MCP Integration

What is MCP?

The Model Context Protocol (MCP) provides a standardized way for LLMs to interact with external systems. Agent Sentinel’s MCP integration gives LLMs direct, structured access to:

Platform data (runs, approvals, stats, policies)
Tool execution (create policies, approve actions, get metrics)
Prompt templates (common workflows pre-configured)

This enables LLMs to become autonomous operators of the Agent Sentinel platform.

MCPClient is fully async. Every method below is a coroutine and must be awaited inside an async function or via asyncio.run(...). The examples on this page wrap calls in an async def main() block accordingly.

Quick start

import asyncio
from agent_sentinel import MCPClient

async def main():
    client = MCPClient(
        platform_url="https://platform.agentsentinel.dev",
        api_token="as_your_api_key_here",
    )

    # Discover available tools
    tools = await client.list_tools()
    print(f"Available tools: {[t.name for t in tools]}")

    # Execute a tool
    result = await client.call_tool(
        tool_name="create_policy",
        arguments={
            "name": "Budget Control",
            "session_budget": 10.0,
            "run_budget": 1.0,
        },
    )
    print(result)

asyncio.run(main())

MCPClient also supports the async context-manager protocol so the underlying httpx.AsyncClient is closed cleanly:

async with MCPClient(platform_url=..., api_token=...) as client:
    tools = await client.list_tools()

MCP tools

Tools allow LLMs to perform actions on the platform:

Available tools

Tool	Description
`create_policy`	Create a new policy with budgets and rules
`list_runs`	Get list of agent runs with filters
`get_pending_approvals`	Fetch pending approval requests
`approve_action`	Approve a pending action
`reject_action`	Reject a pending action
`get_agent_stats`	Get statistics for a specific agent
`export_ledger`	Export activity ledger in various formats

Example: Create a policy

async def create():
    result = await client.call_tool(
        tool_name="create_policy",
        arguments={
            "name": "Production Safety",
            "description": "Strict limits for production agents",
            "enabled": True,
            "session_budget": 50.0,
            "run_budget": 5.0,
            "denied_actions": ["delete_database", "drop_table"],
            "rate_limits": {
                "api_call": {"max_count": 100, "window_seconds": 60},
            },
        },
    )

Example: Approve an action

async def approve_first_pending():
    approvals = await client.call_tool("get_pending_approvals", {})
    if approvals.get("data"):
        approval_id = approvals["data"][0]["id"]
        await client.call_tool(
            tool_name="approve_action",
            arguments={
                "approval_id": approval_id,
                "approver_email": "manager@company.com",
                "notes": "Approved - verified with customer",
            },
        )

MCP resources

Resources provide read-only access to platform data:

Available resources

Resource URI	Description
`agentsentinel://runs/latest`	Get the most recent run
`agentsentinel://approvals/pending`	List all pending approvals
`agentsentinel://stats/dashboard`	Get dashboard statistics
`agentsentinel://policies/active`	List all active policies
`agentsentinel://compliance/summary`	Get compliance summary

Example: Access resources

async def fetch():
    latest_run = await client.get_resource("agentsentinel://runs/latest")
    print(f"Latest run: {latest_run['data']['run_id']}")

    stats = await client.get_resource("agentsentinel://stats/dashboard")
    print(f"Total cost: ${stats['data']['total_cost']}")

    pending = await client.get_resource("agentsentinel://approvals/pending")
    print(f"Pending approvals: {len(pending['data'])}")

MCP prompts

Prompts are pre-configured workflows that LLMs can execute:

Available prompts

Prompt	Description
`create_budget_policy`	Guided workflow to create a budget policy
`analyze_agent_costs`	Analyze cost patterns for an agent
`review_pending_approvals`	Review and triage pending approvals
`compliance_audit_report`	Generate a compliance audit report

Example: Execute a prompt

async def analyse():
    result = await client.execute_prompt(
        prompt_name="analyze_agent_costs",
        arguments={"agent_id": "trading-bot", "days": 7},
    )
    print(result["data"]["analysis"])

