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Glama
pablontiv

PDF Reader MCP Server

by pablontiv

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
extract_pdf_textC

Extract text content from PDF documents with optional metadata and formatting preservation

extract_pdf_metadataC

Extract metadata and document information from PDF files

extract_pdf_pagesC

Extract content from specific pages or page ranges of PDF documents

validate_pdfB

Validate PDF file integrity and readability

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.5/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: metadata extraction, page extraction, text extraction, and validation. The descriptions reinforce these differences, with no overlap that would cause agent confusion or misselection.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with 'extract' or 'validate' as the verb and 'pdf' plus a specific noun (metadata, pages, text, or implied file for validate). The naming is uniform and predictable throughout the set.

Tool Count5/5

With 4 tools, the server is well-scoped for a PDF reader domain, covering key operations without bloat. Each tool earns its place by addressing a distinct aspect of PDF processing, making the count appropriate and focused.

Completeness4/5

The tool set covers essential PDF operations like metadata, content extraction, and validation, but lacks tools for actions like merging, splitting, or converting PDFs. These gaps are minor and agents can likely work around them for basic reading tasks.