io.github.imran-siddique/agentos

NPM · AGENTOS-MCP-SERVER · SCANNED AUG 3

Build and manage policy-compliant AI agents with safety enforcement and compliance checking

Available components

+9 this week 60 60 Trust /100

Trust breakdown (6 categories)

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score →

Supply Chain Security70

No malware found by supply-chain analysis.Pass
CVE check failed: a known high-severity CVE affects uuid 9.0.1, a direct dependency. A fixed version is available. View diagnostics → Fail
No install/post-install scripts declared.Pass
Only part of the dependency tree could be resolved (123 of 127), so this covers what we could see, not the whole tree. View diagnostics → Partial

Provenance & Transparency45

Source repository is publicly reachable at the declared URL. View diagnostics → Pass
Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
Clear OSI-approved license (MIT).Pass
Actively maintained (last published 181 days ago).Pass
Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail

Schema Quality & AI Usability76

100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
AI-judged instruction clarity (good).Pass
Context-footprint check failed: tool/resource definitions use about 1591 tokens (~159/item across 10 items; 10 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
Usage-examples check failed: none of the tools include examples. See how to fix → Fail

Stability & Change Management0

Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified

Tool Coverage100

100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
100% of tool parameters carry a description.Pass

Capabilities100

Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass

Unverified: 1 category

A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

Install

Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.

npm · agentos-mcp-server

Claude Codex opencode OpenClaw Hermes Other

# add to Claude Code

claude mcp add imran-siddique-agentos -- npx -y agentos-mcp-server

# add to Codex CLI

codex mcp add imran-siddique-agentos -- npx -y agentos-mcp-server

// opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "imran-siddique-agentos": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "agentos-mcp-server"
      ],
      "enabled": true
    }
  }
}

# add to OpenClaw

openclaw mcp add imran-siddique-agentos --command npx --arg -y --arg agentos-mcp-server

# ~/.hermes/config.yaml

mcp_servers:
  imran-siddique-agentos:
    command: "npx"
    args: ["-y", "agentos-mcp-server"]

// mcp.json

{
  "mcpServers": {
    "imran-siddique-agentos": {
      "command": "npx",
      "args": [
        "-y",
        "agentos-mcp-server"
      ]
    }
  }
}

Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

2 Aug 26 +35
- CVE-2026-41907 affects this package: high ▼ security
- Provenance: unverified → fail ▼ security
- Known CVEs: unverified → fail ▼ security
- Install scripts: unverified → pass ▲ security
- Malware scan: unverified → pass ▲ security
- Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window). security
- Schema quality: 100 → unverified ▼ functional
- Tool coverage: 100 → unverified ▼ functional
- License: unverified → pass ▲ functional
- Maintenance: unverified → pass ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Schema quality: unverified → good ▲ functional
- Dependency health: unverified → partial ▲ functional
- Licence: MIT functional
31 Jul 26 +1
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
30 Jul 26 −27
- Tool coverage: 100 → unverified ▼ functional
- Schema quality: 100 → unverified ▼ functional
27 Jul 26 51

First indexed and scored.

Show 3 earlier entries Hide earlier entries

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 3 Aug 2026 · Analysed npm/[email protected]

Provenance none

Ecosystem: npm · Outcome: none

Vulnerabilities 1 finding

ID	CVE	Severity	Vector	Fix available
GHSA-w5hq-g745-h8pq	CVE-2026-41907	high	CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N	yes

Dependencies 123 packages

123 packages in the resolved dependency tree · 123 deprecated · 42 stale.

The dependency tree was only partially resolved, so these counts may be incomplete.

MCP tools — 10 exposed · ~1,591 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.

Tool Tokens

attach_policy ~210

Attach safety policies to an agent. Policies enforce rules and constraints on agent behavior. Available policy templates: - pii-protection: Protects personally identifiable information (GDPR compliant) - rate-limiting: Prevents resource abuse through rate limits - cost-control: Prevents runaway costs from automated operations - data-deletion: Prevents accidental data loss - secrets-protection: Prevents exposure of secrets and credentials - human-review: Requires human approval for sensitive actions Compliance frameworks: - gdpr-compliance: EU data protection compliance - soc2-security: SOC 2 Type II security controls - hipaa-healthcare: Healthcare data privacy (PHI protection) - pci-dss-payments: Payment card data security

Input Output Examples

Name	Type	Req	Description
agentId	string	yes	Agent ID to attach policy to
customRules	array	—	Additional custom rules to add
policyId	string	yes	Policy template ID (e.g., "pii-protection", "gdpr-compliance")

No output schema declared.

No examples provided.

audit_log ~151

Query the audit trail for an agent. The audit log records: - Every action attempted by the agent - Policy evaluations and decisions - Approval requests and outcomes - Success/failure status - Timestamps and metadata Use for: - Debugging agent behavior - Compliance reporting - Security investigations - Performance analysis

Input Output Examples

Name	Type	Req	Description
actionFilter	string	—	Filter by action type
agentId	string	yes	Agent ID to get audit log for
endTime	string	—	End of time range (ISO 8601)
limit	number	—	Maximum entries to return (default: 100)
startTime	string	—	Start of time range (ISO 8601)

No output schema declared.

