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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly: hard input/output limits, non-streaming behavior, no hidden system prompt, response contents, and exact payment terms are all disclosed. The mention of a signed receipt and a fixed USDC payment is particularly valuable for an agent deciding whether to call this 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 dense but every clause earns its place: purpose, prerequisite, limits, streaming behavior, response contents, and pricing are all included without filler. The semicolon-separated structure makes the constraints easy to scan.

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?

The description is largely complete for a small inference tool given no output schema and no annotations: it explains preparation, hard limits, output characteristics, and payment mechanics. It could add a bit more about the exact response payload or failure behavior, but the essential call-time guidance is present.

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 schema gives no parameter descriptions, so the description must compensate. It usefully adds hard input size and output token limits and clarifies the no-hidden-system-prompt stance, but it does not explicitly explain the relationship between prompt, system, and max_tokens beyond what the schema already implies.

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 identifies AgentLLM Micro as an OpenAI-compatible text inference tool for concrete tasks (classification, extraction, routing, short summaries), so an agent can recognize both the resource and the job. It also distinguishes itself from the sibling preparation tool by explicitly saying to run prepare_agentllm_micro first.

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 gives explicit when-to-use context through the listed task types and provides a critical prerequisite: run prepare_agentllm_micro with identical arguments first. It does not discuss exclusions or alternative LLM tools, but no other LLM sibling exists, so the guidance is sufficiently clear.

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