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Extract positioned text runs

extract_entities
Read-onlyIdempotent

Extract text runs with layout geometry (page, x, y, font) from PDFs for positional lookup and table reconstruction.

Instructions

Extract every text run of a PDF together with its layout geometry.

Returns JSON {path, entities, entity_count, page_count} where each entity is {text, page (0-based), x, y, font_size, font_name}. Coordinates are in PDF points with the origin at the bottom-left of the page. Read-only.

Use this for layout-aware tasks (table reconstruction, positional lookup, locating a label on the page). If you only need the reading text without coordinates, use extract_text; for Markdown or RAG chunks use convert_pdf.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the PDF file, relative to the configured workspace.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. Changed1 schema field changedv0.12.0
    • addedInput schema / properties / path / description
      Added value: +"Path to the PDF file, relative to the configured workspace."
  2. First observedv0.11.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already provide readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral context: output structure (JSON with fields), coordinate system (PDF points, bottom-left origin), and explicitly states 'Read-only.' No contradiction with annotations.

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 three sentences: first sentence states purpose, second explains output, third gives usage guidelines. It is concise, front-loaded with key information, and every sentence serves a purpose.

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?

The description explains the output structure in detail (JSON with path, entities, entity_count, page_count, and entity fields). Output schema exists but description covers it sufficiently. For a 1-parameter tool, all necessary context is provided.

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?

Schema coverage is 100% with a clear parameter description in the schema itself. The tool description does not repeat or add significant value beyond the schema's definition of the 'path' parameter. Baseline 3 is appropriate.

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 clearly states 'Extract every text run of a PDF together with its layout geometry.' It distinguishes from siblings by explicitly naming alternatives: 'use extract_text' for reading text without coordinates, and 'use convert_pdf' for Markdown or RAG chunks.

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

Usage Guidelines5/5

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

The description gives explicit usage context: 'Use this for layout-aware tasks...' and clearly states when not to use it by specifying alternatives, e.g., 'If you only need the reading text without coordinates, use extract_text; for Markdown or RAG chunks use convert_pdf.'

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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