Agent Tollgate

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

No annotations, so the description must carry the load, and it does disclose a key behavioral trait: 0.001 USDC per call via x402, a paid tool. It does not describe the 402 challenge flow or failure modes, but the payment proof mechanics are documented in the schema.

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 purpose, with the pricing note appended. No wasted text.

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, so the description must imply return values, and it does so by stating the tool classifies accounts as wallet, contract, or token mint. The payment flow and response structure are only partially covered, leaving 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?

Schema coverage is 100%, so address, chain, and paymentProof are already fully documented. The description only implies that address format detection is automatic, adding marginal meaning beyond the schema; 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?

States specific verbs and resources: detects address format (EVM/Solana) and checks on-chain whether it is a wallet, contract, or token mint. This clearly distinguishes it from siblings like wallet_balance or token_audit, which fetch data rather than classify account type.

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 case — preventing agents from sending funds to the wrong kind of account — which implies when to call it before transfers. It does not name alternative tools or state exclusions, but the context is explicit.

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 and does reasonably well: it discloses that validation is offline, which chain families are covered, and a per-call cost of 0.001 USDC via x402. It does not describe the response shape (e.g. detected chain plus validity flag) or behavior for malformed/unsupported addresses, which for a validator is the main behavioral question.

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, both front-loaded and dense: capability with the exact algorithm coverage first, cost/mechanism second. 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?

With no annotations and no output schema, the description should say what comes back (detected chain, boolean validity, error cases) and how it differs from address_check. As written an agent knows what goes in and what it costs, but not what it gets out.

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 the paymentProof field already documents the 402 retry flow in detail, so the schema does the heavy lifting. The description adds no format hints for the address parameter beyond naming supported encodings, so 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+resource ('Detects the chain of an address and validates it') and enumerates exactly which encodings are supported (Base58Check, bech32/bech32m, EIP-55, Solana Base58). It does not distinguish itself from the sibling address_check, which an agent would have to disambiguate on its own.

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?

Pricing and the x402 flow are mentioned, and the paymentProof schema field spells out the two-step call pattern. But there is no guidance on when to use this versus address_check, and no statement of what to do on an unsupported chain.

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 load and does disclose two meaningful traits: it abstains when the context lacks the answer (no hallucination) and it is metered at 0.01 USDC per call via x402. It does not elaborate on failure modes such as an unpayable or invalid proof, but the pricing/abstention disclosure is well beyond what structured fields provide.

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 with the core behavior front-loaded and the billing note trailing; nothing is redundant or padded.

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 three-parameter tool with a fully documented schema and no output schema, the description supplies the critical non-schema facts: grounding scope and cost. The only remaining gap is a hint at the shape of the returned answer, which is largely inferable.

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% — context, question, and the paymentProof 402 flow are all documented in the schema, including the two-step pay-then-retry pattern. The description adds no parameter-level meaning, 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?

Names a specific capability — grounded reading comprehension that answers a question strictly from supplied context — which cleanly separates it from the general-purpose ai_chat sibling. It stops short of naming any sibling explicitly, so the differentiation is implied rather than stated.

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 'from the supplied context only' implies you should reach for this when you have source text plus a question, rather than for open-ended generation. However, there is no explicit when-not guidance or pointer to alternatives like ai_chat or ai_summarize.

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 still discloses the output token cap and per-call price via x402, which is useful behavioral context. It omits return format, error behavior, and payment flow details; those are partly covered by the schema but not the description.

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?

Four short sentences, front-loaded with the core operation and followed by use cases, a hard limit, and price. No filler; each sentence adds useful 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?

The description covers core operation, use cases, token cap, and price, and the schema documents paymentProof mechanics. The main remaining gap is return-value behavior, since no output schema exists, but the tool is otherwise sufficiently specified for a one-shot completion.

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 75%, so the schema already documents prompt, system, maxTokens, and paymentProof. The description reinforces optional system prompt and max output tokens but adds no new syntax or constraints 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 names a specific operation, one-shot chat completion, and notes the optional system prompt. It does not differentiate itself from sibling AI tools such as ai_rewrite or ai_summarize, so it is clear but not fully sibling-aware.

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 lists suitable use cases (drafting, rewriting, brainstorming, simple reasoning), which implies when to use it. However, it gives no exclusions and does not direct the agent to specialized siblings like ai_rewrite or ai_summarize, leaving route selection 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?

With no annotations, the description carries the full burden and does well: it discloses cost (0.005 USDC per call via x402), the single-label selection behavior, and the shape of the response (label choice, confidence, one-line reason). It stops short of stating rate limits, failure modes, or the 402 retry flow, which only appears in the paymentProof schema field.

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 capability, then the output shape, then pricing. Every clause carries information an agent needs and nothing is repeated from the schema.

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?

There is no output schema, so the description correctly compensates by describing the return values (best label, confidence, one-line reason). Combined with a 100%-covered input schema, an agent has enough to call it, with only pricing/retry mechanics left implicit across description and schema.

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 text, labels, and paymentProof are already fully documented in the schema, including the two-call 402 payment handshake. The description only adds the semantic that 'labels' is a candidate set from which one winner is chosen, which is the baseline 3 when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific technique (zero-shot classification) and the exact operation (picks the best of the provided labels for the text), plus what comes back (confidence and a one-line reason). It is clearly distinct from fixed-label siblings like ai_sentiment, though it never names a sibling 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?

'Zero-shot' plus 'best of the provided labels' implies when to reach for it (custom label sets, single-label selection rather than multi-label tagging), but there is no explicit when-to-use framing and no comparison against ai_sentiment, ai_extract, or ai_answer.

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 behavioral burden and does disclose two costly-to-discover facts: the 0.002 USDC per-call price and the x402 payment protocol, plus the hard 50-text batch cap. It does not say what happens beyond 50 texts (error vs. truncation) or whether billing is per call or per text, which leaves a real gap.

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: output shape and limits first, then price and payment protocol. Every clause carries information an agent needs before calling.

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?

Dimension, batch limit, cost, payment mechanics and use cases are all covered without an output schema, so an agent can call it correctly. Missing only edge behavior (over-limit handling, per-text vs. per-call billing) for a paid 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 description coverage is 100%, so both parameters are already documented, including the array form of 'text' and the full 402 payment retry flow. The description adds only the 50-item ceiling, which the schema leaves implicit — enough to be useful, but 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 concrete verb and resource ('Returns 768-dimensional embedding vectors'), plus the batch ceiling (up to 50 texts) and the intended jobs (semantic search, clustering, deduplication). That makes it easy to separate from near-siblings like ai_classify or ai_sentiment, which return labels or scores rather than vectors.

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 three named use cases give an agent clear trigger conditions for choosing this over ai_chat or ai_summarize. It stops short of naming alternatives or stating when not to use it (e.g., single-text classification belongs to ai_classify).

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It explicitly outlines the operational cost ('0.01 USDC per call via x402') and describes a crucial multi-step dependency involving the `paymentProof` parameter. These are highly valuable behavioral traits for an autonomous agent.

