Local Document MCP Server with Marker and ChromaDB

This project implements a Python MCP server for OpenCode. It reads PDF, Word (.docx), PowerPoint (.pptx), and EPUB files located under DOCS/, always excluding DOCS/mdDB/. It converts the documents to Markdown with Marker, preserves tables and equations as LaTeX, stores the complete Markdown files under DOCS/mdDB/, and creates a persistent semantic index in ChromaDB.

Retrieval is structure-aware. ChromaDB locates the chunks most relevant to a query, but the MCP server does not return an isolated chunk. It uses the result metadata to open the original Markdown file and reconstruct the complete section delimited by headings. The response includes the surrounding text, tables, and equations, together with file paths and line ranges.

Data Flow

flowchart TD
    A["DOCS: PDF, DOCX, PPTX, EPUB"] --> B["Marker 2"]
    B --> C["Complete Markdown + images"]
    C --> D["DOCS/mdDB"]
    C --> E["Structural chunks"]
    E --> F["Local ChromaDB"]
    G["OpenCode query"] --> F
    F --> H["Chunk metadata"]
    H --> D
    D --> I["Complete Markdown section"]
    I --> G

At a minimum, each chunk stores source_path, markdown_path, section_title, section_path, section_start_line, section_end_line, chunk_start_line, and chunk_end_line. It also stores SHA-256 hashes for the source document and Markdown file to detect changes.

Related MCP server: Personal Semantic Search MCP

Project Structure

current-project/
├── DOCS/
│   ├── article.pdf
│   ├── manual.docx
│   └── mdDB/
│       ├── article.md
│       ├── manual.md
│       └── .chroma/
├── .opencode/
│   └── MCP/
│       └── opencode-document-rag-mcp/
│           ├── src/doc_rag_mcp/
│           ├── tests/
│           ├── README.md
│           └── pyproject.toml
└── opencode.jsonc

Source documents may be placed directly under DOCS/ or in any of its subdirectories except DOCS/mdDB/. Their relative directory structure is preserved in the output. For example, DOCS/manuals/instrument.pdf produces DOCS/mdDB/manuals/instrument.md. Extracted images are stored next to the Markdown file under instrument_assets/, and their links are rewritten as relative paths. The entire DOCS/mdDB/ tree is excluded from discovery so the MCP server cannot process its own output.

Requirements

Python 3.10–3.13 and uv are required. Marker 2 requires an inference backend for OCR and equations. llama.cpp is recommended on macOS or CPU-only systems. Systems with NVIDIA GPUs can use the VLLM backend configured through Surya.

On macOS:

brew install uv llama.cpp

On Linux, install uv and a recent llama-server binary provided by llama.cpp. For NVIDIA systems, install Docker and the NVIDIA Container Toolkit according to Marker’s requirements.

Installation

Extract the release archive directly into the root of the current project. The archive already contains the .opencode/MCP/opencode-document-rag-mcp/ directory structure:

cd /path/to/current-project
unzip opencode-document-rag-mcp-v1.1.2.zip -d .
uv sync --project .opencode/MCP/opencode-document-rag-mcp

After extraction, the MCP server is installed at exactly:

.opencode/MCP/opencode-document-rag-mcp

The first conversion and first vectorization download the required models. The ONNX embedding model is stored under DOCS/mdDB/.chroma/.embedding_models/. Marker models use the cache configured by Marker and Surya. The initial process may take some time and consume several gigabytes. DOCX, PPTX, and EPUB documents require the marker-pdf[full] variant, which is already included in pyproject.toml.

OpenCode Configuration

Copy the configuration from opencode.example.jsonc into the opencode.json or opencode.jsonc file at the project root. If the MCP server is stored elsewhere, change only the path that follows --project.

Minimum configuration:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "document-rag": {
      "type": "local",
      "command": [
        "uv",
        "run",
        "--project",
        ".opencode/MCP/opencode-document-rag-mcp",
        "doc-rag-mcp"
      ],
      "cwd": ".",
      "enabled": true,
      "timeout": 30000,
      "environment": {
        "DOC_RAG_PROJECT_ROOT": ".",
        "SURYA_INFERENCE_BACKEND": "llamacpp",
        "SURYA_INFERENCE_KEEP_ALIVE": "true"
      }
    }
  }
}

The cwd: "." setting resolves all paths relative to the root of the project opened in OpenCode. Verify the connection with:

opencode mcp list

The AGENTS.example.md file contains an optional policy that instructs OpenCode to query this MCP server before answering questions about the documents. You can incorporate its contents into the project’s AGENTS.md file.

MCP Tools

Using the MCP Server in OpenCode

Place source documents under DOCS/, but never under DOCS/mdDB/. You can then use requests such as:

Use document-rag to list the available documents.

Use ingest_all_documents to convert and index every source document under DOCS, excluding mdDB.

Search the documents for the definition of wave energy flux, preserving the related LaTeX equations and tables.

Search only manual_tecnico.pdf for the instrument's operating limits and cite the Markdown section and line range.

Read the Methods > Statistical analysis section from article.docx.

Expanded Retrieval

search_documents accepts query, a top_k value from 1 through 20, and an optional document_name. Internally, it requests additional results from ChromaDB so multiple chunks from the same section do not occupy every result position. It then removes duplicate sections and returns up to top_k distinct sections.

Each result contains context, which is the complete section read from disk at query time. index_is_current indicates whether the Markdown file still has the same hash it had when it was indexed. If this value is false, run ingest_document or ingest_all_documents. When the source document has not changed, the system reindexes the existing Markdown without running Marker again.

Conversion and Equations

Marker emits formatted tables and LaTeX equations delimited by $$. The default mode is balanced, which is appropriate when table, OCR, and mathematical fidelity are the priority. On CPU or Apple Silicon systems, reduce processing cost with:

"DOC_RAG_MARKER_MODE": "fast"

For scanned documents or unreadable text:

"DOC_RAG_FORCE_OCR": "true"

For Marker’s optional hybrid correction through a compatible LLM service:

"DOC_RAG_USE_LLM": "true"

The last option requires credentials and a service supported by Marker. It is not required for normal MCP server operation.

Environment Variables

Security and Consistency

The server rejects unsupported extensions, .. path traversal, sources outside DOCS/, any source inside DOCS/mdDB/, and Markdown paths outside DOCS/mdDB/. A path stored in ChromaDB is never used without being validated again. Markdown writes are atomic, and index replacement is limited to the corresponding document.

If two files in the same directory have the same base name, such as manual.pdf and manual.docx, both would produce manual.md. The server detects this collision and requires one of the source files to be renamed before writing or indexing.

Tests

The unit tests do not load Marker or ChromaDB. They validate hierarchical segmentation, preservation of tables and equations, section expansion, and path protection:

PYTHONPATH=src python -m unittest discover -s tests -v

You can also check the syntax of the complete source tree with:

python -m compileall -q src tests

Licenses

This project is distributed under the MIT License. Marker uses the Apache-2.0 License for its code and a separate license for its model weights. Review Marker’s terms before large-scale commercial use.