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transkribus-mcp-server

by lazyants

Get Validation Data Documents

transkribus_model_get_validation_data_docs
Read-onlyIdempotent

Retrieve documents used as validation data for a model, with filtering by model type and ID. Inspect validation sets for HTR, OCR, or layout analysis tasks.

Instructions

Get the documents used as validation data for a model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesResource ID
typeYesModel type (e.g. htr, la, ocr)
indexNoStart index (0-based)
nValuesNoNumber of results (-1 for all)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv4.0.0
    • addedInput schema / properties / index / default
      Added value: +0
    • changedInput schema / properties / index / description
      Previous value: -"Start index"New value: +"Start index (0-based)"
    • changedInput schema / properties / index / minimum
      Previous value: --9007199254740991New value: +0
    • addedInput schema / properties / nValues / default
      Added value: +-1
    • changedInput schema / properties / nValues / description
      Previous value: -"Number of values"New value: +"Number of results (-1 for all)"
  2. Changed1 schema field changedv3.0.0
    • addedInput schema / properties / type / pattern
      Added value: +"^[^/\\s]+$"
  3. First observedv2.1.1

TDQS

B3.2/5.0
Behavior2/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint. The description adds only the domain context that the returned items are validation-data documents, with no additional behavioral details such as pagination behavior, authentication needs, or return format.

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

Conciseness5/5

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

The description is a single front-loaded sentence with no wasted words. For a simple read-only getter, this is appropriately sized and clearly structured.

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

Completeness4/5

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

The tool is a simple read-only retrieval operation with rich annotations and fully documented parameters. The description states what is returned, and the schema covers pagination inputs, so an agent has enough information to call it correctly despite the absence of an output schema.

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 input schema already documents id, type, index, and nValues. The description adds no parameter-level meaning beyond the schema, making the baseline score of 3 appropriate.

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 states a clear verb-and-resource pair: get the documents used as validation data for a model. It distinguishes the resource from train-data documents and validation-data statistics by the noun phrase itself, though it does not explicitly name sibling alternatives.

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 gives no explicit guidance on when to use this tool versus alternatives such as get_validation_data_stats or get_train_data_docs. Usage is only implied by the resource name.

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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