alphapipeline

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

Annotations already mark read-only/non-destructive, and the description adds substantial behavioral context: the reference price source and fallback, the flat-gas-cost assumption, the returned metrics, and the USDC payment. 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?

Three sentences are tightly packed: use case, output, exclusions, and cost. Front-loaded with the most important instruction; no 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?

For a read-only analysis tool with full schema coverage and an output schema, the description provides all necessary selection and invocation context, including payment and scope. It even disambiguates against sibling tools.

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?

Input schema covers all 6 params with descriptions (100% coverage), so baseline 3 applies. The description adds context about the analysis (gross/net spreads, assumed gas) but does not add parameter-level detail 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?

States a specific use case (pre-trade arbitrage check), defines the exact comparison (global reference price vs DEX pool price), and explicitly excludes unrelated uses (liquidity depth, contract security), which differentiates it from 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?

Explicitly says when to use ('before executing a cross-venue arbitrage trade') and when not to use ('Do not use for DEX-only liquidity depth checks or contract security'), pointing to alternative categories among siblings. It also mentions the cost and network context.

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=true and destructiveHint=false. The description adds useful behavioral context beyond that: it is a zero-dependency static calendar with no upstream failure risk, and it is paid in USDC on Base, which informs cost and reliability expectations.

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 primary use case and exclusions. It is a bit promotional with phrases like '100% uptime' and 'paid in USDC on Base,' but these add decision-relevant reliability and cost context without excessive length.

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 tool has zero parameters, read-only annotations, and an output schema, the description covers all essential aspects: purpose, specific macro events, year scope, exclusions, and reliability/cost characteristics. Nothing critical is missing for an agent to select and 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?

There are no input parameters, and the description explicitly confirms this with 'No input parameters.' Since there is nothing for the schema or description to elaborate on, the baseline of 4 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 states a specific verb and resource: it provides days remaining until major US macro events (FOMC, CPI, NFP). It clearly identifies the tool as a static 2026 macro calendar, which differentiates it from the finance, derivatives, and security 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 explicitly says when to use it: when planning trading schedules around major US macroeconomic volatility. It also explicitly states when not to use it: for real-time market price data or economic forecast consensus figures, which is clear exclusion guidance.

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=true and destructiveHint=false, and the description adds meaningful behavior beyond that: it performs a pure calculation on top of funding_rate data, has no extra upstream call, flags the funding-paying side, and is paid in USDC on Base. These details are not visible elsewhere.

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?

Three dense, front-loaded sentences cover the main action, exclusions, alternatives, and payment context with zero filler. Every sentence 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 two-parameter calculation tool with an output schema, the description covers purpose, usage boundaries, computation behavior, and payment context. Nothing needed for correct invocation is missing.

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 the schema already fully documents both parameters. The description contextualizes assumed_round_trip_cost_pct as the cost recouped by funding income, but it does not add new parameter-level details beyond what the schema already provides.

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: 'evaluate a spot+perpetual carry trade' and enumerates three concrete outputs: annualized APR, funding side, and days to recoup cost. It also explicitly distinguishes itself from the sibling tool derivatives.funding_rate, making its purpose clear without needing to open schemas.

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?

Contains explicit routing guidance: 'Do not use for the raw current funding rate alone (use derivatives.funding_rate) or for spot price data.' It also clarifies this is a pure calculation with no extra upstream call, which tells the agent exactly when this tool is appropriate versus alternatives.

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 mark the tool as read-only, and the description adds useful behavioral context beyond that: Bybit is the primary data source with Binance as fallback, and the tool is paid in USDC on Base. This gives agents practical knowledge about data provenance and cost 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: use cases first, then data sources, then exclusions, then payment note. Every sentence contributes meaningful information with no redundancy or 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?

For a single-parameter read-only tool with an output schema, the description is complete. It covers when to use it, what data it aggregates, important exclusions, and payment context. Nothing essential for correct invocation or selection is missing.

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 fully documents the single 'symbol' parameter with examples (BTC, ETH, BTCUSDT), so the description does not need to add much. It implicitly connects the symbol to perpetual funding rates, but the schema already provides sufficient 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 aggregates perpetual futures funding rates from Bybit and Binance, with a specific use context of sentiment analysis and hedging timing. It also distinguishes itself from spot and security-related tools by explicit exclusions, making its purpose unambiguous.

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 strong when-to-use guidance: analyzing funding rates, long/short sentiment, and hedging before settlement periods. It also gives clear when-not-to-use exclusions for spot volume and token security checks, though it does not explicitly name which sibling tools should be used in those excluded cases.

