How to attach citation metadata to source documents inside MCP?

 

Step 1: Understand MCP and Document Integration

 

To begin with, it's important to understand what MCP (Model Context Protocol) is in the context of AI and LLMs. It serves as a structured way to deliver context, including document source information, to a language model. This step ensures that you grasp the significance of attaching citation metadata to source documents, as it helps maintain predictable and effective model behavior by providing additional context.

 

Step 2: Prepare and Structure Your Documents

 

Before attaching citation metadata, ensure your documents are well-organized and formatted consistently. Document preparation involves structuring them in a way that those reading, parsing or used by LLMs, can easily identify sections that will eventually have citation information.

 

Step 3: Identify Necessary Metadata

 

Identify the metadata elements that need to be included to ensure proper citation and reference. This typically may include:

• HTML Tags:

  Elements or identifiers that clearly mark metadata.

• Author Name:

  The name of the individual or organization.

• Publication Date:

  The date of publication or the last update.

• Source Title:

  The title of the document or web source.

• Publisher/Website Name:

  Where it was published.

This task should be painstakingly synchronized with the system instructions in MCP to derive effective usage.

 

Step 4: Format Metadata for Integration

 

Format the metadata following a standardized schema that will then plug into MCP. Use a JSON object or XML for structured data representation, which helps in easy transmission and retrieval. A typical JSON structure might look like this:

{
    "author": "Jane Doe",
    "publicationDate": "2023-10-15",
    "sourceTitle": "Understanding MCP in AI",
    "publisher": "AI Research Journal"
}

 

Step 5: Integrate with MCP Document Context

 

Modify the MCP context entry to include the document and its citation metadata. Use the 'Document Context' part of MCP to embed this data, ensuring it's immediately accessible by the language model.

Example in practical context:

{
    "system_instructions": "You are a research assistant.",
    "document_context": [
        {
            "document": "Understanding MCP in AI",
            "metadata": {
                "author": "Jane Doe",
                "publicationDate": "2023-10-15",
                "sourceTitle": "Understanding MCP in AI",
                "publisher": "AI Research Journal"
            }
        }
    ],
    "active_tasks": "Summarize the attached documents with proper citation."
}

 

Step 6: Verify and Validate Metadata Attachment

 

Once the metadata is attached, perform validation checks to ensure that fields are correctly populated and adhere to the predefined standards. Test the integration by running tasks or queries that require citing documents, ensuring the LLM references them correctly and predictably.

 

Step 7: Test Model Behavior with MCP Context

 

After attaching and validating the citation metadata, scrutinize the operation of the language model in a test environment. Assert that the document and its corresponding metadata are utilized effectively, leading to a predictable and consistent response that acknowledges the citations.