MCP (Model Context Protocol)

Overview

The Model Context Protocol (MCP) provides a standardized interface for LLMs to interact with Agent Sentinel. It exposes platform functionality as:

Tools: Actions LLMs can execute (create policies, approve actions, query data)
Resources: Data LLMs can read (runs, approvals, stats, policies)
Prompts: Pre-configured workflows (analyze costs, review approvals, generate reports)

This enables LLMs to autonomously manage and monitor the Agent Sentinel platform.

Endpoints

List available tools

GET /api/v1/mcp/tools

Returns all available MCP tools with schemas:

{
  "tools": [
    {
      "name": "create_policy",
      "description": "Create a new policy with budgets and rules",
      "input_schema": {
        "type": "object",
        "properties": {
          "name": {"type": "string"},
          "description": {"type": "string"},
          "enabled": {"type": "boolean"},
          "session_budget": {"type": "number"},
          "run_budget": {"type": "number"},
          "denied_actions": {"type": "array", "items": {"type": "string"}},
          "rate_limits": {"type": "object"}
        },
        "required": ["name"]
      }
    },
    {
      "name": "list_runs",
      "description": "Get list of agent runs with optional filters",
      "input_schema": {
        "type": "object",
        "properties": {
          "status": {"type": "string", "enum": ["running", "completed", "failed"]},
          "min_cost": {"type": "number"},
          "agent_id": {"type": "string"},
          "limit": {"type": "integer"}
        }
      }
    },
    {
      "name": "get_pending_approvals",
      "description": "Fetch all pending approval requests",
      "input_schema": {"type": "object"}
    },
    {
      "name": "approve_action",
      "description": "Approve a pending action",
      "input_schema": {
        "type": "object",
        "properties": {
          "approval_id": {"type": "string"},
          "approver_email": {"type": "string"},
          "notes": {"type": "string"}
        },
        "required": ["approval_id", "approver_email"]
      }
    },
    {
      "name": "reject_action",
      "description": "Reject a pending action",
      "input_schema": {
        "type": "object",
        "properties": {
          "approval_id": {"type": "string"},
          "approver_email": {"type": "string"},
          "notes": {"type": "string"}
        },
        "required": ["approval_id", "approver_email"]
      }
    },
    {
      "name": "get_agent_stats",
      "description": "Get comprehensive statistics for an agent",
      "input_schema": {
        "type": "object",
        "properties": {
          "agent_id": {"type": "string"}
        },
        "required": ["agent_id"]
      }
    },
    {
      "name": "export_ledger",
      "description": "Export activity ledger in JSON/JSONL/CSV format",
      "input_schema": {
        "type": "object",
        "properties": {
          "format": {"type": "string", "enum": ["json", "jsonl", "csv"]},
          "start_date": {"type": "string"},
          "end_date": {"type": "string"},
          "agent_id": {"type": "string"}
        },
        "required": ["format"]
      }
    }
  ]
}

Execute MCP tool

POST /api/v1/mcp/call

Request body:

{
  "tool_name": "create_policy",
  "arguments": {
    "name": "Budget Control",
    "description": "Limit costs for dev environment",
    "enabled": true,
    "session_budget": 5.0,
    "run_budget": 1.0,
    "denied_actions": ["delete_production_db"]
  }
}

Response:

{
  "success": true,
  "data": {
    "policy_id": "policy_abc123",
    "name": "Budget Control",
    "enabled": true,
    "created_at": "2024-12-28T14:30:00Z"
  },
  "error": null,
  "metadata": {
    "execution_time_ms": 45
  }
}

List available resources

GET /api/v1/mcp/resources

Returns all available MCP resources:

