submit_and_pay_job_from_file

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

No annotations provided, but the description discloses key behaviors: the server reads the file, the content avoids LLM output tokens, and max file size matches inline limit. This adds valuable context beyond the bare schema.

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 (2-3 sentences), front-loaded with the relationship to the sibling, and each sentence adds distinct value: usage guidance, path details, and size limit.

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 no output schema and 6 parameters, the description covers the key differentiator but omits payment or result details. It references the sibling for completeness, but standalone it leaves gaps in understanding the full job submission and payment flow.

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?

With only 17% schema description coverage, the description adds meaning only for input_path (absolute/relative, server directory). Other 5 parameters (provider_npub, capability, etc.) are left entirely undocumented in both schema and description, relying on inference from the sibling tool.

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 it is the same as submit_and_pay_job but with file input, specifying the verb 'submit and pay' and resource 'job', and distinguishes itself from the sibling tool by the input mechanism.

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?

Explicitly advises when to use: 'when the input is large... and the LLM only needs to forward it', and notes that file content never enters model output tokens, providing clear context versus the inline sibling.

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