transkribus_recog_train_table
Start table recognition model training for a collection.
This record as markdown: /tools/io-github-lazyants-transkribus/transkribus-recog-train-table.md
What transkribus_recog_train_table does on Transkribus
AI agents invoke transkribus_recog_train_table to trigger actions in Transkribus. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why transkribus_recog_train_table is rated High
The tool executes/triggers a machine learning training operation on the Transkribus backend. This is an Execute category action because it initiates an external process with side effects (resource consumption, model generation, job queueing) that cannot be easily reversed without additional cleanup tools.
From the tool's definition Tool description states 'Start table recognition model training' — the word 'Start' indicates triggering an external operation (model training job) whose effects depend on arguments (which collection, which parameters).
Attacks that exploit this kind of access
The rule that runs transkribus_recog_train_table safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Transkribus, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For transkribus_recog_train_table, this is the rule to start with:
transkribus_recog_train_table stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Transkribus, apply this rule, and every transkribus_recog_train_table call is checked against it from then on.
Questions about transkribus_recog_train_table
Start table recognition model training for a collection. It is categorised as a Execute tool in the Transkribus MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Transkribus MCP server in PolicyLayer and add a rule for transkribus_recog_train_table: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Transkribus. Nothing to install.
transkribus_recog_train_table is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the transkribus_recog_train_table rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for transkribus_recog_train_table. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
transkribus_recog_train_table is provided by the Transkribus MCP server (@lazyants/transkribus-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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