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Optical Context MCP

Optical Context MCP

by ChrBoebel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Compress Pdf

compress_pdf

Compress OCR-heavy PDFs into dense packed PNG images so agents can work with long visual documents.

Instructions

Read a local PDF, run Mistral OCR, recompose it into dense packed PNG images, and create a retrievable compression job.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "pdf_path": "<pdf_path>"
    },
    "name": "compress_pdf"
  }
}

Input Schema

NameRequiredDescriptionDefault
pdf_pathYes
inline_imagesNo
chars_per_imageNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

C2.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It usefully discloses the OCR dependency (an external Mistral service, implying network/cost implications) and that the output is a retrievable job, but it omits permissions, cost/rate limits, whether existing output is overwritten, and synchronous vs. asynchronous behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence listing the pipeline in execution order, with no filler. It is slightly dense but every clause conveys a distinct step.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three undocumented parameters, no annotations, and no output schema, the description should explain the tuning knobs and how the returned job is consumed. It leaves the caller unable to set inline_images/chars_per_image meaningfully or know what the job handle is.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for all three parameters, and it does not. pdf_path, inline_images, and chars_per_image are never named or explained; 'dense packed PNG images' only loosely gestures at the packing knobs without mapping to any parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific multi-step operation (read PDF, OCR, pack PNGs, create job) rather than restating the name 'compress_pdf'. It implicitly distinguishes itself from the retrieval siblings get_job_manifest and get_packed_images by being the creator of the job, though it never explicitly names them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance, no prerequisites (e.g., a running OCR service, local file access assumptions), and no mention of when to prefer the sibling retrieval tools. Usage is only inferable from the pipeline description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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