transkribus_du_recognizeRun Document Understanding recognition on a specified document in a collection. Choose a model and optional page range to extract recognized text and structured data.
Run Document Understanding recognition on a document using a specific model.
| Name | Required | Description | Default |
|---|---|---|---|
| docId | Yes | Document ID | |
| pages | No | Page range (e.g. "1-5") | |
| collId | Yes | Collection ID | |
| modelId | Yes | Model/HTR ID |
Changes observed during successful MCP inspections.
v2.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and destructiveHint=false, implying a non-destructive write operation. The description merely says 'Run recognition', adding no detail about side effects, job creation, or output behavior. It does not exceed what annotations already convey.
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 a single sentence with no verbosity. However, it is overly terse and could include more detail without becoming wasteful. Still, it is efficient.
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?
Given the complexity of the tool (recognition, many sibling tools, no output schema), the description is insufficient. It does not explain what DU recognition entails, what happens after running, or how the result is accessed. This gap makes it hard for an AI to use correctly.
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% with descriptions for all four parameters. The description adds no additional parameter context beyond the schema, so it meets the baseline but does not enrich understanding.
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 action ('Run Document Understanding recognition') and objects (document, model). It identifies the specific type of recognition (DU), but does not distinguish it from sibling recognition tools like transkribus_pylaia_recognize or transkribus_recog_run_htr_citlab, which could confuse an AI agent.
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?
No guidance is provided on when to use this tool versus alternatives. Given many sibling recognition tools (e.g., pylaia_recognize, recog_run_htr_citlab), the description lacks context to help an agent choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.