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 definition is highly concise, packing function, output format, financial constraints, and usage guidelines into a single, dense statement. It front-loads the most important operational details (cost and primary function).

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 a robust definition for an AI extraction tool, covering not just the 'what' (extraction) but also the 'how' (JSON output) and the operational 'how' (payment flow). It addresses the most common gaps left by the absence of a structured output schema.

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?

While the input schema coverage is 100%, the description adds vital semantic context. It clarifies that the `fields` parameter acts as an exclusion filter (empty = full entity extraction) and explains the conditional nature of the `paymentProof` requirement on the first call.

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 that the tool 'Pulls' requested fields or named entities out of free text, defining it as an extraction tool that returns valid JSON. It specifically lists the types of entities extracted (names, dates, amounts, etc.), which helps differentiate it from simple summarization or classification 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?

It provides clear guidance on how the `fields` parameter should be used: if fields are provided, only those specific entities are extracted, while leaving it empty triggers the extraction of general named entities. This dual functionality serves as a practical guideline for tool invocation.

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 load and does well: it discloses the return format (base64 JPEG data URL) and the critical commercial term (0.03 USDC per call via x402), which an agent must know before invoking. It omits latency, resolution/size limits, and failure 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?

Three compact sentences with the capability, return type, and price front-loaded in that order. Nothing superfluous and nothing buried.

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-capability generation tool with no output schema, the description covers what comes back and what it costs, which are the two things an agent most needs. The remaining gap is guidance on the `steps` parameter and image dimensions.

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 67% and the description adds no meaning beyond the schema — notably it never explains `steps` (1-8, default 4), which is the one undocumented parameter and affects cost/quality trade-offs. The paymentProof mechanics are fully spelled out in the schema, not the description.

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+resource (text-to-image) and even names the underlying model, FLUX.1 schnell, so an agent knows exactly what capability it is invoking. It is clearly distinct from the text-oriented ai_* siblings, though it does not explicitly say so.

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?

There is no statement of when to use this versus alternatives, no prerequisites beyond the implicit cost note, and no exclusions. The domain is obvious from the name, but the description provides no actual routing 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?

Crucial external behaviors are disclosed here: the cost per call (0.005 USDC) and the necessary payment procedure (via x402 and the use of paymentProof). This critical operational context is not captured in the input schema.

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 tool is concisely defined in two sentences. The structure effectively prioritizes the functional description first, followed by the necessary operational/financial constraints. It is efficient, though the second sentence is quite dense.

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 covers all necessary operational details: what it does, what parameters it accepts (including valid tone options), and the mandatory billing/payment procedure. It leaves no critical gap for agent planning.

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 description adds significant semantic value beyond the 100% schema coverage. It explicitly lists the valid options for the 'tone' parameter and details the complex, multi-step flow required for the 'paymentProof' parameter, which is vital for correct tool invocation.

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: rewriting text for grammar/spelling or tone modification. While clear, it does not explicitly differentiate the rewriting function from other AI siblings like ai_summarize or ai_translate, relying solely on the verb 'Rewrites'.

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 usage boundaries based on intent (correct grammar vs. match tone). However, it does not offer explicit 'when to use' vs. 'when not to use' logic against other related AI tools.

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, and it discloses the most consequential behavior an agent must know: this is a paid call at 0.001 USDC via x402, with the two-step 402-then-pay flow documented in the schema. It adds that results are 'current' rather than forecast, but says nothing about failure modes, rate limits, or what happens when no location argument is supplied.

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, zero filler: the returned metrics first, the cost second. Nothing is repeated from the schema and nothing could be cut without losing 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?

There is no output schema, and the description compensates by enumerating the metrics returned, plus the per-call price. The remaining gap is that all four parameters are optional, so the description should state what happens if neither a place nor coordinates is given.

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 lat/lon/place/paymentProof are all documented structurally and the payment flow is spelled out in paymentProof's own description. The description adds only the mapping of 'place name or coordinates' onto those fields, 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?

Names a specific resource (current US AQI, European AQI, PM2.5, PM10, ozone, NO2, UV index) and the two accepted locators (place name or coordinates), so an agent knows exactly what comes back. It doesn't explicitly contrast with the sibling 'weather' tool, which is the nearest potential source of confusion, so it stops short of a 5.

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?

'By place name or coordinates' implies the selection between geocoded and raw-coordinate lookups, but there is no explicit when-to-use/when-not guidance and no mention of the 'weather' sibling as an alternative for forecast data. Usage is inferable but not spelled out.

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 at all, the description still carries the key behavioral facts an agent needs before calling: a per-call cost (0.003 USDC via x402), a batch limit of 50, and an English-only input constraint. It stops short of describing failure behavior or the exact response shape, which keeps it out of the top band.

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 pair that front-loads what the tool does, then appends the two operational constraints an agent must know. No filler and nothing buried.

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?

There is no output schema, so the description does the minimum needed by naming the return values (positive/negative label plus probability), and the payment mechanics are covered by the schema. Only the absence of non-English or error-case behavior keeps it below complete.

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 `text` and `paymentProof` are already fully documented, including the 402 retry flow. The description's mention of batching to 50 merely restates the schema, so 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?

States a specific verb and resource ('Classifies English text as positive or negative') plus the output form (probability score) and a batch ceiling. It is clearly distinguishable from generic siblings like ai_classify and ai_extract, though it never names the closest alternative 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?

Usage is only implied: the English-only restriction and the 50-text batch cap hint at appropriate inputs, but there is no statement of when to prefer this over ai_classify or how to handle non-English content. The payment retry flow lives entirely in the schema, not the description.

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 exist, so the description carries the full burden. It does disclose two things structured fields don't: the response shape (summary plus bullets) and the economic model (0.01 USDC per call via x402), which is material for an agent deciding whether to call. It omits failure behavior (what happens past 20,000 characters, whether failed calls are charged) and any latency or rate characteristics.

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 short sentences, front-loaded with what the tool produces and followed by the cost. No filler. The second sentence is a distinct and useful constraint rather than padding, though it could have been merged into a single tighter line.

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 4-parameter tool with full schema coverage and no output schema, the description covers purpose, output artifacts, and pricing — enough to call it correctly. The gaps around oversized input handling and payment failure modes are minor given the schema spells out the 402 handshake.

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%, including the non-obvious two-step paymentProof flow, so the schema already does the parameter work. The description only restates length and language at a high level and adds no format, unit, or enumeration detail beyond it. 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?

States a specific verb and resource ("LLM summary of long text") and names the return artifacts (concise summary plus bullet key points), which separates it from generic text siblings like text_stats. It does not explicitly distinguish itself from ai_rewrite, ai_extract, or ai_answer, so an agent must infer the boundary from the word "summary."

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?

"Long text" implies the input regime where this tool is appropriate, but there is no explicit when-to-use/when-not-to-use guidance and no sibling is named as an alternative. With ai_rewrite, ai_extract, and ai_answer in the same family, routing depends on inference rather than stated rules.

