governance, and content provenance (EU AI Act Art. 50). Works with Claude Desktop, Cursor, and any MCP client. (approve_jit_grant and issue_jit_token are deliberately not exposed to the LLM — those actions live behind an authenticated session in the web UI.) |
| OpenClaw Plugin | TypeScript | openclaw plugins install veriswarm | 11 tools + 3 hooks for OpenClaw agents. PII tokenization, policy enforcement, audit. Per-feature enable/disable. |
| GitHub Action | Python | GitHub Marketplace | CI/CD trust gate — check agent trust scores in your pipeline |
| JSON Schemas | JSON | npm install @veriswarm/schemas | Schema definitions for events, profiles, scores, and workflows |
Add to your MCP client config (Claude Desktop, Cursor, etc.):
{
"mcpServers": {
"veriswarm": {
"command": "python3",
"args": ["-m", "veriswarm_mcp"],
"env": {
"VERISWARM_API_URL": "https://api.veriswarm.ai",
"VERISWARM_API_KEY": "YOUR_API_KEY"
}
}
}
}
Or use the setup CLI:
pip install veriswarm-mcp
veriswarm-setup --platform claude --api-key YOUR_API_KEY
This installs the MCP server, Guard hooks (PII protection + activity logging), and Guard Proxy config.
from veriswarm import VeriSwarmClient
client = VeriSwarmClient("https://api.veriswarm.ai", "YOUR_API_KEY")
# Register an agent
agent = client.register_agent({"slug": "my-agent", "display_name": "My Agent"})
# Check if an action should be allowed
result = client.check_decision(agent_id="agt_123", action_type="send_email")
print(result["decision"]) # "allow", "review", or "deny"
# Ingest events
client.ingest_event(
event_id="evt_001", agent_id="agt_123", source_type="platform",
event_type="task.completed", occurred_at="2026-04-01T00:00:00Z",
payload={"task": "onboarding", "success": True}
)
import { VeriSwarmClient } from '@veriswarm/sdk'
const client = new VeriSwarmClient({
baseUrl: 'https://api.veriswarm.ai',
apiKey: 'YOUR_API_KEY'
})
const result = await client.checkDecision({
agentId: 'agt_123',
actionType: 'send_email'
})
{
plugins: {
entries: {
veriswarm: {
enabled: true,
config: {
apiKey: "YOUR_API_KEY",
piiEnabled: true,
policyEnabled: true,
injectionScan: true,
auditEnabled: true
}
}
}
}
}
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MIT
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