Fuci

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds which data categories are surfaced but no additional behavioral context such as auth needs, rate limits, or output variability. The concrete price figure is an odd inclusion and may mislead rather than clarify behavior.

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 core purpose is conveyed in a single compact sentence, but the second sentence 'Price: $0.002 USDC (x402)' is unnecessary and likely volatile data that does not belong in a static tool description. This undercuts the conciseness and adds noise.

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?

For a read-only single-token lookup with one optional parameter, an output schema, and strong annotations, the description is mostly complete. It names the key data categories returned. The main blemish is the hard-coded price example, but that does not create a critical gap in the agent's ability 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?

The schema description covers 100% of the single 'token' parameter, including address format and default ($FUCI). The description does not add further parameter semantics beyond mentioning 'one Argus token', so it does not exceed the schema baseline.

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: it reports price, bonding progress, taxes, and recent buys/sells for a single Argus token. It identifies a specific resource and scope ('one Argus token'), though it does not explicitly distinguish itself from siblings like argus_launches. The stray 'Price: $0.002 USDC (x402)' looks like sample data and slightly muddies the purpose.

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 phrase 'for one Argus token' implies this is a single-token lookup rather than a list tool, giving some contextual usage signal. However, it does not explicitly state when to use this tool over sibling tools such as argus_launches or market_search, nor does it mention any exclusions or prerequisites.

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 establish this as a read-only, non-destructive call. The description adds a key behavioral detail beyond annotations: 'Price: $0.001 USDC (x402)' indicates the call incurs a payment. It also specifies the data content (pool, taxes), enriching the behavioral picture. No contradiction with annotations.

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, no filler. The first sentence front-loads the core purpose and returned fields; the second adds pricing. Every word earns its place, and the structure is efficient and scannable.

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?

For a simple one-parameter tool with a full input schema and an output schema, the description covers the essential: what data is returned and the cost implication. It doesn't explain the ordering of results (newest first) but that's already in the parameter schema. The presence of an output schema means return format details are handled elsewhere, so completeness is adequate.

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% – the only parameter 'limit' is fully documented in the schema with range and default. The tool description adds nothing about the parameter beyond what the schema already states. Per rubric, a high coverage baseline is 3, and the description does not 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 identifies the resource: 'Latest token launches on Argus' and the specific data returned (who launched, pool, buy/sell tax). It lacks an explicit verb like 'fetch' or 'list', but the intent is unambiguous. It distinguishes from siblings by naming a unique platform (Argus) and scope (launches), though it doesn't explicitly contrast with alternatives.

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 context for use is implied: if you need recent token launches on Argus, this is the tool. However, there's no explicit guidance on when NOT to use it or which sibling tool might be a better fit for related queries (e.g., argus_bonding for bonding curves). No alternatives are mentioned, so the usage guidance is minimal.

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 readOnly, idempotent, openWorld, non-destructive. The description adds the pay-per-prompt price ($0.04 USDC via x402), a data-purchase cap (0.02 USDC), KYA seller checks, and Claude-written briefs—substantial behavioral context beyond annotations.

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 but front-loaded sentence: pricing model first, then orchestration and output. Parentheticals earn their place by clarifying budget and writer, though the sentence is longer than typical.

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 a paid, open-world orchestration tool with an output schema present, the description supplies the key invocation context: cost, payment protocol, data-buy cap, KYA, and brief output. Missing failure or budget-exhaustion behavior is a minor gap.

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 single prompt parameter is fully documented in the schema as 'Your question about Argus launches / Arc markets.' The description adds no further format, length, or constraint information, so with 100% schema coverage it stays at the baseline.

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 the tool answers a question by orchestrating sibling tools and external data, then writes a brief. The phrase 'the tools above' and 'other sellers in Fuci Market' differentiates it from single-source siblings, though the core verb 'answers' is broad.

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?

Implies use for questions where a synthesized, paid answer is desired and notes the agent will only buy data 'when it helps.' However, it never states when to use this agent instead of the individual Argus/Fuci tools, nor any exclusions.