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 behavioral burden. It does add real value by disclosing the pricing model ('0.005 USDC per call via x402'), which the schema does not state — though the mechanics live in the paymentProof field description. It omits other behavior: rate limits, what happens on unsupported languages, or the shape of the translated result.

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 short sentences, zero filler, and the core capability is front-loaded ahead of the optional-source note and the price. Every sentence contributes a distinct fact.

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 four-parameter, fully documented schema with no output schema, this covers capability, optionality, and cost. The only meaningful gap is that it never says the return is the translated text (and in what form), which is predictable but unstated.

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 all four parameters, including the x402 payment flow, are already fully documented. The description only reiterates that the source language is optional, adding no syntax, format, or default detail beyond the schema — 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+resource ('Machine translation between about 100 languages') and names the underlying model (m2m100), which clearly separates it from sibling AI tools like ai_rewrite or ai_summarize. It lacks an explicit 'not for X' clause but the operation is 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?

Usage is only implied: the description mentions source language is optional, hinting at auto-detection, and the cost line hints at the payment flow. There is no explicit when-to-use/when-not guidance or comparison to the many other ai_* siblings, which an agent must infer.

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?

Since no annotations are provided, the description carries the full burden and excels by disclosing critical transactional behavior: it explicitly states the cost (0.001 USDC per call) and the necessary payment proof via x402.

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 and front-loaded, but it is slightly wordy given the complexity (covering function, output, cost, and payment method) in a single statement.

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 that the tool is a transactional calculation with dependencies (payment proof), the description is highly complete. It covers the calculation scope, the cost, and the payment workflow, leaving no major functional unknowns for the agent.

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?

Though schema coverage is 75%, the description provides high semantic value by detailing the complex payment flow required for the `paymentProof` parameter. This is vital context that goes beyond the schema definition.

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 core function: converting between simple APR and compounded APY. It also mentions the delivery of equivalent daily and monthly rates, making it highly specific.

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?

While the function is clear, it does not explicitly state when to use this tool relative to similar financial sibling tools (e.g., `compound_interest`). Usage guidance relies solely on understanding the conversion relationship.

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 usefully discloses the paid model (0.002 USDC per call via x402) and the return payload, but says nothing about rate limits, error behavior, or empty-result handling; the 402 handshake is largely covered by the paymentProof schema description.

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, zero waste: capability and return shape first, pricing second. Nothing repeated or padded.

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 stateless search tool with no annotations or output schema, the description covers what it does, what comes back, and the cost model. It lacks only details like result pagination or max result behavior, which the schema partially implies via limit.

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 75% and the only undocumented parameter is limit, whose 1-30 bounds are still in the schema. The description clarifies the sort semantics (relevance vs submission date), which is a modest addition; baseline 3 applies given the high 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?

States a specific verb (searches) and resource (arXiv), plus the two sort modes and the exact fields returned. No sibling tool overlaps arXiv literature search, so the agent can select it unambiguously.

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?

Usage is implied by the resource name – an agent looking for academic papers will pick it – but there is no explicit when-to-use/when-not guidance or comparison to alternatives like wikipedia or web_extract.

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 behavioral burden, and it does disclose the notable trait that each call costs 0.001 USDC via x402. However, it does not state that payment is required before a successful result, nor what happens on an unpaid call, leaving the schema's paymentProof field to carry the flow.

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 short sentences, zero filler, with the core encode/decode operation front-loaded ahead of the pricing note. Nothing needs trimming.

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 pure-function tool with no output schema and no annotations, the description covers input and direction but not the return shape: whether a decode returns both hex and UTF-8 simultaneously, and what happens for byte sequences that are not valid UTF-8. That ambiguity matters for a codec 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 description coverage is 100%, so the schema already defines mode, input, value, and paymentProof; baseline is 3. The description's mention of 'hex or UTF-8 text' and 'decodes to hex and UTF-8' loosely echoes the input/value params but adds nothing the schema does not already say.

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 pair (encodes/decodes) and the exact resource (Base58) plus the source/target encodings (hex, UTF-8), so the operation is unambiguous. It does not name or contrast with the adjacent codec siblings (base64, hex_codec, url_codec), which keeps it just short of a 5.

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?

Usage is only implied by describing both directions; there is no explicit guidance on when to pick this over base64/hex_codec, or on which mode to choose for a given input. An agent can infer mode from the sentence, but nothing steers it away from wrong 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?

Since no annotations are provided, the description must carry the burden of behavioral disclosure. It successfully informs the user of the high operational cost (0.001 USDC) and the complex payment procedure required for the `paymentProof` parameter.

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 highly concise, but it mixes the functional description of the utility with the complex operational cost and payment procedure in a single, slightly dense sentence, impacting optimal structure.

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 utility wrapper, it provides adequate operational context, especially regarding the financial and payment requirements. However, it lacks detail on error responses or expected output formats upon successful decoding.

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 description provides critical semantic and procedural context far beyond the schema. Specifically, it mandates the payment workflow (omit on first call, pay, then call again) and specifies the maximum character limit for the `text` input.

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 core function: encoding or decoding UTF-8 text using base64 or base64url. Since no other encoding/decoding tools are listed, the distinction is implicitly clear.

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 zero guidance on when to use this tool versus other similar utilities (e.g., the sibling tool `hash` or `json_format`). It only describes what it *does*.

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, so the description carries the full burden — and it does disclose the per-call price (0.002 USDC via x402), which is valuable and non-obvious. Beyond that it says nothing about read-only nature, rate limits, result ordering, or the two-step 402 retry pattern (that lives only in the paymentProof schema field). Adequate but thin for a paid endpoint.

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 capability and closed by the cost — zero wasted words.

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 usefully enumerates the returned data fields, and the pricing and payment mechanism are covered across description plus schema. The remaining gap is query semantics — whether ISBN/author/year searches are supported — which affects 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 coverage is 67%. The 'q' parameter is documented only as 'Query', so the description's mention of titles/authors/ISBNs is the only hint about what can be searched. 'limit' is undocumented in both places; paymentProof is fully explained in the schema, so the description need not repeat it.

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+resource ('Open Library search') and enumerates the fields it searches/returns (titles, authors, publish year, editions, ISBNs, covers). It is clearly distinguishable from sibling search tools like arxiv_search, wikipedia, or hn_search, though it never names them.

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?

Usage is only implied by the word 'search'; there is no statement of when to prefer this over wikipedia or arxiv_search, and no precondition guidance. An agent can infer the domain (books on Open Library), but nothing is explicit.

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 behavioral disclosure burden. It usefully states the cost (0.001 USDC per call) and x402 payment model, but does not describe read-only behavior, output format, or other operational traits such as determinism or rounding.

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 two tightly written sentences with the core calculation front-loaded and the payment cost appended without clutter. Every phrase contributes useful 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?