Convenience methods

The MCP client provides convenience wrappers for common operations (also async):

async def manage():
    # Create a policy
    policy = await client.create_policy(
        name="Dev Environment",
        session_budget=1.0,
        enabled=True,
    )

    # List runs with filters
    runs = await client.list_runs(status="failed", min_cost=0.50, limit=10)

    # Get pending approvals
    approvals = await client.get_pending_approvals()

    # Approve / reject
    await client.approve_action(
        approval_id="approval-123",
        approver_email="you@company.com",
        notes="LGTM",
    )
    await client.reject_action(
        approval_id="approval-456",
        approver_email="you@company.com",
        notes="Too risky",
    )

    # Get agent statistics
    stats = await client.get_agent_stats(agent_id="my-agent")
    print(f"Total runs: {stats['total_runs']}")

Using MCP with LLMs

The primary use case is giving LLMs tool-calling access to the platform:

import asyncio
from anthropic import Anthropic
from agent_sentinel import MCPClient

async def main():
    mcp = MCPClient(
        platform_url="https://platform.agentsentinel.dev",
        api_token="as_your_api_key_here",
    )
    anthropic = Anthropic()

    tools = await mcp.list_tools()
    anthropic_tools = [
        {
            "name": tool.name,
            "description": tool.description,
            "input_schema": tool.input_schema,
        }
        for tool in tools
    ]

    response = anthropic.messages.create(
        model="claude-opus-4-7",
        max_tokens=4096,
        tools=anthropic_tools,
        messages=[{
            "role": "user",
            "content": "List any pending approvals waiting more than 1 hour at critical priority and summarise them.",
        }],
    )

    if response.stop_reason == "tool_use":
        for block in response.content:
            if block.type == "tool_use":
                result = await mcp.call_tool(
                    tool_name=block.name,
                    arguments=block.input,
                )
                print(result)

asyncio.run(main())

Caching

Tool / resource / prompt lists are cached in-process for performance. Pass use_cache=False to force a refresh on a single call:

tools = await client.list_tools(use_cache=False)

Global client

Set a default MCP client for your application:

from agent_sentinel.mcp import set_default_client, get_default_client

set_default_client(MCPClient(
    platform_url="https://platform.agentsentinel.dev",
    api_token="as_your_api_key_here",
))

# Use anywhere in your app
client = get_default_client()
tools = await client.list_tools()

set_default_client and get_default_client are sync helpers — only the network methods on the client itself are async.

The MCP module requires httpx (installed automatically with pip install agentsentinel-sdk). If httpx is unavailable, set_default_client/get_default_client are not exported from agent_sentinel at the top level — import them directly from agent_sentinel.mcp as shown above.

Best practices

Use MCP for autonomous operations: Let LLMs manage policies, approve actions, and analyze costs without manual intervention.

Combine with function calling: Use Claude 4.x or GPT-4 with function calling to enable fully autonomous platform management.

Secure your API tokens: MCP gives LLMs full access to your platform. Use read-only tokens for analysis tasks, and carefully control write access.

Getting started

Concepts

Project direction

SDK (Python)

Platform API

Web Console

What is MCP?

Quick start

MCP tools

Available tools

Example: Create a policy

Example: Approve an action

MCP resources

Available resources

Example: Access resources

MCP prompts

Available prompts

Example: Execute a prompt

Convenience methods

Using MCP with LLMs

Caching

Global client

Best practices

See also

​What is MCP?

​Quick start

​MCP tools

​Available tools

​Example: Create a policy

​Example: Approve an action

​MCP resources

​Available resources

​Example: Access resources

​MCP prompts

​Available prompts

​Example: Execute a prompt

​Convenience methods

​Using MCP with LLMs

​Caching

​Global client

​Best practices

​See also

What is MCP?

Quick start

MCP tools

Available tools

Example: Create a policy

Example: Approve an action

MCP resources

Available resources

Example: Access resources

MCP prompts

Available prompts

Example: Execute a prompt

Convenience methods

Using MCP with LLMs

Caching

Global client

Best practices

See also