Logseq & MCP: Unveiling Hidden Blocks & Page Structure

by Alex Johnson 55 views

Welcome, fellow Logseq enthusiasts! Have you ever encountered the frustration of your Large Language Models (LLMs) seemingly missing the intricate details within your Logseq pages when connected through an MCP (Model Communication Protocol) server? It's a common puzzle, and we're diving deep into it today. We will explore the challenges of MCP's limitations in exposing the full outline structure and how it impacts interaction with the LLMs. This article is your guide to understanding the issue, its potential causes, and what we can do to navigate these complexities. We will break down the problem, explore the expected behavior, and delve into the reasons behind the observed behavior. Prepare to understand the nuances of how Logseq interacts with MCP and the crucial role of the underlying structure in the performance of LLMs.

The MCP Conundrum: Understanding the Block Hierarchy

Let's paint a picture. Imagine you have a Logseq page, meticulously organized with nested blocks: a top-level block 'A', followed by sub-blocks 'B' and 'C', and then another top-level block 'D'. When you query the model, it appears to only acknowledge 'A' and 'D', completely oblivious to the existence of 'B' and 'C'. This is the core of the problem: the MCP server might be only exposing top-level blocks. This means that when the model queries a page, it only receives information about the highest level of organization. This limitation significantly hinders the ability to fully grasp the structure and content within a page.

This is not just a technicality; it strikes at the heart of how we use Logseq. Logseq's power lies in its ability to create complex and deeply connected notes, and the hierarchical structure of blocks is fundamental to this functionality. When that hierarchy is hidden from the model, the model is effectively blind to a large part of the notes.

Analyzing the Symptoms

The issue manifests as an inability for the LLMs to accurately list or interact with nested content. When asked to list all blocks, the model provides an incomplete response, missing the sub-blocks that are nested under the top-level blocks. Upon further inquiries, the model will often assert that these sub-blocks do not exist. This behavior hints at a fundamental communication problem: the model is not receiving the complete page structure from the MCP server. It's like trying to navigate a detailed map where only the main roads are visible, and everything else is a mystery. Therefore, a deeper understanding of the MCP and how it communicates with the underlying data structure of Logseq is required.

Reproducing the Bug & Identifying the Root Cause

The most straightforward way to reproduce the bug is to set up an MCP server and then ask the model to list all blocks within a page containing nested blocks. The exact steps are as follows:

  1. Activate an MCP server: Ensure your MCP server is running and connected to your Logseq graph.

  2. Create a Page with Nested Blocks: Create a Logseq page with a structure such as the following:

    - A
  - B
  - C
- D

  3. Query the Model: Ask the model connected to the MCP server to list all blocks within the page. For example, you might use a prompt like, "List all the blocks in this page."

  4. Observe the Response: The model should respond with something similar to: "A", "D". The key observation is the absence of "B" and "C".

The Problem Unveiled

The root cause likely lies within how the MCP server interacts with the Logseq data structure. There are two primary possibilities:

  1. MCP Server Limitations: The MCP server might be designed to only expose top-level blocks for efficiency or simplicity. This design choice would prevent the LLM from accessing the nested blocks.
  2. Incorrect Tool Description: The tools provided to the LLM might be incorrectly described. The model may not have the appropriate tools to navigate or parse the nested structure, even if the MCP server provides the data.

Understanding which of these is the issue is important in creating a solution. Testing different MCP servers, tools, and configurations can help determine the exact source of the problem. Further investigations may involve looking at the MCP server's code, or the documentation provided to the LLM.

Expected Behavior vs. Observed Reality

The expected behavior in an ideal scenario is for the LLM to understand and list all blocks in the page accurately, including all nested blocks. The LLM should be able to follow the complete hierarchical structure as presented in Logseq.

Expected Response

For the example structure provided above, the model should ideally respond with something like:

  • A
    • B
    • C
  • D

This behavior is fundamental for using LLMs to interact with Logseq content. It would enable powerful features, such as:

  • Precise Information Retrieval: The model can find and present information from any part of your notes.
  • Automated Note Summarization: The model can summarize your notes at different levels of detail, taking into account the nested structure.
  • Effective Content Generation: The model would create new content within the context of the existing nested blocks.