For a calculation tool with a fully described schema and no output schema, the description adequately conveys that the result is a break-even price. It could mention return denomination or rounding, but the essential behavior is clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all five parameters are already documented in the schema. The description reinforces that side, entry, fees, and slippage are relevant, but adds no parameter syntax or meaning beyond what the schema 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?

The description clearly states that the tool computes the break-even price after entry/exit fees and slippage for long or short positions. The purpose is specific and immediately understandable, though it does not explicitly differentiate itself from related calculation siblings such as position_pnl or liquidation_price.

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 no explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites beyond the payment note. Usage is only implied by the tool name and the calculation it describes.

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 does add real behavioral context: the tool is priced at 0.001 USDC per call via x402, which warns the agent that calls cost money and that a payment handshake occurs. It does not state read-only/no-side-effect behavior, data freshness, or caching, which for a zero-annotation definition is a meaningful gap.

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 compact sentences, front-loaded with what is returned and at which confirmation targets, then the source and the price. Every clause carries information an agent needs; nothing is padding.

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?

There is no output schema, and the description compensates by enumerating the returned data (three fee tiers, mempool size, block height), so the agent knows what comes back. Combined with a fully documented single parameter, the definition is nearly self-sufficient, missing only freshness/latency and read-only reassurance.

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 exists and its schema description is 100% covered, including the full 402 challenge flow (omit first, pay, retry with proof). The description adds nothing about paymentProof itself, so the baseline 3 applies with the schema doing the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: recommended Bitcoin fee rates at three confirmation targets, plus mempool size and block height, with the data source (mempool.space) named. An agent can tell this is a BTC on-chain fee/mempool reader. It does not explicitly distinguish itself from related siblings such as gas_price or multi_chain_gas, which keeps it short of a 5.

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?

Usage is implied: fetch it when you need Bitcoin fee estimates or mempool conditions, and the confirmation tiers signal the decision the caller faces. There is no explicit statement of when to prefer this over gas_price/multi_chain_gas, nor any exclusion or prerequisite beyond the pricing note.

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 exist, so the description carries the full burden. It does disclose the genuinely non-obvious behaviour, the x402 payment model at 0.002 USDC per call, and the paymentProof description supplies the 402-handshake flow. It says nothing about side effects, rate limits, or what the response looks like, which leaves meaningful gaps for an unannotated 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 function and followed by the pricing constraint. Nothing is redundant and every clause carries information an agent needs.

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 and no annotations, the description should at least indicate the return type, and it is ambiguous whether counting returns an integer while adding returns a date. The payment and holiday-skipping semantics are covered, but the return contract is not.

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 80%, so the baseline is 3. The description adds the useful fact that 'add N business days' corresponds to the undocumented add parameter, but it does not clarify the required from plus to combination for counting mode, or what happens if both to and add are supplied.

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 computation (count working days, or add N business days to a date) and its defining rule (skip weekends and official public holidays for a given country), which clearly distinguishes it from the neighbouring date_diff and public_holidays tools without naming them. It is not a tautology, but it never explicitly routes against those 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?

Two distinct modes are announced, but the description never states which parameter selects which mode (to for counting vs add for shifting), nor when an agent should prefer this tool over date_diff or public_holidays. Usage is implied by the prose rather than operationalized.

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 present, so the description carries the full burden. It does disclose an important behavioral trait — a per-call cost of 0.001 USDC via x402 — but it omits whether one call returns all formats simultaneously or whether casing is lossy for some inputs, and says nothing about reversibility or output shape.

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 short sentences, zero waste, with the output set front-loaded and the cost appended. Every clause 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?

With no output schema, the description usefully enumerates the returned formats, and pricing/payment is covered by the schema. The only residual gap is confirming that all formats are returned together rather than one per call.

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 payment flow for `paymentProof` is documented thoroughly in the schema itself. The description adds no parameter-level meaning beyond the schema, so 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?

The description enumerates the nine case formats produced, which makes the tool's output identifiable, but it never states the verb or that it acts on the input `text` — an agent must infer that it converts the supplied string into all of these forms at once. Nothing distinguishes it from nearby text utilities like slugify or text_stats.

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?

There is no guidance on when to use this versus slugify, text_stats, or a formatting tool, and no statement of what happens if you only need one case. The only usage instruction (the x402 payment loop) lives in the schema, not the description.

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. It does disclose meaningful behavior: the DefiLlama data source and the paid x402 model at 0.002 USDC per call, which is valuable for planning. It does not state that this is a read-only, idempotent lookup, nor anything about upstream caching or data freshness.

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 short sentences, front-loaded with the core capability and followed by the cost model. Nothing redundant; the source and pricing each earn their 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 simple read lookup with three optional parameters and no output schema, the description covers source, two query modes, and payment cost. Return shape (ranking fields, ordering) is not described, but it is largely self-evident from the stated purpose.

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 67%, and the description only indirectly touches the parameters: 'single chain' maps to chain, 'ranking' to limit's default of 20, and the per-call payment to paymentProof. The mechanics of paymentProof and the limit's max of 100 are left entirely to the schema, so the description adds only marginal meaning.

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: a TVL ranking of blockchains sourced from DefiLlama, or the TVL of one chain. The 'blockchains' scope implicitly separates it from the sibling protocol_tvl, but that differentiation is never made explicit.

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 two modes (ranking vs single chain) are implied by whether a chain is supplied, which is a reasonable usage signal. However, there is no explicit when-to-use guidance, no statement of when to prefer protocol_tvl, and no mention of the first-call-402 pattern in the description itself (only in the schema).

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 burden and does disclose the commercial trait (0.001 USDC per call via x402) and the derived return values, which go beyond a plain conversion. It stops short of explaining the 402 handshake or what happens on malformed color input, though the payment mechanics are covered in the schema's paymentProof description.

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 with zero waste: capabilities front-loaded, commercial constraint second. Nothing repeated or padded.

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, no-output-schema utility the description enumerates what comes back, so an agent knows the return content without an output schema. Minor gap: it does not indicate whether complementary/analogous colors and the contrast ratio are always returned or conditional on input format.

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 parameters (including the two-step paymentProof flow) are already fully documented in the schema. The description's mention of HEX/RGB/HSL merely mirrors the color parameter's own description and adds no format, default, or validation detail beyond it; 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?

States a specific verb (converts) plus the exact resource and format set (HEX, RGB, HSL), and additionally names the derived outputs (complementary/analogous colors, WCAG contrast ratio). No sibling in the list overlaps this function, so an agent can select it unambiguously.

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?

Usage is only implied by the description of formats; there is no explicit when-to-use/when-not statement or named alternative (closest siblings, case_convert and units_convert, are unrelated enough that no routing is needed). The one piece of real guidance — pricing at 0.001 USDC per call via x402 — is a useful constraint but is stated rather than explained.

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 still discloses meaningful behavior: the exact signals combined, the output scale and classification, the conditional Kelly position when bankroll is given, entry/stop/target levels and warnings, plus a per-call cost of 0.02 USDC via x402. It does not state read-only status or rate limits, but the pricing and output-shape disclosures add substantial context beyond the schema.

