Optical Context MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MISTRAL_API_KEY | Yes | API key for Mistral OCR (required for compress_pdf) | |
| OPTICAL_CONTEXT_ADAPTIVE_MODEL_PATH | No | Override path to the bundled adaptive sizing model checkpoint | |
| OPTICAL_CONTEXT_DISABLE_ADAPTIVE_SIZING | No | Set to '1' to force legacy fixed sizing instead of adaptive sizing |
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": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| compress_pdfC | Read a local PDF, run Mistral OCR, recompose it into dense packed PNG images, and create a retrievable compression job. |
| get_job_manifestC | Load the saved manifest for a previously compressed PDF job. |
| get_packed_imagesC | Return one or more previously generated packed PNG images for a stored job. |
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 targets a distinct operation: compression, manifest retrieval, and image retrieval. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern with lowercase and underscores (compress_pdf, get_job_manifest, get_packed_images).
Three tools are appropriate for the narrow domain of PDF compression and retrieval. The count feels slightly minimal but sufficient for the core workflow.
The set covers the essential operations (compress, get metadata, get output) but lacks optional features like job deletion or status polling, which are minor gaps.