qm_data_feed

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

With no annotations, the description carries the full burden and does so richly: pricing ($0.01 USDC on Base via x402), the live-402-challenge caveat, the two-phase flow, the guarantee that the server never signs/holds funds/touches keys, and resale rights. These are exactly the behavioral traits an agent needs to invoke safely.

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?

Information-dense and front-loaded, leading with purpose and limits before payment mechanics. The payment paragraph is long but every clause is load-bearing for correct invocation; slightly verbose rather than padded.

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?

With no output schema and no annotations, the description covers the return (clean JSON, up to 20 items, HTML stripped, returned in the tool result), the payment lifecycle, and the safety model. Nothing material for correct invocation is missing.

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 coverage is 100%, so the baseline is 3; the description goes beyond the schema by explaining the temporal relationship between the two parameters (omit 'payment' first to get requirements, then re-call with the signed payload) and reiterating the pass-through safety semantics.

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?

States a specific verb and resource ('RSS/Atom feed parsed to clean JSON') and names concrete use cases (news monitoring, content ingestion, change detection) that distinguish it from data siblings like qm_data_wiki or qm_data_httpcheck. An agent can identify the tool's job immediately.

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?

Gives clear usage context via the three named scenarios and, crucially, an explicit two-phase invocation procedure (call without 'payment', then re-call with it). It does not name alternative sibling tools or state when NOT to use this versus another data tool, so it stops short of a 5.

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