PAID ($0.005 USDC): GLM 5.3 chat completion. Reasoning, tool-calls, code, OpenAI-compatible messages.
| Name | Required | Description | Default |
|---|---|---|---|
| messages | Yes | OpenAI-compatible chat messages. | |
| max_tokens | No | Max completion tokens. | |
| temperature | No | Sampling temperature. |
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.
Add one secure layer between your agents and this server.
glm53_chat is the actual model inference endpoint, while service_info provides static service metadata. There is no overlap in purpose or behavior.
Both names use clear lowercase snake_case and are noun-like descriptors; while not verb_noun, the convention is consistent and predictable.
Two tools is minimal but appropriate for a narrowly scoped paid inference service; while slightly below the typical 3-15 range, each tool serves a clear purpose.
For a single-model paid inference server, the chat tool plus service information covers the full needed surface; there are no obvious gaps or dead ends.