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)
Endpoints
List available tools
GET /api/v1/mcp/tools
{
"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
{
"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"]
}
}
{
"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
{
"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}
curl -H "Authorization: Bearer $TOKEN" \
"https://platform.agentsentinel.dev/api/v1/mcp/resources/agentsentinel://stats/dashboard"
{
"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
{
"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}
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"
{
"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.
See also
- SDK MCP Client - Python SDK for MCP integration
- Approvals API - Approval workflow endpoints
- Policies API - Policy management endpoints