pdf-extract-mcp
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 | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| extract_fieldsB | Extract structured fields from an unstructured PDF using regex/heuristics. Args: pdf_path: Path to the PDF file. schema: JSON Schema describing the fields to extract (must have a non-empty 'properties' object). A built-in schema name (e.g. "invoice") or a path to a .json schema file is also accepted. Returns: {"ok": True, "data": {...}, "text_length": N} on success, or {"ok": False, "error": "..."} on failure (corrupt PDF, bad schema, ...). |
| validate_against_schemaA | Validate extracted data against a JSON Schema. Args: data: The data to check (e.g. the "data" from extract_fields). schema: JSON Schema to validate against. Returns: A structured report with passed/failed/missing field lists and a human-readable reason for each failure. Never raises: invalid schemas are reported via the "error" key instead. |
| list_supported_document_typesB | List document types with pre-built schemas (e.g. invoice, resume, purchase_order). |
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 serves a clearly distinct purpose: listing supported schemas, extracting fields from a PDF, and validating extracted data against a schema. There is no overlap or ambiguity in tool responsibilities.
All tool names follow a consistent verb_noun pattern in snake_case: list_supported_document_types, extract_fields, validate_against_schema. The convention is uniform and predictable.
With only 3 tools, the set is at the lower end of the well-scoped range but still appropriate for a focused PDF extraction and validation server. Each tool earns its place without redundancy.
The tool surface covers the core workflow: discovering available schemas, extracting fields, and validating results. Minor gaps exist (e.g., no tool to add custom schemas or handle batch processing), but they are not critical for the primary purpose.