create_job

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains that training produces a custom model using QLoRA (code_repo) or synthetic error-resolution pairs (sera_code_repo), and mentions that the model can be stored in cloud storage or exported to S3. It does not detail potential side effects, rate limits, or the exact output format, but the description is fairly transparent about the training process and output destinations.

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 well-structured with sections for agents, base models, and workflow, but it is quite lengthy, particularly the exhaustive list of supported base models which could be omitted since users can use list_supported_models. While the structure is clear, conciseness is slightly compromised by the inclusion of redundant details.

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 fine-tuning an LLM, the description is fairly complete. It explains the tool's purpose, agents, supported base models, and workflow. It references sibling tools for cost estimation and monitoring. However, it does not describe the return value or response structure (e.g., job ID or status), which would be helpful. Since there is no output schema, a brief note on what the tool returns would improve completeness.

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?

The input schema has 100% description coverage, so the schema already documents all parameters. The description adds value by providing extra context for the agent and quality_tier parameters, including best-use recommendations ('Best for: code autocomplete, inline suggestions...') and agent-specific behavior. This goes beyond the schema's enum descriptions, justifying a score above the baseline of 3.

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 tool's purpose: 'Fine-tune an LLM on a GitHub repository using Tuning Engines.' It specifies the action (fine-tune), the resource (LLM), and the data source (GitHub repo). The description distinguishes the tool from siblings like estimate_job and job_status by outlining the typical workflow, and it further differentiates the two agents (code_repo and sera_code_repo) with explicit use cases.

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 explicit usage guidelines, including a 'TYPICAL WORKFLOW' section that advises users to run estimate_job first for cost estimation, then create_job, and finally job_status to monitor progress. It also clarifies when to use each agent and the quality_tier parameter for the SIERA agent, offering clear decision criteria.

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