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 two dense sentences with no wasted words. The aggregation and output scale are front-loaded, followed by conditional Kelly sizing, output extras, and cost.

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?

There is no output schema, so the description appropriately enumerates the return: composite score, classification, Kelly sizing, entry/stop/target and warnings. Payment handling is covered by the paymentProof schema, leaving only minor gaps such as exact response field names and error 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?

Schema coverage is 100%, so chain, address, bankroll and paymentProof are already documented in the schema. The description adds only the conditional effect of bankroll (Kelly-sized position) and otherwise does not expand parameter meanings beyond the schema, making 3 the appropriate 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 states a specific verb and resource: it aggregates council_verdict, technical_indicators, funding_rates, fear_greed and hype_signal into a single -100 to +100 composite score with a named classification scale. It names the sibling tools it combines, so an agent can distinguish it from calling those individual tools or a generic scanner.

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 makes clear this is the aggregate option rather than a single-signal tool, and notes the conditional Kelly sizing when bankroll is supplied. It does not explicitly state when not to use it versus siblings like signal_scanner or when to call the component tools separately, so it stops short of 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?

With no annotations, the description carries the full behavioral burden. It discloses the paid nature and cost (0.001 USDC per call via x402) and mentions the year-by-year schedule output, but it does not explain the payment workflow, rate limits, or that the operation is purely computational.

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 two compact sentences with no wasted language. The core purpose is front-loaded, followed by the cost disclosure.

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 covers the main result and the per-call cost, and the schema supplies most parameter details. However, with no output schema and no annotations, it should more fully explain the payment-proof workflow and the meaning of periodsPerYear for compounding frequency.

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 high at 83%, so the schema already documents most parameters. The description adds conceptual context by referencing principal, periodic contributions, and compounding, but it does not clarify the undocumented periodsPerYear parameter or add syntax 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 computed result: future value of a principal with periodic contributions and compounding, plus a year-by-year schedule. It is specific enough to distinguish this from generic arithmetic tools, though it does not explicitly differentiate from finance siblings like apr_apy or dca_plan.

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 no explicit guidance on when to use this tool versus alternatives, nor does it state prerequisites or exclusions. The implied use case is compound-interest projection, but there is no routing guidance among the many finance-related siblings.

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 and does well: it discloses the voting model, veto behavior, the returned artifacts, and the per-call price. It omits whether any state is written, latency, and rate-limit behavior, which matters for a paid paid-per-call endpoint.

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 plus a price line, front-loaded with the differentiating mechanism before the commercial detail. Nothing is padded and the agent roster is the useful kind of specificity.

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?

There is no output schema, and the description compensates by naming the return fields (verdict, tally, evidence). It leaves the verdict vocabulary and confidence scale unspecified, which is the main remaining gap for an agent that must interpret the response.

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%, including the full x402 first-call/402/pay/retry flow for paymentProof, so the schema does the heavy lifting. The description adds no parameter-level meaning beyond implying a token is required.

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 mechanism — seven named rule-based agents voting with confidence and reasons, with Guardian/Wash Detector veto power — and the output (consensus verdict, tally, evidence). That is far more concrete than a generic 'analyze token'. It never names a sibling (e.g. token_audit or wash_trading_check) to contrast against, so it stops short of the 5 bar.

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 statement of when to choose this over siblings such as token_audit, wash_trading_check, or hype_signal, and no prerequisites (supported chains are only in the schema). The cost note ('0.02 USDC per call') hints at a paid path but is not usage 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?

No annotations exist, so the description carries the full behavioral burden. It usefully discloses the per-call cost and the paid-call model via x402, but says nothing about behavior on invalid cron expressions, what the English explanation looks like, or whether counts are capped.

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 short sentences, the functional core front-loaded and pricing appended. Zero wasted words.

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 4-parameter utility with no output schema, the definition covers purpose, scope, and cost adequately; the payment retry semantics live in the schema. Only minor gaps remain: invalid-input behavior and the count parameter's effect.

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 75% and the schema documents tz and paymentProof well. The description reinforces the time zone and 5-field aspects but adds nothing about the count parameter or the payment-proof retry flow beyond what the schema already states. 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?

Three specific verbs (parses, explains, lists next run times) applied to a specific resource (5-field cron expression) with a time zone scope. No sibling is remotely similar, so an agent can select it unambiguously.

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?

Purpose strongly implies when to use it, but there is no explicit when/when-not guidance or mention of alternatives, and the payment flow ('0.001 USDC per call via x402') is stated as a fact rather than as a usage consideration.

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 usefully discloses that the endpoint costs 0.002 USDC per call via x402, which is important context beyond the schema, and it names returned metrics, but it omits rate limits, response shape, and broader error/payment behavior beyond the schema's paymentProof note.

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 front-load the resource, ranking, metrics, and pricing. There is no wasted prose, and the payment disclosure is appended efficiently.

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 read/list endpoint with no output schema, the description covers the ranking, included metrics, and payment model. However, with no annotations and no output schema, it still leaves gaps around limit semantics and the exact response 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 description coverage is only 50%: paymentProof is well documented in the schema, but limit has no description at all. The tool description does not compensate by explaining what limit controls, so an agent gets little added meaning from the description for that parameter.

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 resource (crypto categories), ranking basis (24h market-cap change), and included fields (market cap and volume), with useful examples like AI, memes, DeFi. It is clear what the tool returns, but it does not explicitly differentiate itself from sibling market tools such as top_movers, trending_tokens, or crypto_global.

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?

There is no explicit guidance on when to use this tool versus alternatives, nor any when-not conditions. The ranking metric implies a category-performance use case, but the description does not route the agent among the many sibling crypto tools.

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 discloses the paid nature of the tool—0.001 USDC per call via x402—and the schema explains the 402 payment flow, which covers the most important operational behavior for this simple read-only data 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-load the specific data returned and follow with the payment cost. There is no redundant or filler text, and the structure is immediately useful to an agent deciding whether to call it.

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 no-annotation, no-output-schema read tool, the description states the metrics, source, and cost, while the schema covers payment mechanics. It is complete enough to invoke correctly, though it could briefly clarify how it differs from sibling crypto data 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?

There is only one parameter, paymentProof, and the schema description coverage is 100%, so the schema already explains when to omit it and how to supply proof after payment. The description adds no parameter-level meaning beyond the schema, which is acceptable at baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the specific global crypto metrics returned—market cap, 24h volume, market-cap change, and BTC/ETH dominance—plus the data source CoinGecko. It is clearly distinct from price-focused siblings such as crypto_price, but it does not explicitly contrast itself with those 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?

Usage is implied by 'Global crypto market cap' versus token-level tools like crypto_price or crypto_categories. However, there is no explicit when-to-use statement, no exclusion of alternatives, and no guidance on whether this is for aggregate market snapshots versus specific asset data.

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 data source with fallback, the 20-symbol cap, default quote currency, and a pay-per-call x402 cost, which are important behavioral details beyond the schema. It does not cover rate limits or error behavior, but the core operational profile is clear.

