Generate a concise professional profile bio for an AI agent. Invoke the paid REST endpoint POST /service/profile-bio; the call costs $1.00 USDC on Base through x402 and returns HTTP 402 before settlement.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | Optional writing tone. | |
| audience | No | Optional intended audience. | |
| agentName | Yes | Name of the AI agent. | |
| capabilities | Yes | Two to six concise capabilities. |
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?
The description discloses that the call costs $1.00 USDC on Base through x402 and that it returns HTTP 402 before settlement. This transparently communicates the payment requirement and potential error response. There are no annotations to rely on, but the description itself covers key behavioral aspects such as cost and failure mode.
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 concise, consisting of two sentences. The first sentence states the core purpose, and the second provides essential operational details (endpoint, cost, and 402 behavior). No unnecessary words or redundant information is included, making it efficient and well-structured.
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 sufficiently covers the tool's purpose, input parameters, and operational constraints (cost, payment, and 402 response). While there is no output schema, the action of 'generating a bio' implies the return value. The absence of error handling details beyond 402 is a minor gap, but overall the description gives enough context for effective use.
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?
All parameters are described in the schema with meaningful descriptions: agentName ('Name of the AI agent.'), capabilities ('Two to six concise capabilities.'), tone ('Optional writing tone.'), and audience ('Optional intended audience.'). The descriptions clarify the purpose and constraints of each field, achieving full coverage of the schema.
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's purpose: 'Generate a concise professional profile bio for an AI agent.' It uses a specific verb ('generate') and identifies the target resource (an AI agent's bio). It is distinct from sibling tools which focus on blockchain data (block, gas price, USDC balance), so there is no ambiguity about what this tool does.
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 implicitly indicates usage—whenever a professional bio for an AI agent is needed—and provides implementation details (REST endpoint, payment method). However, it does not explicitly state alternative scenarios or contrast with sibling tools, though the sibling tools are clearly unrelated. The cost and payment requirement are mentioned, which helps set expectations for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