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 only declare readOnlyHint and destructiveHint, so the description carries the behavioral burden and delivers substantively: the tool is paid in USDC on Base, risk_flags come from fixed numeric thresholds only, and Hyperliquid's public API has no leaderboard endpoint so discovery/ranking is impossible. 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?

The description is front-loaded with the core purpose in the first sentence, and every subsequent sentence adds distinct value (scope limit, threshold behavior, exclusions, payment). It is slightly verbose at about 110 words for a one-parameter tool, but the density of caveats justifies the length.

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 an output schema present, annotations covering safety, and a single well-documented parameter, the description covers usage scope, exclusions, and payment currency/chain. The only noticeable gap is the exact payment amount, which is not essential for correct selection or invocation but would be useful for cost-sensitive agents.

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 only parameter (address) at 100% coverage, so the baseline is 3. The description reinforces that the address must be user-supplied and Hyperliquid/EVM, but adds no new parameter-level semantics beyond what the schema already states.

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 opens with a specific verb and resource ('audit a Hyperliquid wallet address'), enumerates exactly what is returned (every open perp position with side, size, leverage, unrealized PnL, liquidation price, distance-to-liquidation), and explicitly contrasts with smart-money discovery/ranking. This makes the tool's scope unambiguous even among siblings that could sound similar.

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 explicitly states the condition for use ('a Hyperliquid wallet address you already know') and gives clear when-not guidance: not for smart-money discovery (with a rationale), not for spot price data, and not for any exchange other than Hyperliquid. This is explicit usage scoping that prevents common misuses without needing a named sibling alternative.

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 and destructiveHint. The description adds no additional side-effect information beyond the implied non-mutating 'calculate' action, so it meets the baseline for annotated tools without extra context.

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 concise, with three sentences that pack key information: purpose, use case, exclusions, and output highlights. No fluff or 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?

For a read-only analytics tool with 4 parameters and an output schema, the description provides all necessary context: what it calculates, when to use it, what to avoid, and a hint at the output structure (slippage_tiers). Nothing essential is missing.

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 descriptions cover all parameters at 100%. The tool description reiterates the optionality between pool_address and token_address and the auto-selection logic, but this is already present in the schema, so it adds minimal new semantic value.

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 calculates DEX price slippage, pool liquidity depth, and optimal routing, with a specific verb and resource. It distinguishes itself from sibling tools by focusing on pre-swap analysis and explicitly excluding CEX and contract risk.

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 explicitly says when to use the tool (before executing an on-chain token swap) and when not to use it (CEX orderbooks, contract risk analysis). This provides unambiguous guidance on applicability.

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 readOnlyHint=true already covering the safety profile, the description adds meaningful behavioral context: real-time nature, reverse-premium and surge alerts, and the payment requirement ('Paid in USDC on Base'). These are traits an agent cannot infer from annotations or schema alone.

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?

Three efficient sentences each serve a distinct purpose: when to use, what it reports, and cost basis. The content is front-loaded and free of filler or repetition of structured schema details.

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 one optional parameter, read-only annotations, and an output schema, the description supplies the remaining needed context: scope, exclusions, alert behavior, and payment. It could name an alternative sibling for excluded cases, but that is a nice-to-have rather than essential for invoking 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 single symbol parameter, including examples and a default value, so description coverage is 100%. The tool description adds no extra parameter-level semantics beyond the schema, matching 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?

The description opens with a specific use case—'evaluating Korean exchange price premiums, the kimchi premium, Upbit price gaps vs Binance/OKX'—and names the resource and behavior: real-time Upbit vs global price spread with reverse-premium and surge alerts. It also differentiates from siblings by explicitly excluding general USD spot prices and DEX swaps.

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 clear when-to-use trigger list and explicit exclusions ('Do not use for general USD spot prices or on-chain DEX swaps'). However, it does not name a sibling alternative tool for those excluded cases, so it falls just short of full guidance.

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 mark the tool as readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds valuable behavioral context: it takes a live snapshot rather than historical liquidity, resolves token_id from a market_slug automatically, and notes payment in USDC on Base. 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?

The description is compact but information-dense: the first sentence delivers the core purpose, and later sentences add routing, constraint, and payment context. Every clause earns its place without unnecessary repetition or 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 the annotations, full schema coverage, and presence of an output schema, the description gives everything an agent needs to select and invoke the tool correctly: purpose, exact input resolution options, the key exclusion, and cost context. No critical gap remains.

