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read_document

Extract paragraph text from Word .docx or .docm files as a JSON array of strings, skipping tables and non-paragraph content.

Instructions

Reads an existing Word document (.docx/.docm) and returns its paragraphs as a JSON array of plain strings, one per paragraph, matching create_document's own input convention. Features outside the plain-paragraph model (run styling, tables, images, headers/footers, comments, track changes, content controls, etc.) are not included in this output - see MAPPING.md in the main library repo for the full list of what round-trips. Table content and non-paragraph blocks are skipped entirely.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesPath to an existing .docx or .docm file.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full disclosure burden. It does well by revealing the exact return format, the round-trip convention, and the major excluded feature categories plus where to find the full list. It does not mention error behavior for nonexistent paths or unreadable files, which is a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured: the first sentence states the core behavior and return format, the second provides useful limitations, and the third emphasizes the table/non-paragraph skip rule. No sentence is wasted, and the key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only one parameter, no output schema, and no annotations, the description is sufficiently complete: it states what the tool does, what it returns, which document types are accepted, and which content is omitted. An agent has enough information to decide whether to call it and what to expect from its response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes 'path' as 'Path to an existing .docx or .docm file', so schema coverage is 100%. The description adds context about the expected file type and that the document must already exist, but it does not materially expand beyond the schema's parameter description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Reads'), a specific resource ('existing Word document (.docx/.docm)'), and precisely describes the output ('JSON array of plain strings, one per paragraph'). It also ties the output convention to create_document, which distinguishes the read behavior from the create-oriented sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies when to use the tool: to read an existing Word document as plain paragraphs. It also gives explicit when-not guidance by stating that tables, non-paragraph blocks, and rich features like styling or track changes are skipped entirely. However, it does not name an explicit alternative tool for those unsupported features, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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