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transkribus-mcp-server

by lazyants

Run PyLaia Recognition

transkribus_pylaia_recognize

Recognize handwritten text in Transkribus documents using PyLaia HTR with a specific model. Configure recognition options including page range, language model, and segmentation.

Instructions

Run PyLaia HTR recognition on a document using a specific model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collIdYesCollection ID
modelIdYesModel/HTR ID
docIdYesDocument ID
pagesNoPage range (e.g. "1-5")
languageModelNoLanguage model to use
printedModelIdNoPrinted text model ID
printedLanguageModelNoLanguage model for printed text
doLinePolygonSimplificationNoSimplify line polygons (default true)
keepOriginalLinePolygonsNoKeep original line polygons (default false)
writeKwsIndexNoWrite KWS index (default false)
nBestNoNumber of best results (default 1)
useExistingLinePolygonsNoUse existing line polygons (default false)
doStructuresNoStructure analysis mode
doWordSegNoPerform word segmentation
creditsNoCredits parameter
allowConcurrentExecutionNoAllow concurrent execution (default false)
doNotDeleteWorkDirNoDo not delete working directory
writeLineConfScoreNoWrite line confidence scores
writeWordConfScoresNoWrite word confidence scores
batchSizeNoBatch size (default 10)
clearLinesNoClear existing lines before recognition
b2pBackendNoBaseline to polygon backend (default "Legacy")
Behavior2/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, but the description adds no behavioral context. It does not mention that the tool modifies the document, how long it takes, or any side effects. With annotations providing minimal clarity, the description fails to disclose essential behavioral traits.

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?

The description is a single concise sentence, appropriate for a clear purpose. However, it could be slightly more informative without becoming verbose.

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?

Given the tool's complexity (22 parameters) and the absence of an output schema, the description is too minimal. It does not explain what the output is, how results are provided (e.g., job creation), or any post-processing needed. The description is incomplete for effective agent use.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond the schema, justifying a baseline score of 3.

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

Purpose5/5

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

The description clearly states the action ('Run') and the resource ('PyLaia HTR recognition on a document using a specific model'). It distinguishes this tool from sibling recognition tools (e.g., transkribus_recog_run_htr_citlab) by explicitly naming 'PyLaia' as the HTR engine.

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?

The description provides no guidance on when to use this tool versus alternatives, such as other HTR recognition tools or OCR tools. It lacks prerequisites, exclusions, or context for selection.

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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