postGenerate Content Billing per call: Credits: metered.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | JSON request body. Example: {"messages":[{"role":"user","content":"There are ten birds in a tree. A hunter shoots one. How many are left in the tree?"}]} |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It mentions that calls are billed with metered credits, which is useful context, but it says nothing about request/response behavior, side effects, or whether generation is synchronous or asynchronous. The billing note is the only behavior disclosed beyond generating content.
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 text is short but not effectively structured: it opens by repeating the title and appends a billing note without context. It is under-specified rather than concise, leaving out critical information an agent would need to use the tool correctly.
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 one-parameter tool with a nested body, no output schema, and no annotations, the description should at least clarify request semantics and return behavior. It only provides a generic generate-content phrase and a billing note, forcing agents to infer from the schema example. This is not complete enough for reliable tool selection and invocation.
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?
Schema description coverage is 100%, so the schema already documents the 'body' parameter with an example. The tool description adds no additional semantic detail about the parameter, so it neither improves nor harms understanding. Baseline 3 is appropriate.
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 repeats the title 'Generate Content' without specifying a precise operation or resource. It does not explain what kind of content is generated, how the request is processed, or how this differs from any other tool. The schema's example hints at a chat-completion API, but the description itself is essentially a tautology.
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?
There are no sibling tools or conditions for use. The description offers no guidance on when to use this tool versus alternatives, nor any context about appropriate use cases or prerequisites. Only billing information is given, which is not usage guidance.
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.
With only one tool, there is no possibility of confusion or overlap. The sole tool 'post' is trivially distinct from any other.
A single tool follows a consistent pattern by definition. The name 'post' is a verb and aligns with a simple action-oriented style.
The server has only one tool, which is too few for a content generation service. The apparent scope likely requires more than a single generic endpoint to be useful.
The tool set is severely limited: it only offers a one-shot generation action with no way to retrieve history, manage configuration, or handle related workflows. This leaves significant gaps for agents needing broader GPT capabilities.