Model Context ProtocolBeta

First-classMCP support

Trace every tool call, context source, and structured output in your MCP-powered agents. Full protocol visibility, zero guesswork.

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MCP Request Lifecycle

Every step is traced and monitored automatically

Agent Request
User query received
Context Assembly
MCP sources queried
Tool Selection
Best tools identified
Execution
Tools invoked in parallel
Validation
Outputs schema-checked
Response
Structured response sent
Complete MCP toolkit

Full protocol observability

Native support for tool definitions, structured outputs, context sources, and everything the Model Context Protocol offers.

Tool Call Tracing

Full visibility into every MCP tool invocation. See inputs, outputs, latency, and error details for each tool call in your agent workflow.

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Context Source Management

Manage and monitor all your context sources from simple documents to runtime data. Track which contexts are used and how they affect output quality.

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Structured Output Validation

Define expected schemas and automatically validate every structured output. Catch malformed responses before they reach your users.

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Universal Provider Support

Works with every MCP-compatible provider out of the box. OpenAI, Anthropic, Google, and custom tools all in one unified view.

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Security & Permissions

Control which tools agents can access, set rate limits, and audit every tool invocation. Built-in guardrails prevent unauthorized actions.

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Server-Side Tracing

Trace MCP server operations end-to-end. Monitor connection pools, request queues, and resource utilization across your MCP infrastructure.

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The standard for AI tools

Model Context Protocol is the emerging standard for AI agent tool use. Intercept gives you first-class support from day one.

MCP protocol v1.0+ support
Custom tool definitions
API-based & code-based tools
Context source monitoring
Schema validation engine
Tool execution analytics
Rate limiting & quotas
Webhook-based integrations
mcp_tools.py
# Define MCP tools with tracing
from intercept.mcp import tool, trace
@tool("search_docs")
def search_docs(query: str):
"""Search the knowledge base."""
results = vector_db.search(query)
return results
@tool("send_email")
def send_email(to, subject, body):
"""Send an email."""
return mailer.send(to, subject, body)
# All tool calls → auto-traced ✓

Ready for the MCP era

Be among the first to get full observability into your MCP-powered agents. Early access available now.

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