service_info
FREE: describe this service — model, price, network, and how to call glm53_chat.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
GLM 5.3 Inference (x402 paid)
TDQS
A4.2/5.0
Behavior3/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
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TDQS
A4.2/5.0
Disambiguation5/5
glm53_chat is the actual model inference endpoint, while service_info provides static service metadata. There is no overlap in purpose or behavior.
Naming Consistency5/5
Both names use clear lowercase snake_case and are noun-like descriptors; while not verb_noun, the convention is consistent and predictable.
Tool Count4/5
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.
Completeness5/5
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.