arctic

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

No annotations are provided, so the description carries the full behavioral burden. It helpfully discloses what data comes back for each mode (age/curve progress/market cap vs volume/liquidity/buys-sells/change), but says nothing about auth, rate limits, freshness, or result size limits beyond the schema's cap.

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?

A single dense sentence front-loads the core action and the mode semantics, then closes with the sibling routing. Every clause earns its place with zero filler.

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 must convey return values, and it does so per mode. For a read-only list tool this is largely complete, though pagination/limit behavior and data freshness are left implicit.

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, but the description adds real meaning by explaining what each enum value ('new' vs 'trending') actually returns, making the mode choice decidable. It adds no further detail on 'limit' beyond the schema's default of 5.

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 (List) and resource (Robinhood Chain tokens) with an explicit enumeration of the two list modes and what each returns. It clearly distinguishes itself from sibling scan_token by describing the find-then-inspect workflow.

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?

Explicitly says when to use it ('Use it to find tokens') and names the follow-up alternative ('then scan_token to read one in depth'). No exclusion conditions are given for when NOT to use it, but the routing guidance is clear.

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?

With no annotations, the description carries the full burden. 'Read' plus 'right now' usefully signals a non-destructive, live-snapshot operation, and 'Every key carries its unit' discloses output formatting. However, it says nothing about freshness/staleness guarantees, rate limits, or failure behavior for non-token addresses.

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?

Front-loaded with the verb and resource, then the field list, then the usage trigger. The field enumeration is long but each item is substantive and tells the agent what data is available. No filler sentences.

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?

No output schema exists, so the description must describe return values — it does so thoroughly by enumerating fields and noting that each key carries its unit. Remaining gaps are behavioral (rate limits, error cases) rather than about what the tool returns.

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 and schema description coverage is 100% — the schema already specifies '0x followed by 40 hex characters.' The description adds no address-specific semantics, so the baseline of 3 applies.

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 a specific verb ('Read') and resource ('Robinhood Chain token') and enumerates the returned fields (name, supply, owner powers, price, liquidity, etc.), so the agent knows exactly what it gets. It does not explicitly contrast itself with robinhood_radar or trace_wallet, but those target different resources (radar and wallet tracing), so confusion risk is low.

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?

It gives an explicit trigger: 'Use it whenever the user mentions a token contract address.' That is a clear when-to-use condition. It stops short of stating when NOT to use it or naming alternatives, so it is not a full 5.

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?

No annotations are provided, so the description carries the full behavioral burden. It implies a safe read via 'Read' and discloses the analytical dimensions returned, but says nothing about wallet-not-found handling, latency, limits, or the shape/format of the response. Adequate but incomplete for a zero-annotation tool.

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 sentences, front-loaded with the core action and followed by the usage trigger. The first sentence is a long run-on list, but every clause describes a distinct returned dimension, so little is wasted.

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 does the job of previewing the return: launch counts, graduations, curve status, and own-curve buy/sell activity. It stops short of describing response structure or edge cases, but is sufficient for an agent to call the tool correctly.

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 both parameters are already documented in the schema, including the 0x+40-hex format and the 1-48 range. The description's 'in the last hours' only loosely echoes the hours parameter and adds no format or default semantics beyond 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 states a specific verb and resource ('Read the wallet behind a Robinhood Chain token') and enumerates what the read covers (launches, graduations, curve status, self-trading). It is clearly distinct in substance from scan_token or robinhood_radar, but it never names or contrasts those siblings explicitly.

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?

It gives a concrete trigger: 'Use it when the user asks who is behind a token, or about a deployer.' That is a clear use context, though it offers no when-not condition and does not name an alternative tool to prefer in adjacent cases.

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