transkribus_model_get_validation_dataRetrieve validation data for a Transkribus model by type and ID to assess performance, with pagination via index and nValues.
Get the validation data for a model by type and ID.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Resource ID | |
| type | Yes | Model type (e.g. htr, la, ocr) | |
| index | No | Start index (0-based) | |
| nValues | No | Number of results (-1 for all) |
Changes observed during successful MCP inspections.
v4.0.0Input schema / properties / index / defaultAdded value: +0Input schema / properties / index / descriptionPrevious value: -"Start index"New value: +"Start index (0-based)"Input schema / properties / index / minimumPrevious value: --9007199254740991New value: +0Input schema / properties / nValues / defaultAdded value: +-1Input schema / properties / nValues / descriptionPrevious value: -"Number of values"New value: +"Number of results (-1 for all)"v3.0.0Input schema / properties / type / patternAdded value: +"^[^/\\s]+$"v2.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), so the description need not restate that. It adds nothing beyond annotations—no note on pagination behavior (index/nValues), result size, or latency—so it contributes no extra behavioral context.
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?
A single, front-loaded sentence with no wasted words. It is efficient, though arguably under-specified rather than a model of completeness.
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 read-only lookup with a fully documented schema and no output schema, the description is minimally adequate: an agent can form a call, but it lacks any differentiation from the several near-identical validation-data siblings.
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 description coverage is 100%, so the schema already documents id, type, index, and nValues. The description only repeats 'type and ID' and adds no format or constraint detail beyond the schema, making the baseline 3 appropriate.
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?
States a specific verb and resource ('Get the validation data for a model') and clarifies the lookup key ('by type and ID'). However, it does not distinguish this from close siblings such as transkribus_model_get_validation_data_stats, transkribus_model_get_validation_data_docs, or transkribus_doc_get_validation_data, leaving the agent to guess which variant applies.
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 gives no when-to-use guidance, no prerequisites, and no exclusions relative to the many sibling tools that also return validation data. It only restates the inputs, so an agent gets no routing help.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.