FabTally Chain

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

With no annotations, the description carries the transparency burden, and it does well by clearly stating 'PAID', 'Read-only, descriptive', and 'ON-CHAIN (public RPC)'. It also discloses the two-step behavior (challenge vs. result) and that payment occurs via x402/USDC on Base, giving the agent a solid understanding of side effects and prerequisites.

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 well-structured: it front-loads the payment warning, then states the tool's purpose, expected outputs, and payment flow. Each sentence provides necessary information without redundancy, achieving high information density in a few 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?

Given the lack of an output schema, the description compensates by enumerating key return fields (expiry date, days-left, grace period, release date, owner) and detailing the payment interaction. It does not cover edge cases like invalid names, but overall it is sufficiently complete for a simple lookup tool with clear scope.

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%, and the schema already explains both parameters in detail, including the x_payment challenge behavior. The description's mention of 'Args: name' and the payment flow adds little beyond what the schema provides, so the baseline 3 is appropriate.

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 resolves ENS .eth or Basename .base.eth registration expiry on-chain, using specific verbs like 'Resolve' and listing exact outputs. It distinguishes itself from sibling tools (gas, oracle_health, token_unlocks) by focusing on domain-name expiry, which is unrelated to those.

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 provides clear context on when to use the tool (when expiry info is needed) and explains the payment workflow explicitly: without payment it returns a 402 challenge, and passing x_payment settles the call. It does not name alternatives, but the sibling domains are clearly different, so no explicit exclusion is necessary.

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 provided, the description discloses essential behavior: it is read-only, requires payment, and returns a challenge when x_payment is omitted. It also explains the optional below_gwei threshold behavior. This covers the main behavioral aspects an agent needs to know.

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, front-loaded with the paid status, and efficiently communicates the core function, payment flow, and optional threshold. Every sentence contributes useful information without redundancy.

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 the absence of an output schema, the description adequately explains the return values (base fee, priority, max fee, optional boolean) and the payment challenge/settle mechanism. It provides enough context for an agent to understand the tool's full behavior.

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?

The input schema provides complete descriptions for all three parameters (chain, x_payment, below_gwei), achieving 100% coverage. The description reinforces the chain options and optionality but does not add substantially new semantic meaning 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 clearly states the tool's purpose: returning current gas data (base fee, priority fee, max fee) for a supported chain via public RPC. It also specifies the output format (gwei) and an optional threshold check, making it distinct from sibling tools (ens_expiry, oracle_health, token_unlocks).

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 provides clear context on when to use the tool: for reading current gas prices. It explains the payment flow (x402 challenge vs. x_payment to settle), but does not explicitly mention alternatives or when not to use it, though the sibling tools are clearly different in scope.

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 burden. It discloses the paid $0.003 cost, the x402 challenge flow, the read-only nature, and the specific factors computed. It could mention rate limits or exact response format, but the main behavioral traits are transparent.

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 dense but well-organized, front-loading the payment requirement and then listing the key outputs. Each sentence adds value—no fluff or repetition—making it appropriately concise for a complex tool.

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?

Despite lacking an output schema, the description enumerates all the returned factors (last-update age, latest answer, monotonicity, stale flag, deviation), explains the payment gate, and gives an example feed address. This is comprehensive for an agent to understand what the tool does and how to invoke it 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?

The input schema already covers all parameters with descriptions (100% coverage), so baseline is 3. The description adds extra meaning by explaining that 'reference' enables deviation calculation and that 'x_payment' is required to bypass the 402 challenge, going beyond the schema's raw definitions.

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 identifies the tool as a Chainlink price-feed health checker via public RPC, listing specific outputs like last-update age, latest answer, round monotonicity, stale flag, and deviation. This specific verb-resource pairing distinguishes it from sibling tools (ens_expiry, gas, token_unlocks), which cover different domains.

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 clearly implies when to use the tool (checking Chainlink feed health) and explicitly notes it is read-only and descriptive. It does not provide direct exclusions or mention alternatives, but the provided context is sufficient for an agent to know when it applies.

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 provided, the description carries the full burden. It discloses the payment requirement ($0.003 via x402/USDC on Base), the challenge/response flow (x_payment to settle), read-only nature, and the behavior of clearly stating when a token has no public unlock schedule. This is rich behavioral disclosure.

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 front-loaded with the paid cost and structured into three sentences. It is somewhat dense but every phrase adds value, covering purpose, output, payment, and argument hints without 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?

Given no output schema, the description explains what the tool returns (date, amount, % supply, human description) and covers edge cases (no public schedule) and payment mechanics. This is complete enough for a simple read-only lookup 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?

Schema coverage is 100%, so baseline is 3. The description largely repeats schema information (token can be symbol/protocol/address, chain only for address lookup, x_payment omitted to get challenge). It adds minor context about USDC on Base but no new parameter 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?

The description clearly states the tool returns next token vesting/unlock event(s) with date, amount, and % of max supply from DefiLlama's public emissions dataset. This distinguishes it from sibling tools (ens_expiry, gas, oracle_health), which cover different domains.

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 provides clear context: it is a read-only, descriptive unlock-event lookup and explicitly says it is NOT a price signal. It does not name alternative tools, but the sibling tools are topically distinct, so usage context is evident.

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