Lizeur
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CACHE_PATH | No | Path where processed documents will be cached | |
| MISTRAL_API_KEY | Yes | Your Mistral AI API key for OCR processing |
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
| Name | Description |
|---|---|
| read_pdfB | Read a PDF document and return the complete OCRResponse as a dictionary. Returns the full OCR response including all pages, not just the first page. The response includes pages with markdown content, bounding boxes, and other OCR metadata. |
| read_pdf_textA | Read a PDF document and return only the markdown text content from all pages. |
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 2 tools
The two tools have overlapping purposes: both read PDF documents and extract text content. While read_pdf returns full OCR metadata and read_pdf_text returns only markdown text, an agent might struggle to choose between them when only text is needed, as both could technically serve that purpose. The descriptions help clarify the difference, but the core functionality is very similar.
Both tools follow a consistent verb_noun naming pattern with snake_case: read_pdf and read_pdf_text. The naming is clear and predictable, making it easy for agents to understand the action (read) and target (pdf or pdf_text). There are no deviations or mixed conventions in this small set.
With only 2 tools, this server feels under-scoped for a PDF processing domain. While the tools cover reading and extracting text, there are obvious gaps like creating, editing, or converting PDFs. A typical PDF server would benefit from more operations, making this count too low for comprehensive functionality.
The tool surface is severely incomplete for PDF processing. It only includes reading operations (two variants of the same basic function) and lacks essential capabilities such as creating PDFs, merging/splitting files, converting formats, or editing content. This will likely cause agent failures when more complex tasks are required.