{
  "resources": [
    {
      "uri": "agentsentinel://runs/latest",
      "name": "Latest Run",
      "description": "Get the most recent run",
      "mime_type": "application/json"
    },
    {
      "uri": "agentsentinel://approvals/pending",
      "name": "Pending Approvals",
      "description": "List all pending approval requests",
      "mime_type": "application/json"
    },
    {
      "uri": "agentsentinel://stats/dashboard",
      "name": "Dashboard Stats",
      "description": "Get aggregate dashboard statistics",
      "mime_type": "application/json"
    },
    {
      "uri": "agentsentinel://policies/active",
      "name": "Active Policies",
      "description": "List all enabled policies",
      "mime_type": "application/json"
    },
    {
      "uri": "agentsentinel://compliance/summary",
      "name": "Compliance Summary",
      "description": "Get compliance overview and status",
      "mime_type": "application/json"
    }
  ]
}

Get resource data

GET /api/v1/mcp/resources/{uri}

Example:

curl -H "Authorization: Bearer $TOKEN" \
  "https://platform.agentsentinel.dev/api/v1/mcp/resources/agentsentinel://stats/dashboard"

Response:

{
  "uri": "agentsentinel://stats/dashboard",
  "mime_type": "application/json",
  "data": {
    "total_runs": 1234,
    "total_actions": 45678,
    "total_cost_usd": 567.89,
    "success_rate": 0.95,
    "pending_approvals": 5,
    "interventions_24h": 23
  }
}

List available prompts

GET /api/v1/mcp/prompts

Returns all available prompt templates:

{
  "prompts": [
    {
      "name": "create_budget_policy",
      "description": "Guided workflow to create a budget policy",
      "arguments": [
        {
          "name": "agent_id",
          "description": "Agent ID to apply policy to",
          "required": false
        },
        {
          "name": "budget_usd",
          "description": "Budget amount in USD",
          "required": true
        }
      ]
    },
    {
      "name": "analyze_agent_costs",
      "description": "Analyze cost patterns for an agent over time",
      "arguments": [
        {
          "name": "agent_id",
          "description": "Agent ID to analyze",
          "required": true
        },
        {
          "name": "days",
          "description": "Number of days to analyze",
          "required": false
        }
      ]
    },
    {
      "name": "review_pending_approvals",
      "description": "Review and triage pending approval requests",
      "arguments": []
    },
    {
      "name": "compliance_audit_report",
      "description": "Generate a compliance audit report",
      "arguments": [
        {
          "name": "start_date",
          "description": "Report start date",
          "required": false
        },
        {
          "name": "end_date",
          "description": "Report end date",
          "required": false
        }
      ]
    }
  ]
}

Execute prompt template

POST /api/v1/mcp/prompts/{name}

Example:

curl -X POST \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "arguments": {
      "agent_id": "trading-bot",
      "days": 7
    }
  }' \
  "https://platform.agentsentinel.dev/api/v1/mcp/prompts/analyze_agent_costs"

Response:

{
  "prompt": "analyze_agent_costs",
  "data": {
    "summary": "The trading-bot has spent $45.23 over 7 days across 1,234 actions. Top cost drivers: call_llm ($23.45, 456 calls), search_web ($12.34, 234 calls). Average cost per run: $0.37. Recommendation: Consider caching search results to reduce API calls.",
    "details": {
      "total_cost_usd": 45.23,
      "total_actions": 1234,
      "total_runs": 123,
      "avg_cost_per_run": 0.37,
      "top_actions": [
        {"action": "call_llm", "cost": 23.45, "count": 456},
        {"action": "search_web", "cost": 12.34, "count": 234}
      ]
    }
  }
}

Using MCP with Claude

Give Claude access to Agent Sentinel via tool use:

from anthropic import Anthropic
import requests

# Fetch MCP tools
response = requests.get(
    "https://platform.agentsentinel.dev/api/v1/mcp/tools",
    headers={"Authorization": f"Bearer {api_token}"}
)
mcp_tools = response.json()["tools"]

# Convert to Anthropic tool format
anthropic_tools = [
    {
        "name": tool["name"],
        "description": tool["description"],
        "input_schema": tool["input_schema"]
    }
    for tool in mcp_tools
]

# Give Claude access
client = Anthropic()
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=4096,
    tools=anthropic_tools,
    messages=[{
        "role": "user",
        "content": "Check if there are any critical-priority approvals that have been pending for more than 5 minutes. If so, show me the details."
    }]
)