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 returned data, then adds source, constraints, and cost. Every clause carries useful information with no wasted text.

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 usefully lists the returned fields: last price, 24h change, high, low, and quote volume. It also covers source, fallback, max symbols, default quote, and payment cost, leaving only minor response-shape details unspecified.

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 documents symbols, quote, and paymentProof. The description repeats the default quote and max symbols but does not add substantial parameter semantics beyond the schema, making 3 the appropriate 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 a specific resource and returns: last price, 24h change, high, low, and quote volume for up to 20 symbols, with Binance spot and Bybit fallback. It is clear what the tool does, but it does not explicitly distinguish itself from siblings like ohlcv or token_market.

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 usage for a quick multi-symbol spot price snapshot and mentions the max-symbol/default-quote constraints. However, it does not state when to use this tool versus alternatives such as ohlcv or token_market, nor does it include 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?

No annotations exist, so the description carries the full burden. It does disclose a non-obvious behavioral trait: the 0.002 USDC x402 charge per call. It omits other important traits such as statelessness, size/row limits, and what happens on malformed or headerless CSV. It also does not say whether all statistics are returned in one payload.

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, output list front-loaded, pricing appended where it belongs. No filler, though the pricing sentence is a slight tack-on rather than integrated with usage.

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 usefully enumerates the returned metrics, which is the key thing an agent needs. What remains missing is input constraints (encoding, size, numeric-column handling) and failure behavior, which matter for a paid call.

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 (csv and paymentProof) are documented in the schema, including the two-step 402 flow. The description adds no semantics beyond that, 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?

Enumerates concrete outputs (per-column type detection, count, empties, uniques, min/max/mean/median/stdev for numeric columns), so the resource and operation are unambiguous. It is distinguishable from the closest sibling csv_to_json by being analysis rather than conversion, though it never names that sibling 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?

No statement of when to use this versus csv_to_json, text_stats, or json_query, and no prerequisites such as header-row requirement or column-count limits beyond what the schema says. The only guidance-like sentence is about billing, not about tool selection.

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 and does disclose material behavioral traits: a per-call price of 0.002 USDC via x402 and the parsing characteristics (quoted fields, header row, delimiter auto-detection). It does not address input size limits, malformed-CSV error behavior, or whether header detection can be disabled — meaningful gaps for a parsing tool with zero 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?

Two sentences, both front-loaded with the highest-value information: the primary CSV→JSON direction first, the reverse mode second, and the pricing disclosed at the end. No filler 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?

Given four parameters, no output schema, and no annotations, the description covers direction, return shape ('array of objects' vs CSV), parsing assumptions, and cost. Remaining gaps are limits and failure modes, which are secondary but notable for a paid parsing endpoint.

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 all four parameters documented including the mode enum values and the full x402 payment-proof handshake, so baseline is 3. The description restates the mode duality and delimiter auto-detection but adds no syntax, format, or constraint 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 verb and resource in both directions: parses CSV with quoted fields and a header row into an array of objects, and converts an array of objects to CSV via mode=to_csv. The transformation semantics (header row required, quoting handled, delimiter auto-detected) make the tool's behavior unambiguous. It stops short of distinguishing itself from the close sibling json_format, which an agent could plausibly confuse with this.

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 names the condition that selects the reverse direction (mode=to_csv) and notes the delimiter is auto-detected by default, giving an agent clear context for invocation. There are no exclusions or 'when not to use' statements, and no guidance on choosing this over json_format for JSON-side work.

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, and it does disclose a meaningful behavioral trait: a per-call charge of 0.001 USDC via x402, which an agent needs for cost-aware selection. However, it omits the actual 402 handshake behavior (first call returns payment instructions) and says nothing about side effects, though a date-difference computation is implicitly a pure read.

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 with the core capability front-loaded and the cost stated immediately after. The payment sentence partially duplicates the paymentProof field description, but it is short enough that the redundancy costs little.

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?

There is no output schema, and the description compensates by specifying the returned units (years, months, days, hours, total seconds). Combined with full parameter coverage this is nearly complete; only accepted input date formats and sign/ordering behavior are left to inference.

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 'from' and 'to' documented including the 'default now' behavior for 'to', so the schema does the heavy lifting. The description adds nothing beyond it—no accepted format examples (ISO 8601, epoch, timezone suffix) or negative-difference semantics—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 computation (exact difference between two dates/timestamps) and enumerates the output units, so an agent knows precisely what it gets. It is distinguishable from conversion siblings like timestamp_convert or timezone_convert by the word 'difference', but never names or rules out those alternatives 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?

No statement of when to use this versus timezone_convert, business_days, or timestamp_convert, nor any prerequisites about accepted date formats. The only contextual guidance is the pricing line, which tells the agent about cost but not about tool selection.

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 burden and does disclose key behavior: this is a simulation over past prices (read-only, no side effects), it costs 0.002 USDC per call, and it names the returned metrics. It stops short of describing pagination/limits or the full 402 handshake, though the schema's paymentProof field covers that flow.

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, zero waste: the simulation behavior and outputs come first, the cost disclosure follows. Nothing is repeated or padded.

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 usefully enumerates the return fields (invested, value, average cost, return), and the payment model is stated. The gap is the two undocumented scheduling parameters, whose meaning must be guessed from defaults alone.

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 60%: pair, amount, and paymentProof are documented, but days and everyDays have no descriptions. The description's 'fixed amount' and 'on a schedule' loosely map to amount and everyDays, but it never clarifies that everyDays is the buy interval or what days controls, leaving the untyped-in-schema params under-specified.

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 ('Simulates buying a fixed amount of a crypto pair on a schedule over past daily prices') and even names the outputs. No sibling tool does DCA backtesting, so differentiation is implicit but the purpose is 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 implies the use case (backtesting a DCA strategy on historical prices) and mentions the per-call cost, which is real usage context. However, it never says when to prefer this over alternatives like compound_interest or percent_change, and gives no exclusions or prerequisites beyond the payment hint.

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 behavioral burden. It usefully discloses the paid x402 model at 0.002 USDC per call, result ranking by liquidity, chain coverage, and returned fields, though it omits failure modes or rate limits.

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 tight sentences, front-loads the core search capability, then adds result and payment details. 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?

For a three-parameter search tool with no output schema, the description covers purpose, result fields, chain scope, and payment behavior. It lacks explicit sibling routing guidance, but an agent has enough context 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 67%: q and paymentProof are documented in the schema, while limit has bounds and a default but no description. The prose adds little parameter-specific meaning beyond the schema, 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 states a specific verb and resource: free-text search across DEX pairs on all major chains. It clearly distinguishes the tool as a search interface rather than a price or market-data lookup, though it does not explicitly name sibling 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 phrase 'free-text search across DEX pairs' implies when the tool is useful, but there is no explicit guidance on when to use it instead of siblings such as crypto_price, token_market, or trending_tokens. No exclusions or alternatives are named.

