PDF Reader MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| read-pdfA | Extract text from a PDF file. Returns the full text content of the PDF with optional page filtering and text cleaning. |
| search-pdfA | Search for specific text within a PDF file. Returns matching text with context and page numbers. |
| pdf-metadataB | Extract metadata from a PDF file including title, author, page count, creation date, etc. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: pdf-metadata extracts document properties, read-pdf extracts all text content, and search-pdf finds specific text within the document. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent verb-noun pattern using hyphens (pdf-metadata, read-pdf, search-pdf). The naming is predictable and readable, with 'pdf' consistently placed as a suffix or prefix across all tools.
Three tools is a minimal but reasonable count for a PDF reader server, covering core operations: metadata extraction, full text reading, and text search. It could benefit from additional tools like PDF conversion or annotation handling, but it's well-scoped for basic use.
The toolset covers essential PDF reading operations: metadata, full text extraction, and search. Minor gaps exist, such as no tools for PDF manipulation (e.g., merging, splitting) or advanced features like OCR, but agents can perform basic workflows without dead ends.