Model Context Protocol (MCP)

Overview

Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect with external data sources and tools. Poggio's MCP integration creates a unified ecosystem where your AI agents can seamlessly access enterprise data while maintaining strict security controls.

What is MCP?

MCP standardizes how AI models interact with external systems, creating a secure bridge between AI assistants and your organization's tools, databases, and services. Think of it as a universal connector that allows AI to work with your existing infrastructure without compromising security or requiring complex custom integrations.

Business value of MCP for enterprise organizations

Enhanced AI Capabilities

  • Contextual intelligence: AI agents gain access to real-time business data, enabling more accurate and relevant responses

  • Unified workflows: Connect disparate systems through a single AI interface, reducing context switching for users

  • Custom tool integration: Extend AI capabilities with organization-specific tools and processes

Security and governance

  • Controlled access: MCP provides granular permission controls, ensuring AI only accesses authorized data

  • Audit trail: Complete visibility into AI interactions with enterprise systems

  • Data sovereignty: Keep sensitive data within your security perimeter while enabling AI access

Operational efficiency

  • Reduced integration complexity: Standardized protocol eliminates the need for custom API integrations

  • Scalable architecture: Add new data sources and tools without rebuilding AI workflows

  • Developer productivity: IT teams can quickly connect new systems using established MCP patterns

Poggio's MCP implementation

Poggio supports MCP in two powerful ways:

  1. MCP client: Poggio connects to external MCP servers over the standard HTTP transport. Admins can register servers with URL and optional Bearer token, test connections to discover tools, and selectively enable tools for agents (default disabled). A workspace-wide limit of 64 enabled tools applies across all servers.

  2. MCP server: Poggio exposes an MCP server at mcp.poggio.io/mcp. External clients authenticate with workspace-scoped API tokens. Poggio implements the "deep research" interface via search and fetch, and also provides a create_account tool for queueing deep research and monitoring within the system.

This bidirectional approach creates a flexible AI ecosystem where Poggio serves as both a consumer of revenue data and a provider of account intelligence for downstream systems, complementing Poggio's bidirectional CRM sync to support flexible customer data integration for revenue operations teams.

Use cases

Connecting Poggio to internal data and applications

  • Access customer data from GTM SaaS applications during agent conversations

  • Query internal databases for real-time business metrics

  • Integrate with document management systems for contextual file access

  • Connect to monitoring tools for operational insights

Leveraging Poggio intelligence to enhance external agents

  • Enable developers to build custom applications, programmatically adding accounts to Poggio and retrieving deep research and metadata

  • Integrate Poggio’s MCP tools into existing AI workflows

  • Provide unified access to organizational knowledge across multiple AI interfaces

Getting started

Ready to enhance your AI infrastructure with MCP? Follow these journey guides:

Last updated