​

Overview

Collate’s MCP Server gives technical or non-technical users the ability to interact with your organization’s metadata through natural language conversations via systems such as ChatGPT or Claude.

​

What is MCP?

The Model Context Protocol (MCP) is an emerging open standard (spearheaded by Anthropic and embraced by many industry leaders) that helps AI systems interact with external tools and data in a uniform, secure way. Instead of building one-off integrations or brittle scripts for each data source, MCP works as a “universal translator” between AI assistants (or any LLM-driven application) and the myriad of systems where data and knowledge reside. In technical terms, MCP lets systems expose their capabilities in a machine-readable schema that AI models can understand:

• Tools: Functions an AI can call, such as lookup_customer_by_email.
• Resources: Datasets or knowledge bases an AI can query.
• Prompt templates: Predefined prompts that guide interactions.

An AI assistant connected via MCP can securely retrieve information or trigger actions by invoking these standardized functions, with proper authorization. For organizations, MCP bridges the gap between powerful AI reasoning and real-world data context:

• A single, consistent protocol lets an AI assistant maintain awareness of business-specific context as it moves between different tools and datasets.
• Just as HTTP standardized how clients talk to servers, MCP standardizes how AI models connect with data sources.
• The result is a simpler, more scalable way to give AI access to the knowledge it needs to produce relevant, accurate results.

​

Adding an MCP Server to Collate

Even the best LLMs need context to operate effectively. Collate’s MCP Server embeds directly into the platform and exposes rich metadata context from its unified knowledge graph to AI assistants and other MCP clients in real time. This means an AI tool like ChatGPT or Claude can query Collate and get answers grounded in live organizational metadata, for example:

• “What is the definition of this metric?”
• “Show me the lineage of data feeding this dashboard.”
• “Who is the owner of this dataset, and when was it last updated?”

All the relationships and context captured in Collate’s graph become available to augment AI-driven analyses and automations.

​

Enabling MCP Server

The MCP Server is built into Collate and enabled by default on all Collate SaaS instances. No marketplace installation is required. If the MCP endpoint is not responding on your self-hosted instance, confirm that mcpConfiguration.enabled is set to true in your server configuration. Contact Collate Support if you need help enabling it.

​

Authentication

Collate’s MCP Server supports two authentication methods:

​

OAuth 2.0 (Recommended)

Connect using your existing Collate login – the same way you sign in to the Collate UI. No tokens to generate or manage. OAuth works with all supported SSO providers (Google, Azure, Okta, Auth0, LDAP, SAML, and more). Learn more about OAuth 2.0 Authentication.

​

Personal Access Token (PAT)

For environments where browser-based login is not available, use a Personal Access Token instead.

1. Open https://<YOUR-Collate-SERVER>.
2. Click the Profile icon and select your user name.
3. Click the Access Token tab.
4. Set the expiry time and click Generate Token.
5. Copy and save it for later use.
   Note: By default, the token gives your AI agent the same role and access policy assigned to you in Collate. To scope your agent to different role-based access controls, create a dedicated user and generate the token from that account instead.

​

Monitoring MCP Usage

Collate tracks every Model Context Protocol (MCP) tool call made on your platform. Use the MCP Requests page to monitor MCP tool usage, identify failed calls, and understand how usage contributes to your bill. To access the page:

1. Click the Profile icon and navigate to Settings > Billing.
2. Click MCP Requests.

​

Summary Metrics

The top of the page shows four at-a-glance metrics that give you a quick picture of your MCP activity:

• Total MCP Calls: The total number of MCP tool calls made across all connected clients and tools. Use this to understand overall platform activity at a glance.
• Successful Calls: The number of calls that completed without error. A high success rate generally indicates that your MCP clients and tools are configured correctly.
• Failed Calls: The number of calls that didn’t complete due to issues such as authentication errors, rate limits, or invalid input. Review failed calls to identify misconfigured clients or tools. Failed calls aren’t billed.
• Avg Latency: The average response time per tool call, measured in milliseconds. Use this to spot tools that may be responding slowly.
  Note: Avg Latency isn’t active yet and will be available in a future release.

​

Usage Summaries

Below the summary metrics, the page breaks down MCP activity across three views to help you understand usage patterns in more detail:

• Daily MCP Calls (Last 30 Days): A day-by-day view of all MCP requests across all clients and tools. Use this to spot spikes in activity or identify periods of unexpectedly high or low usage.
• MCP Calls by Tool: A breakdown of how many requests each individual tool has received. Use this to identify which tools are most frequently used and which may not be providing value.
• MCP Calls by User: A breakdown of MCP requests by user. Use this to understand which users or teams are driving the most MCP activity.

​

Connect Your MCP Client

With MCP installed, connect your preferred AI assistant to start prompting with Collate: