GLM 5.3 Inference (x402 paid)

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently reveals the cost ($0.005 USDC per call) and the tool's capabilities, but omits key behaviors such as return format, authentication requirements, rate limits, or any side effects beyond the monetary charge. It states it is a chat completion, implying it returns a completion, yet does not explicitly describe the response.

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 exceptionally concise: two sentences with zero fluff. The prominent cost warning is front-loaded, followed immediately by the tool's purpose and capabilities. Every word contributes to understanding.

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?

For a chat completion tool with three well-documented parameters, the description is mostly adequate. However, it lacks any mention of the return value structure (e.g., you receive a completion message) and does not cover potential error conditions or retry behavior. Given the tool is paid, noting that the charge is per call is helpful, but the absence of output semantics leaves a minor gap.

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 provides complete descriptions for all three parameters (messages, max_tokens, temperature) at 100% coverage. The description adds no extra meaning beyond what the schema already offers; the 'OpenAI-compatible messages' phrase is echoed in the schema. Therefore, the description does not elevate parameter understanding.

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 performs GLM 5.3 chat completion, specifying capabilities (reasoning, tool-calls, code) and the message format (OpenAI-compatible). It unambiguously differentiates from the sole sibling, service_info, which focuses on service information rather than chat.

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 indicates it is a paid tool, implying a cost consideration, but does not explicitly state when to use this tool over alternatives (e.g., free options or when not to use it). It does not mention the sibling service_info as a fallback or provide contextual guidance on selection.

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

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

With no annotations, the description carries the burden. It calls the service 'FREE' and says 'describe', implying an informational read-only operation, but it doesn't explicitly state safety, side effects, or return format. This is minimal but not misleading.

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?

A single sentence that is front-loaded with 'FREE' and immediately explains what is covered. No wasted words, and it directly communicates the tool's value.

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?

For an informational tool with no parameters and no output schema, the description covers the essential content: what it describes and how it relates to the sibling. It could mention that it returns textual information, but this is not critical.

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 tool has zero parameters, so the baseline is 4. There is no schema coverage to worry about, and the description correctly omits parameter details since none exist.

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 states a clear verb 'describe' and a specific resource 'this service', listing the covered aspects (model, price, network) and the relationship to glm53_chat. It distinguishes itself from the sibling tool, which is likely a chat function, making its purpose unambiguous.

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 implies usage by pointing to 'how to call glm53_chat', suggesting this tool should be used to obtain instructions before invoking the chat tool. It doesn't explicitly state when not to use it, but the context of having a single sibling is clear enough.

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