No examples provided.

check_compliance ~174

Check an agent's compliance with regulatory frameworks. Supported frameworks: - SOC2: Service Organization Control 2 Type II - GDPR: EU General Data Protection Regulation - HIPAA: Health Insurance Portability and Accountability Act - PCI_DSS: Payment Card Industry Data Security Standard - CCPA: California Consumer Privacy Act - NIST: NIST Cybersecurity Framework - ISO27001: ISO/IEC 27001 Information Security - FEDRAMP: Federal Risk and Authorization Management Program The compliance check evaluates: - Policy coverage for framework requirements - Audit trail completeness - Access control implementation - Data protection measures

Input Output Examples

Name	Type	Req	Description
agentId	string	yes	Agent ID to check
framework	string	yes	Compliance framework to check against
generateReport	boolean	—	Generate detailed compliance report

No output schema declared.

No examples provided.

create_agent ~189

Create a new AI agent from a natural language description. The agent will be configured with appropriate policies and safety guardrails. Example usage: - "Create an agent that processes customer feedback from support emails daily" - "Build a data pipeline that backs up my Documents folder to Google Drive" - "Set up an agent to monitor our API health and alert on issues" Returns the agent specification with recommended safety policies.

Input Output Examples

Name	Type	Req	Description
approvalRequired	boolean	—	Whether human approval is required before execution
description	string	yes	Natural language description of what the agent should do
language	string	—	Programming language for the agent
policies	array	—	Policy templates to apply (e.g., "pii-protection", "rate-limiting")
schedule	string	—	Cron schedule for recurring execution (e.g., "0 9 * * *" for daily at 9 AM)

No output schema declared.

No examples provided.

create_policy ~173

Create a custom policy from a natural language description. Policies define rules that agents must follow. Example policies: - "Block access to customer credit card data" - "Require approval for any external API calls" - "Rate limit database queries to 100 per minute" - "Log all file deletions" Policies can be based on existing templates: - pii-protection, rate-limiting, cost-control - gdpr-compliance, soc2-security, hipaa-healthcare The policy engine will translate your description into enforceable rules.

Input Output Examples

Name	Type	Req	Description
basedOn	string	—	Policy template to extend
category	string	yes	Policy category
description	string	yes	Natural language policy description
framework	string	—	Compliance framework (e.g., "SOC2", "GDPR", "HIPAA")

No output schema declared.

No examples provided.

deploy_agent ~118

Deploy an agent to start execution. Deployment environments: - local: Runs on your machine (default) - cloud: Runs on AgentOS cloud infrastructure (requires API key) Before deployment, the agent must: - Pass all policy checks - Have valid configuration - Have necessary integrations configured For scheduled agents, deployment starts the scheduler. For triggered agents, deployment enables the triggers.

Input Output Examples

Name	Type	Req	Description
agentId	string	yes	Agent ID to deploy
autoStart	boolean	—	Start agent immediately after deployment
environment	string	—	Deployment environment

No output schema declared.

No examples provided.

get_agent_status ~100

Get the current status, metrics, and health of an agent. Returns: - Current status (draft, testing, deployed, paused, stopped, error) - Execution metrics (runs, success rate, errors) - Recent activity - Policy compliance stats - Resource usage

Input Output Examples

Name	Type	Req	Description
agentId	string	yes	Agent ID to get status for
includeLogs	boolean	—	Include recent logs
includeMetrics	boolean	—	Include execution metrics

No output schema declared.

No examples provided.

list_templates ~178

Browse the library of pre-built agent and policy templates. Agent templates include: - Data processors, email assistants, database analysts - File organizers, backup agents, web scrapers - Slack bots, API monitors, report generators Policy templates include: - Security: PII protection, secrets protection, data deletion safety - Compliance: GDPR, SOC 2, HIPAA, PCI DSS - Operational: Rate limiting, cost control, human review Use templates as a starting point for your custom agents.

Input Output Examples

Name	Type	Req	Description
category	string	—	Filter by category (e.g., "data", "security", "compliance")
framework	string	—	Filter by compliance framework (e.g., "GDPR", "SOC2")
search	string	—	Search query to filter templates
type	string	—	Template type to list

No output schema declared.

No examples provided.

request_approval ~165

Request human approval for a sensitive agent action. Use this when: - Policy requires human review before execution - Agent attempts a high-risk action - You want to add an extra safety check Approval requests are sent to designated approvers via: - Email notification - Slack notification (if configured) - Dashboard alert Requests expire after the specified time (default: 24 hours).

Input Output Examples

Name	Type	Req	Description
action	string	yes	Action requiring approval (e.g., "delete_files", "send_email")
agentId	string	yes	Agent ID requesting approval
approvers	array	yes	Email addresses of approvers
description	string	yes	Detailed description of what will happen if approved
expiresInHours	number	—	Hours until approval expires (default: 24)

No output schema declared.

No examples provided.

test_agent ~133

Test an agent with a simulated scenario before deployment. This performs a dry-run that: - Validates the agent configuration - Checks all policies against the test scenario - Simulates workflow execution - Reports any policy violations or issues - Estimates resource usage and costs No actual changes are made during testing.

Input Output Examples

Name	Type	Req	Description
agentId	string	yes	Agent ID to test
dryRun	boolean	—	Run without side effects (default: true)
mockData	object	—	Mock data for testing (optional)
scenario	string	yes	Test scenario description (e.g., "Process 100 customer emails")

No output schema declared.

No examples provided.

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Scoring key

80+ high trust
60–79 medium
below 60 low

How we score

Score history · last 30 days

27 Jul 3 Aug