document-reader

name	document-reader
description	Load local documents (PDFs, images, handwritten scans, JSON/CSV, text) via the document-reader MCP tool so agents can extract and summarize content without re-implementing file I/O.

Document Reader Skill

Use this skill when the user provides (or references) local files and you need a reliable way to ingest them into the agent workflow:

• Unstructured docs: PDFs, images, scanned or handwritten notes (returned as base64 + mime)
• Structured docs: JSON, NDJSON, CSV/TSV (returned as text plus optional parsing)
• Plain text: TXT/MD/etc.

This avoids ad-hoc “write a quick Python function to read X” every time.

Prerequisites (Local)

1. Start the MCP server:

./scripts/local-test.sh document-reader 7078

2. Ensure .vscode/mcp.json includes:

• local-document-reader: http://localhost:7078/mcp

Tool: read_document

When To Use

• The user says “see attached PDF/image” or provides a file path
• You need to extract fields from a form, notes, or report
• You need to load structured input (JSON/CSV) for transformation/validation

Safety Defaults

• Reads are workspace-only by default. To read outside the repo, set allow_outside_workspace=true.
• Large files are blocked or truncated via max_bytes / max_chars.

Typical Calls

Read a PDF or image as base64 (for downstream OCR/vision or archive):

{
  "path": "data/intake/scanned_note.jpg",
  "mode": "binary",
  "include_data_url": true
}

Read JSON (returns both text and parsed json when valid):

{
  "path": "data/sample_cases/prior_auth_baseline/pa_request.json",
  "mode": "text",
  "parse_structured": true
}

Read CSV (returns rows up to max_rows):

{
  "path": "data/input/patients.csv",
  "mode": "text",
  "max_rows": 200
}

Suggested Workflow

1. Call read_document for each referenced file path.
2. For binaries (PDF/images), use the returned mime + data_url/base64 to drive downstream extraction.
3. Produce a de-identified structured summary (never commit PHI or secrets).