Document corpus MCP server

mdcx

<!-- mcp-name: io.github.jorgell23-sys/markdown-document-search -->

Convert a document collection to verified Markdown, package it into a single encrypted archive with its search index and provenance, and serve it to agents over the Model Context Protocol.

Contents

• Scope
• Requirements
• Installation
• Quick start
• Conversion
• Packaging
• Querying
• Retrieval model
• Calibration
• Vocabulary
• Transcription recovery
• Expressions

Scope

mdcx addresses one constraint. An agent asked a question about a document collection must either receive the documents in its context window, which is bounded and billed per token, or query a component that holds an index and returns only the passages that bear on the question. mdcx implements the second.

Three properties define the result:

original exposes and the coverage achieved is recorded per file. Files that expose no text are marked unverifiable rather than reported as complete. What the comparison establishes differs by engine, and Verification states which.

• Fidelity is checked. Each conversion is compared against the text the

single AES-256-GCM file whose header can be read without the key, and which may be signed.

• The corpus is one artefact. Passages, index and provenance are held in a

position, so an answer can be quoted against a location.

• Answers are citable. Every passage carries its source document and its

The pipeline has three stages. Conversion attempts each document with the least expensive engine able to read it and escalates only where that engine falls short: direct text extraction, then a pass that recovers drawn tables, then layout analysis, and optical character recognition for documents that expose no text. Packaging writes the corpus, its index and the provenance of each passage to one file. Retrieval merges word matching and dense retrieval by reciprocal rank, and states when nothing in the corpus bears on the question rather than returning its nearest passage.

Requirements

Python 3.11 or later. No other component is required to query a package.

The floor is 3.11 because a package is one SQLite database, loaded from the decrypted bytes when it is opened, and sqlite3 gained the required call in that version. Conversion and cross-language retrieval each add dependencies, listed under Installation.

Installation

Querying and conversion are separated because their requirements differ by roughly two orders of magnitude. A recipient who only reads packages installs neither Docling nor PyTorch.

Shortened. The full README is on GitHub.