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Connectors for MCP (Model Context Protocol) are currently in Beta. Features and compatibility may change as the protocol evolves.

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Overview

Praxis middleware empowers you to define and deploy connectors that seamlessly link Pria, your digital assistant, to hundreds of thousands of remote MCP (Model Context Protocol) servers.

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What is MCP?

MCP is an open standard designed to integrate external applications with AI models—think of it as the universal “USB-C port” for AI ecosystems. By utilizing remote MCP connectors, Praxis leverages OpenAI’s Server-Sent Events (SSE) to establish live, two-way communication between Pria and virtually any external service, database, or API.

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Expanded Capabilities

Your digital twin’s reach and intelligence are no longer confined to its internal capabilities. Instead, it can interact dynamically with a vast landscape of external systems, including:
  • Business applications (CRM, ERP, project management)
  • Databases (SQL, NoSQL, data warehouses)
  • APIs (REST, GraphQL, webhooks)
  • Cloud services (AWS, Azure, Google Cloud)
  • Custom applications and proprietary systems (Salesforce, Hubspot, Whatsapp, Slack, etc.)

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Key Benefits

Unified Integration

Tap into any external business logic, data service, or proprietary application seamlessly

Workflow Automation

Automate cross-platform workflows and eliminate manual processes

Break Down Silos

Unify fragmented services without complex custom integrations

AI-Powered Solutions

Unlock new AI capabilities by connecting to specialized external tools

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Prerequisites

Critical Setup Requirement: Always confirm that the conversation model you select for Pria explicitly supports the MCP protocol. If not supported, the system will silently ignore any attached MCP connectors, potentially leaving integrations inactive with no obvious warning.

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Supported Models

  • OpenAI GPT-4 series (with MCP support)
  • Other MCP-compatible models (check model documentation)

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Configuration

Navigate to your instance Edit → MCP Connectors and Tools panel to manage your connectors.
Example Use Case: Add the Praxis AI Documentation as a connector so your digital twin can reference official documentation when responding to questions about Pria’s features and usage.

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Connector List View

MCP Connectors List Only connectors with Status: Enabled are used by your Digital Twin. You can quickly enable/disable connectors from this view.

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Creating/Editing Connectors

Connector MCP Configuration

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Required Fields

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name
string
required
Connector Name (Label): Must match your MCP Server Label exactly
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status
select
default:"Active"
Status:
  • Active - Connector is enabled and available to the Digital Twin
  • Inactive - Connector is disabled
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description
string
Description: A description of your MCP server’s purpose, sent to the AI model as context. Use this to help the AI understand when and how to use the server’s tools.Example: "This server provides access to the company's CRM. Use it to look up customer records, update contact information, and create support tickets."
A well-written description significantly improves how reliably the AI selects the right tools from your MCP server. Be specific about what the server does and when the AI should use it.
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type
select
default:"url"
Type: Communication method
  • url - URL-based communication (currently the only supported method)
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server_url
string
required
Server URL: The endpoint of your remote MCP serverExample: https://docs.praxis-ai.com/mcp

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Tool Management

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tools_enabled
boolean
default:"false"
Tools Filter: Enable to select a subset of available tools
Recommended: Enable this option when your MCP server has many tools. Some servers may expose hundreds of tools, so filtering helps optimize performance and focus functionality.
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tools_choice
array
Selected Tools: List of specific tool names to enable
  • Tool names are case-sensitive
  • Use only alphanumeric characters, dashes, and underscores
  • Allowed characters may vary by LLM model
When to use tool filtering: If your MCP server exposes dozens or hundreds of tools (common with platforms like Zapier or Salesforce), enable the tools filter and select only the tools your Digital Twin actually needs. This reduces token usage and improves response quality — the AI doesn’t have to evaluate tools it will never use.

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Approval Settings

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requires_approval
boolean
default:"false"
Requires Approval: When enabled, the LLM requests user permission before using MCP tools
Recommendation: Leave disabled for most use cases to maintain smooth user experience
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ignore_approval_tools
array
Ignore Approval for Tools: List of tools that bypass the approval step when approval is requiredUse this for frequently-used, low-risk tools like search or read-only operations.

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Authentication

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authorization_header
string
Authorization Header: Service-level authentication tokenFormat: Bearer xyz123...
Security Note: Praxis AI middleware uses service-level credentials. All requests share the same credentials to access the remote MCP server.

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Best Practices

  • Use service accounts with minimal required permissions
  • Regularly rotate authorization tokens
  • Monitor MCP server access logs
  • Implement rate limiting on your MCP servers
  • Enable tool filtering for servers with many available tools
  • Use descriptive connector names for easy management
  • Test connectors in development before production deployment
  • Monitor response times and adjust timeouts as needed
  • Verify model MCP compatibility before deployment
  • Check authorization headers and server URLs
  • Ensure tool names match exactly (case-sensitive)
  • Monitor server logs for connection issues

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Next Steps

Read on Model Context Protocol

Read on Model Container Protocol

Search through existing MCP Servers

Search Existing MCP server

Develop on hoster platforms

Develop MCP servers on hosted platforms like smithery.ai zapier.com

Praxis AI is also an MCP Server

Connect Claude, ChatGPT, or your favorite LLM to Praxi-AI