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 parameters are already documented. The description adds relationships between parameters, explaining that either token_id or market_slug (+ outcome) is used to resolve the market, and emphasizing that position_size_shares is the size to evaluate for fillability. This goes beyond the schema's individual descriptions.

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: it walks a single Polymarket outcome's live order book to determine how much of a position size can be filled, at what average price, and with what price impact. It clearly distinguishes itself from prediction.neg_risk_arbitrage by explicitly excluding multi-outcome basket arbitrage detection.

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 explicitly says when to use the tool: when you need live fill capacity and price impact for a single outcome. It also tells the agent not to use it for multi-outcome basket arbitrage and directs to prediction.neg_risk_arbitrage instead, plus it clarifies exact-slug resolution with no fuzzy search.

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 read-only and non-destructive behavior, and the description adds substantial context beyond that: it returns executable basket capacity rather than just top-of-book, accounts for costs, and notes settlement in USDC on Base. It also explains the risk-free-edge mechanic without overpromising, saying 'near risk-free'.

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 every sentence earns its place: it states what the tool does, why the edge exists, what the returned capacity means, which markets are excluded, and where settlement occurs. It is front-loaded with the core purpose and contains no 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 the output schema exists and annotations cover the read-only safety profile, the description is complete for an agent to select and invoke the tool correctly. It covers scope, exclusions, the core arbitrage logic, output semantics (capacity size, not a mirage), cost assumptions, and settlement environment.

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%, and all four parameters have clear descriptions in the input schema. The tool description adds high-level context ('after costs', 'liquidity-bottleneck size') but no parameter-level detail beyond what the schema already provides, so the 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 states a specific verb and resource: it detects basket arbitrage in Polymarket neg-risk events, and explains the underlying pricing condition (a full YES basket settling to $1). It clearly differentiates the tool from generic prediction-market tools by restricting it to mutually-exclusive multi-outcome events and explicitly excluding binary markets and Kalshi.

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 strong when-to-use and when-not-to-use guidance: use it for Polymarket neg-risk events, do not use it for binary Yes/No markets or on Kalshi. However, it does not name a specific sibling alternative to route the agent to for those excluded cases, so it falls just short of full alternative guidance.

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=true, but the description adds valuable behavioral context: it reuses GoPlus data without an extra upstream call, outputs only GoPlus's lock/burn numbers with no qualitative judgment, and is paid in USDC on Base. These details go beyond the structured fields and enrich the agent's understanding of side effects and constraints.

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 core purpose in the first sentence, followed by relevant technical details, exclusions, and cost. Every sentence contributes unique information with no fluff or repetition, making it efficient and easy to scan.

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 an output schema present and read-only annotations, the description covers the essential context: purpose, input semantics, data source, output category, exclusions, and payment. It is fully self-contained for an agent to select and invoke the tool correctly, especially given the sibling differentiation.

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?

Input schema coverage is 100%, with both chain_id and contract_address clearly described. The description does not add parameter-specific meaning beyond the schema, which is acceptable given the high schema coverage; 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 clearly states the tool's function: checking whether a token's liquidity pool is locked, burned, or held by a single wallet, and categorizes it as a rug-pull signal. It explicitly distinguishes itself from sibling security.token_risk by noting that token_risk does not cover this check, making the specific resource and verb unmistakable.

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?

Provides explicit when-to-use guidance ('before trusting its liquidity') and exclusion criteria ('Do not use for honeypot or tax checks (use security.token_risk) or market price data'). Names the alternative tool and the conditions that route to it, leaving no ambiguity for the agent.

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=true and destructiveHint=false. The description adds critical behavioral context: parallel execution, deduped union behavior, no composite score/letter grade, fields copied unchanged, cost advantage, and payment currency. No contradictions 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?

The description is organized with the main purpose front-loaded, followed by behavior, alternatives, and cost. Every sentence adds value, though it is slightly long for a tool description. Still, no filler exists.

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 combined tool with output schema available, the description covers what the tool does, why it differs from alternatives, when not to use it, and key behavioral guarantees. Nothing critical is missing for an agent to select and invoke 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%, with both parameters documented (chain_id with an example, contract_address format). The description adds slight context about 'same GoPlus data' but doesn't need to duplicate schema details. Baseline 3 is appropriate given full 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 states a specific purpose: a single-call combined security check running two named tools in parallel and returning both results plus a deduped union. It explicitly names the underlying tools and clearly differentiates itself from siblings like unlocks.dump_risk.

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?

Provides explicit when-to-use guidance ('single-call combined security check') and when-not-to-use guidance with named alternatives ('Do not use for token unlock/vesting risk (use unlocks.dump_risk) or if you only need one of the two checks'). This is exemplary routing.