# Process tool calls
if response.stop_reason == "tool_use":
    for block in response.content:
        if block.type == "tool_use":
            # Execute MCP tool
            result = requests.post(
                "https://platform.agentsentinel.dev/api/v1/mcp/call",
                headers={"Authorization": f"Bearer {api_token}"},
                json={
                    "tool_name": block.name,
                    "arguments": block.input
                }
            )
            print(result.json())

Using MCP with OpenAI

from openai import OpenAI
import requests

# Fetch MCP tools
response = requests.get(
    "https://platform.agentsentinel.dev/api/v1/mcp/tools",
    headers={"Authorization": f"Bearer {api_token}"}
)
mcp_tools = response.json()["tools"]

# Convert to OpenAI function format
openai_functions = [
    {
        "name": tool["name"],
        "description": tool["description"],
        "parameters": tool["input_schema"]
    }
    for tool in mcp_tools
]

# Give GPT access
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": "Create a policy to limit my development agent to $5 per run"
    }],
    functions=openai_functions,
    function_call="auto"
)

# Process function calls
if response.choices[0].message.function_call:
    function_call = response.choices[0].message.function_call
    # Execute MCP tool
    result = requests.post(
        "https://platform.agentsentinel.dev/api/v1/mcp/call",
        headers={"Authorization": f"Bearer {api_token}"},
        json={
            "tool_name": function_call.name,
            "arguments": json.loads(function_call.arguments)
        }
    )

Example workflows

Autonomous approval management

User: "Review all pending approvals and approve any low-risk actions that cost less than $1"

Claude uses MCP tools:
1. get_pending_approvals() → Returns 5 pending approvals
2. For each approval with risk_level="low" and cost < $1:
   - approve_action(approval_id, approver_email="claude@company.com", notes="Auto-approved: low risk, low cost")
3. Returns summary: "Approved 3 low-risk actions, 2 remain pending for human review"

Cost optimization analysis

User: "Analyze costs for all my agents and suggest policy changes to reduce spend"

Claude uses MCP tools:
1. list_runs() → Get all runs
2. For each unique agent_id:
   - get_agent_stats(agent_id) → Get cost breakdown
3. Analyzes patterns
4. create_policy() → Creates budget policies for high-cost agents
5. Returns report with recommendations

Compliance reporting

User: "Generate a compliance report for the last 30 days"

Claude uses MCP prompts:
1. execute_prompt("compliance_audit_report", {start_date: "2024-11-28", end_date: "2024-12-28"})
2. Returns formatted compliance report with:
   - Total actions logged
   - Human oversight events
   - Policy violations
   - Data retention status

Best practices

Use prompts for complex workflows: Prompts encapsulate multi-step operations - use them instead of chaining multiple tool calls.

Secure API tokens: MCP gives LLMs full access to your platform - use read-only tokens for analysis tasks, write tokens only when necessary.

Validate LLM decisions: For critical operations (approvals, policy changes), implement human-in-the-loop confirmation before executing MCP tool calls.

Cache tool schemas: Tool and resource lists change infrequently - cache them to reduce API calls.

Getting started

Concepts

Project direction

SDK (Python)

Platform API

Web Console

MCP (Model Context Protocol)

Overview

Endpoints

List available tools

Execute MCP tool

List available resources

Get resource data

List available prompts

Execute prompt template

Using MCP with Claude

Using MCP with OpenAI

Example workflows

Autonomous approval management

Cost optimization analysis

Compliance reporting

Best practices

See also

​Overview

​Endpoints

​List available tools

​Execute MCP tool

​List available resources

​Get resource data

​List available prompts

​Execute prompt template

​Using MCP with Claude

​Using MCP with OpenAI

​Example workflows

​Autonomous approval management

​Cost optimization analysis

​Compliance reporting

​Best practices

​See also

Overview

Endpoints

List available tools

Execute MCP tool

List available resources

Get resource data

List available prompts

Execute prompt template

Using MCP with Claude

Using MCP with OpenAI

Example workflows

Autonomous approval management

Cost optimization analysis

Compliance reporting

Best practices

See also