Single tool 'run_pipeline' has critical definition gaps. Input schema present with two string parameters (pipeline_name, input_data) but lacks descriptions for both parameters, violating the requirement that every parameter must have a description. Tool description is minimal (11 words, 59 chars) and lacks context on when to use it, what it returns, or prerequisites. No output schema documented despite returning a dict. No error handling guidance. No parameter constraints (enums, ranges, formats). Naming is acceptable (verb_noun form) but the tool's purpose is vague, 'run_pipeline' doesn't clarify whether it executes, schedules, validates, or previews. No indication of side effects or idempotency.

• Add descriptions to both parameters: 'pipeline_name' should specify format (e.g., 'The name of the pipeline to execute (alphanumeric, 1-100 chars)') and 'input_data' should clarify expected format (JSON string, CSV, plain text, etc.) and size limits.
• Expand tool description to 100-200 chars explaining: what the tool does, when to call it (e.g., 'Use this to trigger a pre-configured visual pipeline'), what it returns (status, output summary), and any prerequisites (pipeline must exist).
• Document the output schema formally: specify that the response is an object with fields 'status' (string: 'success'|'error'), 'processed_by' (string: pipeline name), 'output' (string: result summary). Include field descriptions.
• Add error handling guidance: describe what errors can occur (pipeline not found, invalid input_data format, timeout) and what the LLM should do (retry, call list_pipelines, ask user for clarification).
• Consider adding optional parameters for timeout, retry_count, or dry_run mode to support more sophisticated agent workflows and reduce accidental side effects.