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=true and destructiveHint=false. The description adds valuable context: it names the backing sources (GoPlus/Honeypot.is), the specific risk categories checked, and the cost model (paid in USDC on Base). This goes beyond the annotations and gives the agent useful operational expectations.

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 three punchy sentences, with the most important usage instruction front-loaded. Every sentence earns its place: what it does, when to use it, what not to use it for, and the cost. There is no redundancy or 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 the simple two-parameter schema, existing output schema, and safe read-only annotations, the description covers all essential operational context: purpose, trigger condition, exclusions, source, and payment. Nothing critical is missing for correct invocation.

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 both parameters are individually documented. The description adds no additional parameter-level meaning beyond what the schema already provides, so the 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 job: verify ERC-20 contracts for honeypot, rug-pull, malicious taxes, and mintability risks before swaps. It specifies a concrete verb and resource. However, it does not explicitly distinguish itself from the sibling security tools such as security.contract_health_audit or security.token_diagnostic, leaving some differentiation to inference.

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 explicit when-to-use guidance: before executing any on-chain swap. It also includes clear when-not-to-use exclusions: not for market price discovery or liquidity depth. It stops short of naming alternative tools for those exclusions, so it doesn't fully meet the bar for a 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?

Annotations already mark the tool as read-only and non-destructive, and the description adds genuinely useful behavioral context beyond that: it converts pages to Markdown, filters boilerplate, and crucially discloses that the tool is paid in USDC on Base. The payment disclosure is important for an agent deciding whether invocation has cost implications. No contradiction with annotations exists.

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: three sentences cover use case, output, exclusions, and payment. It is front-loaded with the primary trigger condition. Minor redundancy exists between 'without wasting context tokens on... boilerplate' and 'optimized for LLM context windows', but no sentence 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?

The description provides enough context for an agent to select and invoke the tool correctly: purpose, target URLs, exclusions, parameter format, and cost. The presence of an output schema means return-value documentation is handled elsewhere. It does not discuss rate limits or edge cases like JavaScript-heavy or authenticated pages, but this is not a critical gap for a simple single-URL converter.

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 only one parameter and 100% schema description coverage, the schema already documents the url parameter thoroughly ('Full http(s) URL of the webpage to convert to Markdown'). The tool description restates the general conversion behavior but adds little new parameter-level detail beyond the schema. A baseline score of 3 is appropriate because the schema carries the 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 uses specific verbs ('parse', 'converts') and clearly identifies the resource: any URL becomes clean Markdown optimized for LLM context windows. The explicit mention of stripping ads, scripts, navigation, and boilerplate distinguishes it clearly from generic fetchers or URL tools. There is no ambiguity about what the tool accomplishes.

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 explicitly states when to use the tool: for parsing clean webpage article content or preparing a URL for summarization. It also gives clear negative guidance by warning against raw API endpoints and binary files such as PDFs and images. These when/when-not signals are sufficient, especially since none of the sibling tools overlap with this functionality.

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 mark the tool as read-only and non-destructive, so the description only needs to add context beyond that. It adds valuable behavioral details: the tool is free by default, intended as an onboarding aid to verify connectivity, latency, and output schema validity before paid pipelines. This is useful context about cost and intended usage that annotations do not 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?

The description is front-loaded with the primary purpose and then provides usage guidance and free-tier context. It is reasonably compact, though 'FREE ONBOARDING TOOL' and 'Zero payment required by default' are slightly redundant. Overall, every sentence contributes to agent decision-making.

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 zero-parameter, read-only tool with an output schema, the description covers what the tool does, when to use it, what not to use it for, and the free/payment context. An agent has all necessary information to decide whether to call it and what to expect. The presence of an output schema means return-value details are already structured.

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 is an empty object with zero parameters, so the baseline is 4. The description does not need to document parameters, and it avoids introducing false expectations about arguments. It focuses appropriately on tool behavior and use cases rather than fabricated parameter details.

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's function: evaluating token unlock schedules, vesting cliffs, and VC/team dump pressure relative to circulating supply. It goes beyond a vague statement by specifying the resource (token unlock schedules) and the risk metric (supply overhang). It also distinguishes itself from intra-day-focused tools through its mid-to-long term positioning context.

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 explicitly states when to use the tool ('before taking mid-to-long term positions') and provides a clear when-not case ('Do not use for intra-day slippage or real-time transaction simulation'). It stops short of naming specific alternative sibling tools, but the context is strong enough for an agent to route appropriately.

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