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 a read-only, idempotent, open-world, non-destructive operation; the description adds meaningful context beyond that, disclosing the paid cost ($0.002 USDC via x402) and that each grade component is explained with its source. A paywalled call and the attributable-score behavior are exactly the traits annotations cannot convey.

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 sentence, front-loaded with the action ('Check another AI agent before you pay, hire or trust it') followed by the value proposition and the price. The source list is long but each item is substantive; nothing reads as 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?

An output schema exists, so return structure need not be described, and the description still covers what the grade reflects, its attributable sourcing, and the payment model. Only the absence of sibling routing (reputation vs risk vs aggregate) is a remaining gap for a tool with nearby alternatives.

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?

With one parameter at 100% schema description coverage, the schema already documents that 'agent' accepts an ERC-8004 id or a wallet address with an example. The description's mention of 'its ERC-8004 identity' loosely corroborates but adds no format or syntax detail beyond the schema, 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?

States a specific verb and resource – check another AI agent and return an A–F trust grade – and enumerates the inputs feeding that grade (ERC-8004 identity, reputation, validations, wallet activity, USDC balance, x402 record). It does not explicitly differentiate itself from the fuci_reputation and fuci_risk siblings, which an agent could confuse with it, leaving only an implicit 'aggregate composite' distinction.

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 a clear use context – 'before you pay, hire or trust it' – which tells the agent when this is the right call. It stops short of naming when not to use it or routing to alternatives like fuci_reputation/fuci_risk, so no exclusions are offered.

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 readOnly, idempotent, open-world, non-destructive, so the safety profile is covered. The description adds genuinely useful context beyond that: the per-prompt price and payment rail ($0.01 USDC via x402), the multi-tool fan-out behind the scenes, and the small/fast model tradeoff that explains brevity.

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 plus a price tag, with the core purpose and the routing rule front-loaded. Nothing is padded, though the embedded product names make it slightly dense to parse.

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?

An output schema exists, so return-value detail is unnecessary, and annotations carry the safety profile. Combined with cost disclosure and a routing rule, this is nearly complete for a one-param pay-per-call tool; only the sibling-name mapping is left fuzzy.

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?

Only one parameter, and the schema documents it at 100% coverage ('A short question about Argus launches / Arc markets'). The description restates the topical scope but adds no format, length, or constraint guidance beyond the schema, so the baseline 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?

States a specific verb (answers) plus resource (Argus launches/bonding/market mood data) and mechanism (agent buys Argus tools, small fast model). It distinguishes itself from the sibling that handles token picks by name.

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 routes simple questions here and names an alternative ('Ask the Fucus Agent') for token picks and trading strategies. The alternative is referred to by a display-style name rather than a tool name, which makes the mapping to fucus_oracle vs. fuci_agent slightly ambiguous.

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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is fully covered. The description adds value with 'Free' (a cost disclosure) and the data scope (identity, reputation summary, validations), which is useful behavioral context. No contradiction with annotations, but the added behavioral depth is modest given the annotation coverage.

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 a single, tightly packed sentence with zero wasted words. The key attribute 'Free.' is front-loaded, and the rest states scope and returned data efficiently. Every element earns its place.

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?

For a single-parameter, read-only tool with a full output schema and complete annotation coverage, the description covers cost, scope, and returned data. The main gap is the absence of usage context (when to use versus fuci_agent or other siblings), but the annotations and output schema relieve the description of explaining safety and return-value structure.

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 single required parameter agentId is already documented as an 'ERC-8004 agentId'. The description confirms the agentId maps to any ERC-8004 agent on Arc, adding slight value but no syntax or format details beyond the schema. Baseline 3 is appropriate when the schema carries the full parameter documentation burden.

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 precisely ('reputation... for any ERC-8004 agent on Arc') and lists what is returned (identity, reputation summary, recent validations), which implies a read/retrieve action. It lacks an explicit verb like 'Get' or 'Retrieve', and it does not name a sibling to differentiate from, though the content (reputation/validations) clearly separates it from bonding, launches, oracle, and market-search siblings.

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?

