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

With no annotations, the description carries the burden of behavioral disclosure. It adds meaningful operational context: 5-minute sampling cadence, proprietary data source, and a $0.02 USDC cost per call via x402 on Solana mainnet. These details go beyond the schema and help the agent anticipate freshness, cost, and uniqueness, even though error behavior and auth requirements are not mentioned.

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 compact and front-loaded with the core metric definition, followed by data provenance and cost. Every sentence contributes distinct information—what, how sampled, and cost. The first sentence is somewhat long and comma-heavy, but it avoids fluff and remains readable.

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?

Given there is no output schema and no annotations, the description does a solid job of telling the agent what to expect: the metric, the units, the aggregation split, and the cost. It could have specified the exact response shape or the effect of the hours parameter, but the schema covers hours and the description gives enough to invoke correctly. The paid-call warning is an important contextual detail that is well handled.

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 both parameters (symbol and hours) are already documented with examples and valid ranges. The description adds little parameter-specific meaning, though '24h stats' corroborates the default lookback. Baseline of 3 applies because the schema handles the param semantics and the description does not need to compensate.

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 defines what the tool returns: peg deviation in bps for a tokenized US equity, comparing on-chain DEX price to underlying last real trade, with 24h stats split by market hours. It distinguishes the tool from generic price tools by highlighting the unique collector data ('this tape exists nowhere else'). The only minor gap is the lack of an explicit verb like 'Returns' or 'Lists', though the tool name and context make the action obvious.

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 gives no explicit guidance about when to use this tool versus related siblings such as get_peg_universe or get_peg_sessions. It implies relevance to tokenized equities and peg analysis, but does not state exclusions, prerequisites, or conditions that would route an agent here over an alternative. This leaves usage decisions mostly to inference.

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