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, but it does disclose meaningful operational behavior: the 0.001 USDC per-call cost and the x402 payment model, with the retry-with-proof flow documented in the paymentProof param. It says nothing about failure modes (word not found), rate limits, or caching, which is the main gap for an unannotated 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 terse fragments with zero filler, front-loading the result contents and ending with the billing note. It reads as a feature list rather than a sentence stating the action, which slightly weakens front-loading of 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?

For a simple single-word lookup with full schema coverage, this is nearly complete: with no output schema, the enumerated fields effectively serve as a return-value description, and the payment model is called out. The missing piece is sibling differentiation from wikipedia/ai_answer and unknown-word 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?

Schema coverage is 100% and the paymentProof description fully explains the 402 handshake, so the description adds nothing on parameters. Baseline 3 applies; the only added parameter meaning is the price figure.

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 enumerates the concrete contents of a definition result (part of speech, phonetics, audio, examples, synonyms/antonyms), making the resource unmistakable as a dictionary lookup. It lacks an explicit verb and never distinguishes itself from siblings like wikipedia or ai_answer that could also return word meanings.

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?

There is no guidance on when to choose this over wikipedia, book_search, or ai_answer, and no mention of the expected input (a single English word) or what happens for unknown/misspelled words. The only stated condition is the payment step, which lives in the schema rather than as usage 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?

With no annotations, the description must carry the full behavioral burden. It usefully discloses the return contents (answers with TTLs), the DoH transport, and per-call cost of 0.001 USDC via x402 — real value beyond structured fields. However, it says nothing about failure behavior (NXDOMAIN, timeouts), rate limits, or what a 402 response body looks like, leaving an agent to discover the error path at runtime.

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 with zero filler, and the capability statement is front-loaded before the pricing note. Every clause carries information an agent needs.

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 3-parameter, no-output-schema tool, the description covers what it does, what comes back (answers with TTLs), and what it costs, while the schema carries the payment handshake. It falls just short on error/NXDOMAIN semantics and on any hint about naming conventions for the 'name' input.

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 genuine meaning: it enumerates the nine supported record types (A, AAAA, CNAME, MX, TXT, NS, SOA, CAA, SRV) that the schema only labels as 'Record type' with no enum. That closure of the value space is a real contribution; the paymentProof flow is left to the schema, which already documents it fully.

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 (resolves) and resource (DNS records), enumerates exactly which record types are supported, and names the transport (DNS-over-HTTPS). This clearly separates it from siblings like whois, http_check, and domain_email_security without opening any schema.

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?

There is no when-to-use guidance and no routing to alternatives, despite several adjacent siblings (whois, domain_email_security, http_check) that an agent could plausibly confuse with this one. The only contextual signal is the price, which is a cost note rather than usage 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?

With no annotations, the description carries the full burden and does well: it discloses the 10 MB cap, that only public URLs work, per-call cost and x402 payment, and that images are captioned by a vision model rather than OCR'd. It omits failure behavior (oversized document, paywalled/private URL, unsupported format) and any rate-limit or output-size notes.

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 tightly packed sentences: capabilities and limits first, then the cost/payment model. No filler and nothing buried.

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, but the description states the return shape (clean Markdown, images described) well enough. The remaining gap is lead-in to maxChars/truncation behavior and error cases, which matters somewhat for a paid per-call tool but is minor overall.

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 67%: url and paymentProof are documented in-schema (paymentProof quite thoroughly), but maxChars has no description anywhere, and the tool description never explains whether exceeding it truncates or errors. At that coverage level the schema does the heavy lifting, so baseline 3 is appropriate with no compensating detail added.

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 (downloads) and transformation (returns content as clean Markdown) over a named set of formats (PDF, DOCX, XLSX, ODT, CSV, HTML, XML, images), plus a size ceiling. It is clear what the tool does, but it never names or contrasts with the closely related siblings web_extract/web_render, so an agent must infer the boundary itself.

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?

Usage is only implied: 'public document' and 'for LLM use' scope the tool, and '0.005 USDC per call via x402' hints at the metered path, but there is no explicit when-to-use/when-not guidance and no mention of web_extract or web_render as alternatives for HTML/web pages.

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 does disclose a non-annotated behavioral trait that matters: cost of 0.002 USDC per call via x402, plus the fact that it reads (not mutates) and returns flagged weaknesses. It stops short of describing the payment/auth handshake mechanics, error behavior, or limits, though the payment flow is partly covered by the schema field description.

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 capability and concrete checks come first, cost last. Every clause earns its place and the reader gets the essential picture immediately.

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 and no annotations, the description should explain the return shape; it does so at a high level (records, parsed policies, weakness flags) and adds pricing. Missing only finer detail on result structure and failure modes, which is minor for a single-domain read 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 description coverage is 100%, so both parameters (domain, paymentProof) are already documented, including the 402 retry flow. The description adds no meaning beyond the schema, so 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?

Specific verb and resource: reads SPF, DMARC and MX records for a domain, then parses policies and flags weaknesses. Clear about what comes back (records + policy assessment), which distinguishes it from a raw lookup. It never names the nearby siblings (dns_lookup, email_validate, whois), so the differentiation is inferable rather than explicit.

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?

Usage is implied by the mention of auditing for missing DMARC, p=none, or SPF +all weaknesses, which tells an agent this is an email-auth security check. But there is no explicit statement of when to prefer this over dns_lookup, email_validate, or whois, and no prerequisites or 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?

With no annotations, the description carries the full burden and does reasonably well: it discloses the verdict scale and attached flags, the underlying signal sources, and the paid nature of the call (0.003 USDC via x402), which is behavior an agent must anticipate. It omits error behavior beyond the payment handshake, latency, and whether results are cached or rate-limited.

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?

Effectively three compact clauses: what signals are returned, what verdicts look like, what it costs. Front-loaded with the decision-relevant purpose and no filler. Slightly dense but every sentence carries 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?

No output schema exists, so the description must convey return shape, and it does: verdict enum plus flags plus the enumerated signal categories. Combined with a fully documented two-parameter schema, an agent has what it needs to call this correctly; only rate limits and failure modes are unaddressed.

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 both parameters are fully documented there, including the example domain and the complete 402/paymentProof retry flow, so the baseline is 3. The description's only added semantic is the price (0.003 USDC per call), which is useful but marginal relative to what the schema already explains.

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?

Names the resource (domain) and the deliverable (threat verdict: risk types, categories, popularity rank, URL-scanner verdicts, registration age) in the first clause, and states the return values (clean/suspicious/malicious with flags). It is clearly distinguishable from siblings like ip_reputation, whois, dns_lookup or url_parse, which cover different resources or different questions.

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 concrete trigger situations: before an agent visits a link, signs a transaction or pays an invoice. That is a clear positive-use context. It does not, however, say when to prefer this over ip_reputation or whois, nor when a verdict is unnecessary, so the routing guidance stops short of full discrimination.