No guidance on when to choose this tool over alternatives. The closest sibling, fuci_agent, likely overlaps in the agent-information space, yet no differentiation or exclusion is given. The description offers context (free, on-chain, any agent) but nothing about when to reach for this versus another tool.

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?

Beyond the safe read-only annotations, the description discloses the output format (A–F grade), the assessed dimensions, that each factor is source-attributed, and the explicit cost in USDC via x402. This gives an agent useful behavioral context such as expected output shape and pricing without contradicting the annotations.

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: it states the primary output immediately, then adds detail on risk factors and pricing. The two sentences are information-dense without redundancy, though the pricing detail could be considered secondary to the tool's purpose.

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 single parameter, high schema coverage, rich annotations, and presence of an output schema, the description covers the essential decision-making context: what is assessed, in what format, and at what cost. Minor omissions like turnaround time or payment mechanics are not critical given the structured metadata.

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 schema already fully documents the only parameter, target, including its accepted forms (Arc address or DefiLlama slug). The description's mention of tokens and protocols reinforces this but does not add substantially new parameter-level meaning. Baseline of 3 applies due to high schema coverage.

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 a specific resource and outcome: a graded A–F risk report for a token on Arc or a DeFi protocol. It enumerates the risk dimensions analyzed, which makes the tool's purpose concrete. It is not phrased with an explicit action verb, but it is unambiguous and distinguishable from the sibling tools.

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 implies when to use the tool: whenever a user needs a risk rating for an Arc token or DeFi protocol. It does not explicitly compare itself to sibling tools or state when not to use it, but the scope is clear enough for an agent to infer appropriate usage.

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 the tool read-only, idempotent, and non-destructive, so the description does not need to restate these. It adds value by disclosing the cost ($0.0005 USDC) and the output format ('in one sentence'), which are behavioral details not captured elsewhere. It does not describe potential side effects, but given the annotations, this is sufficient.

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 extremely concise, packing purpose, scope, and cost into two short sentences. It is front-loaded with the core function and adds the price as a secondary detail. There is no wasted wording or repetition of schema information.

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 low complexity (one optional parameter) and the presence of an output schema, the description is nearly complete. It covers the tool's purpose, cost, and output format. It could optionally elaborate on what 'sentiment' entails, but that is likely defined elsewhere. Overall, it provides enough information 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?

The input schema fully documents the 'launches' parameter, including its type, default value, and valid range (1–6). The description does not add any additional context about how this parameter influences the reading, so it adds no value beyond the schema. With 100% schema coverage, a baseline score of 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's function: providing a 'tide reading' of net USDC flow and sentiment across the newest Argus launches. This is a specific verb-resource pair that distinguishes it from sibling tools like argus_launches (which likely lists launches) and fuci_risk (risk assessment). The output format 'in one sentence' further clarifies what the agent will receive.

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 does not provide any explicit guidance on when to use this tool versus its siblings. It does not mention alternatives or conditions that would make this tool the preferred choice. The only implicit signal is the phrase 'newest Argus launches', which hints at a specific scope, but no exclusions or comparisons are given.

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 readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond that: the search is free, checked live, and covers every qualifying API. This adds cost and freshness information not present in the annotations.

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 a single efficient sentence that front-loads the most important non-obvious facts: free, live, and comprehensive coverage. Every element earns its place, and there is no wasted wording.

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?

For a relatively simple search tool, the description combined with rich annotations and a complete output schema covers the essential context: what is searched, what is returned, cost, freshness, and safety profile. Nothing critical is missing for an agent 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?

Schema description coverage is 100%, so both 'query' and 'limit' are already documented in the input schema. The tool description does not add parameter-level detail, but it does not need to because the schema carries the full semantic burden.

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: 'Search every paid x402 API that accepts USDC on Arc.' It also lists the returned fields, making the tool's purpose concrete and easily distinguishable from the unrelated sibling tools.

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 this tool: whenever the agent needs to find paid x402 APIs on Arc that accept USDC. It provides clear context but does not explicitly name alternative tools or exclusion conditions, though no sibling tool appears to compete with this search function.

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