The reality, however, often falls short of this ideal. The LLMs' inability to see nested blocks limits these powerful functionalities. Instead of comprehensive insights, we get incomplete summaries and inaccurate information, which significantly decreases productivity.

Potential Solutions & Workarounds

Addressing the limitations of MCP requires either changes to the MCP server or improvements in how the LLMs are instructed to interact with Logseq's data. Here are potential solutions and workarounds:

  1. Enhancements to the MCP Server: The most direct solution is to modify the MCP server to expose the complete block structure. This could involve recursively fetching and sending all blocks, including nested blocks.
  2. Improved Tool Descriptions: Ensure that the tools provided to the LLM are correctly described, so that the model knows how to interpret the nested blocks. This involves accurately describing the data structure and providing tools to navigate it.
  3. Pre-processing of the Data: One workaround is to preprocess the Logseq data before sending it to the LLM. This could involve flattening the structure or encoding the hierarchy in a way the LLM can understand.
  4. Experimentation with Different LLMs: Some LLMs might be better at handling nested structures than others. Experimenting with different models can show better results.

Detailed Solutions

MCP Server Enhancement

This solution requires modifications to the backend of the MCP server.

  • Recursive Retrieval: The server needs to recursively fetch all blocks and their children.
  • Structured Data Format: The server should expose the block as a well-defined structured format, such as JSON, to the LLM. This provides a clear representation of the nested hierarchy.

Improved Tool Description

This requires precise instructions given to the LLM. Ensure it is aware of nested blocks and how to navigate the structure.

  • Clear Documentation: Provide detailed documentation to the LLM on the Logseq data structure.
  • Example Queries: Include examples of how to query for specific blocks and their children.

Pre-processing

  • Flattening the Structure: You can flatten the block structure to make it simpler to understand. However, this might lose some context.
  • Encode Hierarchy: Use special characters or tags to represent the hierarchy. The LLM then needs to learn to interpret the special characters.

The Path Forward: Contributing to a Solution

If you're willing to submit a pull request (PR), it could make a significant difference. Here's a breakdown of how to proceed:

  1. Identify the Specific Code: Determine the code responsible for handling block retrieval within the MCP server. This may involve inspecting the server's code base to understand where the block data is accessed and sent.
  2. Modify the Retrieval Logic: Modify the code so that it recursively fetches the block and its children. Make sure the hierarchy is maintained when constructing the data structure to send to the LLM.
  3. Test Thoroughly: Test your changes to make sure that the nested blocks are correctly exposed and that the model can understand the page. Use a test scenario similar to the one described earlier to verify that all the blocks are being listed.
  4. Submit a PR: Submit a pull request with your changes to the project. Include any necessary documentation and testing instructions.

Collaborative Efforts

This problem is best solved by collaboration. Sharing your experience and contributing to the discussions will help the community. If you are not in a position to submit a PR, you can still help by:

  • Reporting the Issue: Clearly describe the problem and its potential impact.
  • Providing Detailed Examples: Include specific examples of page structures and expected/observed outputs.
  • Joining Discussions: Take part in the discussions and help to identify solutions.

Conclusion: Navigating the Complexities

In conclusion, the issue of MCP not exposing nested blocks is a significant problem that can limit the potential of using LLMs within Logseq. The key lies in understanding how MCP communicates with the underlying Logseq data structure and identifying the limitations in data exposure. Whether the answer lies in changes to the MCP server, improvements in the tool description, or pre-processing of data, it is a problem that requires a multifaceted approach. By understanding the challenges, the expected behavior, and the various solutions and workarounds, we can begin to improve and realize the potential of LLMs within Logseq. By contributing to the solution and working together, we can work towards a more seamless, powerful, and intelligent note-taking experience.

For more information, consider exploring these resources:

  • Logseq Documentation: Official documentation of Logseq.
  • [Model Communication Protocol (MCP) Documentation]([invalid URL removed]): Information on how the MCP protocol works.

Remember, your active engagement can help propel the improvements within the Logseq community and create a better integration of LLMs within your workflow.