export_training_data

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

No annotations are provided, so the description carries the full burden. It discloses filtering behavior, quality-weighted sampling via PRISM scores, and output format (OpenAI-compatible JSONL). It does not mention any side effects, auth requirements, or rate limits, which is acceptable for an export tool.

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 concise with three purposeful sentences plus an Args block. No unnecessary words; every sentence provides essential information.

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 presence of an output schema, the description adequately explains the output format and content. It covers filtering criteria, quality sampling, and parameter defaults, making it complete for an agent to invoke 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 0% but the description includes an Args section that explains both parameters (output_path and format) with defaults and notes that only 'jsonl' is currently supported, adding value beyond the schema properties.

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 (export), resource (vault beliefs), format (JSONL), and purpose (LLM finetuning). It distinguishes itself from sibling tools that read or query beliefs by specifying the export and training data generation function.

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 provides clear context on when to use this tool: for generating instruction-following pairs from beliefs for fine-tuning. It mentions filtering criteria (confidence >=0.5, non-stale) but does not explicitly exclude other tools or state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.