ocr_imageRun optical character recognition on an image (png, jpg, webp, bmp) and return the recognized text with a confidence score. Supports 100+ languages via the language parameter (ISO 639-2 codes like 'eng', 'deu', 'fra', 'spa').
| Name | Required | Description | Default |
|---|---|---|---|
| file_url | No | Public http(s) URL of the file | |
| language | No | Tesseract language code, default 'eng' | |
| file_base64 | No | Base64-encoded file contents (data-URI prefix allowed) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the output (text and confidence score), supported image formats, and language support, which are useful behavioral details. However, it does not mention constraints like file size, authentication, error behavior, or that exactly one of file_url/file_base64 must be provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action and output, and includes necessary details (formats, languages) without redundancy. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple OCR tool with no output schema and decent annotations in the schema itself, the description covers key aspects: input formats, language options, and return value. It could be complete if it mentioned the input source constraint (file_url vs file_base64) that is inferred from the schema, but that is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are already described. The description adds value by giving explicit examples for the language parameter (ISO 639-2 codes like 'eng', 'deu'), but does not add additional meaning for file_url or file_base64 beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'Run optical character recognition on an image' with supported formats and returns 'recognized text with a confidence score.' This distinguishes it from siblings like extract_tables, parse_invoice, pdf_to_markdown, and render_pdf, which target different document processing tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for image OCR but does not explicitly mention when to use this tool over alternatives or provide exclusions. It lacks guidance such as 'for PDFs, use pdf_to_markdown' or any comparison to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct document processing task: table extraction, image OCR, invoice parsing, PDF-to-markdown conversion, and markdown-to-PDF rendering. Even though parse_invoice can process images, its structured output clearly differentiates it from ocr_image.
Four tools follow a clear verb_noun pattern (extract_tables, ocr_image, parse_invoice, render_pdf). However, pdf_to_markdown breaks the pattern by using a source_to_target format instead of a verb-first name, creating a minor inconsistency.
Five tools is a well-scoped count for a document processing server. Each tool serves a distinct purpose without redundancy, covering input (PDF, image), processing (extraction, OCR, parsing), and output (markdown, PDF).
The toolkit covers a coherent document workflow: extract text/tables from PDFs, OCR images, parse invoices, convert to markdown, and generate PDFs. A minor gap is the lack of direct scanned-PDF handling (requires manual page-to-image conversion), but this is workable around.