Plover Solana swap quotes

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the 'Read-only; never asks for a key' line largely restates them. However, the description adds genuinely new behavior: price-impact is hidden for tokens under 24 h old, and the result is a signing link rather than an executed swap. It doesn't cover quote expiry or slippage, but the added context is substantial.

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?

Two tight sentences: the first defines the resource and its return fields, the second explains the signing hand-off and the safety posture. No filler, and the most important information is front-loaded.

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, the description carries the burden of describing the return value, and it does so well (amounts, route, price impact, sign link). It doesn't mention quote freshness/expiry or fee/slippage handling, which an agent might need for a live quote.

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 all three parameters (from, to, amount, including UI-unit semantics) are already documented. The description mentions return fields rather than clarifying parameters, so it adds nothing beyond the schema — baseline 3.

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 ('Live Jupiter quote for a Solana swap') and enumerates exactly what comes back: out amount, min out, route, price impact. This is plainly distinguishable from the only sibling, token_facts.

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 workflow is implicit but clear: get a quote, then the human reviews and signs via the returned plover.markets link. It says 'Read-only; never asks for a key,' which sets expectations. It never explicitly says when to prefer this over token_facts or what to do if the quote expires, so no exclusions are stated.

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

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

Annotations already declare this is a read-only, open-world lookup, so the safety profile is covered. The description adds valuable behavioral context beyond that: ATH is sourced from the live DEX pool rather than the dead Pump.fun bonding curve, which is a non-obvious data-provenance detail an agent would otherwise get wrong. It does not discuss failure behavior for invalid mints, so it is strong but not exhaustive.

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?

One dense sentence that front-loads the token subject, then enumerates what is returned, then closes with the scope exclusion. Every clause earns its place and there is no boilerplate.

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?

Because there is no output schema, the description must carry the return-value burden, and it does by enumerating the computed fields. It is complete for the happy path; the remaining gap is silent behavior on bad input or unavailable data, which is minor but real for an open-world external-data tool.

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?

There is a single parameter with 100% schema description coverage ('Token mint address'), so the schema already carries the parameter semantics. The description adds no additional syntax, format, or validation guidance for the mint argument, which is the correct baseline-3 outcome when the schema does the heavy lifting.

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 names the resource (a Solana token) and enumerates the exact facts returned: market cap, ATH, age, graduation status, liquidity, and authorities. It is specific enough that an agent knows precisely what data it gets, though it does not explicitly differentiate itself from the sibling get_quote beyond the obvious subject difference.

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?

'Facts only, no safety verdict' is an explicit exclusion that tells the agent not to use this tool for a safety/rug assessment and implies it is a pure data lookup. There is no named alternative for the excluded use case, so it falls short of a full when/when-not/alternatives statement.

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