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 burden and does meaningful work: it discloses that the output is an unsigned transaction (not a signed/broadcast one), that a 30 bps integrator fee is embedded, that each call costs 0.001 USDC via x402, and that LI.FI Composer executes the route. It does not cover token approval requirements, failure/refund behavior, or whether the quoted rate is final.

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?

Four dense sentences, front-loaded with the core action and output type before fees and payment mechanics. Every sentence carries operative information an agent needs (output form, fee, payment flow, execution venue); nothing is padded.

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 an 8-parameter, no-annotation, no-output-schema tool that spends real money, the description covers output type, cost structure, and payment protocol well. It leaves gaps around approval steps, slippage/default behavior implications, and what the unsigned transaction payload contains.

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; the description earns an increment by explaining where vaultToken comes from (earn-vaults lpToken.address) and by framing the call's inputs as a swap-bridge-deposit pipeline. The payment/402 mechanics also contextualize the optional paymentProof parameter beyond its schema text.

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 concrete verb chain and resource: returns an unsigned EVM transaction that swaps + bridges + deposits into a vault in one step. It implicitly routes to the earn_vaults sibling by specifying that vaultToken comes from earn-vaults lpToken.address, which differentiates it from swap_quote_and_build. It stops short of naming an alternative explicitly, so not a 5.

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?

Clearly scopes usage: cross-chain deposit from any chain into any vault, with the token-address sourcing rule pointed at earn_vaults. The payment sequencing (omit paymentProof on first call, receive 402 instructions, retry with proof) tells the agent exactly how to drive the call. No explicit "when not to use this / use X instead" clause.

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 well: it discloses cursor pagination, the exact x402 cost (0.001 USDC per call), and what each result contains (APY, TVL, tokens, vault address). It does not detail rate limits or failure behavior, but the payment model and return shape are clearly surfaced.

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?

Five tight sentences, front-loaded with purpose then filters, output, pagination, and pricing. No filler; every clause conveys actionable 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?

For a 7-parameter tool with no output schema, the description adequately explains return values, pagination, and the x402 payment flow (reinforced in the schema's paymentProof field). The chief gap is the phantom minimum-TVL filter, which slightly undermines completeness.

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 71% and the description adds filter semantics for chainId, asset, and protocol. However it advertises a 'minimum TVL' filter that does not exist in the schema (only sortBy apy/tvl is present), which introduces a mismatch that could mislead an agent into passing a nonexistent parameter.

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 ('Lists transactional DeFi vaults') with scope ('across EVM chains via LI.FI Earn') and links to the sibling earn_deposit tool that consumes its output. An agent can immediately distinguish this from generic TVL tools like chain_tvl or protocol_tvl.

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?

Names the filtering dimensions and explicitly frames the output as the prerequisite for the earn-deposit tool, giving clear usage context. It stops short of stating when NOT to use it or contrasting it with the TVL/analytics siblings, so 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?

With no annotations, the description carries the full burden, and it does disclose two important traits: the pay-per-call cost (0.001 USDC via x402) and the exact fields returned. However, it says nothing about rate limits, caching/refresh cadence, behavior when the upstream USGS feed is unavailable, or what happens if payment is omitted (that is left entirely to the schema's paymentProof field).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences: the substantive data description comes first and the pricing note second. Every clause contributes either capability or cost information, with 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?

There is no output schema, so the description usefully enumerates the returned fields (place, depth, time, tsunami flag, USGS link), and the payment mechanics are covered by the parameter schema. What is missing is a note on result volume/ordering and freshness, which an agent might want before calling.

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 all three parameters are already documented, including the 402 payment round-trip. The description restates the magnitude threshold and the 24h/7d window rather than adding new meaning, which is the expected baseline when the schema does the work.

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 resource (earthquakes), a data threshold (magnitude 4.5+) and a scope (last 24 hours or 7 days), plus the fields returned and the upstream source (USGS). Nothing in the sibling list covers seismic data (weather and space_weather are distinct), so an agent can select this without ambiguity.

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?

Usage is only implied: an agent infers it should call this when it needs recent significant earthquake data. There is no explicit statement of when to prefer this over alternatives, no note about data staleness, and no mention of how the day/week choice should be made beyond the parameter default.

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 and does well: it discloses the exact checks performed and the cost model (0.001 USDC per call via x402), with the 402 payment flow documented in the schema. It stops short of describing the response shape or failure semantics (e.g., how invalid vs. disposable results are reported).

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 compact sentences with zero filler, front-loading what is checked and then the pricing constraint.

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 core decision inputs — what is validated and what it costs — are covered, and payment mechanics live in the schema. Without an output schema, the description could have said more about what the result contains (validity flag, reason codes), which is the only notable gap for a validation 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 both parameters are already fully documented, including the paymentProof handshake. The description adds no syntax or format detail beyond what the schema provides, 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 specific verbs and resources: syntax check, MX/A record lookup for mail reception, and disposable/role address detection. An agent can distinguish this from domain_email_security or address_check without opening the schema.

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?

Usage is implied (validate an email address before using it), but there is no explicit when-to-use vs. when-not guidance and no pointer to the related sibling tools such as domain_email_security or address_check.

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 and does well: it discloses the paid x402 model at 0.002 USDC per call, that it reads Ethereum mainnet directly, and what data classes are returned. It omits error behavior (e.g., unresolvable names) and whether reverse results can be unverified, but core behavioral traits are covered.

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, front-loaded with the primary forward-resolution behavior, followed by reverse mode and then cost/source. Every clause carries information an agent needs.

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, and the description names the returned fields (address, avatar, url, twitter records; verified primary name on reverse), which is adequate. It is slightly incomplete on failure modes and the exact response shape for partial records.

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, including the dual ENS-name/0x-address use of 'name' and the 402 payment flow for paymentProof. The description adds only marginal value by restating the dual-input behavior; 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 (resolve) and resource (ENS name) and covers both directions — ENS name to address/records and 0x address to primary ENS name. An agent can tell exactly what the tool does without opening the schema.

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 makes the forward vs reverse selection condition explicit (ENS name vs 0x address), and no sibling tool performs ENS resolution, so differentiation is implicitly clear. It stops short of an explicit 'use this instead of dns_lookup' statement or 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?

With no annotations, the description carries the full disclosure burden. It usefully adds the x402 payment model and 0.001 USDC cost, plus the EIP-191 protocol, but it omits return-format details, error behavior, and the exact permission/read-only profile.

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 concise sentences: the first front-loads the core operation, and the second states the payment requirement. There is no redundant or wasted language.

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 is adequate for selecting and invoking the tool, and the paymentProof flow is well documented in the schema. However, with no annotations and no output schema, it should more explicitly describe return values and behavior when the optional address is omitted.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters are already documented in the schema. The description only reinforces the expected-address concept and adds no syntax or format detail beyond what the schema 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?

The description states a specific verb and resource: recovering an Ethereum signed-message signer and checking it against an expected address. The EIP-191 personal_sign qualifier distinguishes it from the sibling solana_signature_verify tool and similar verification utilities.

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 clear context by naming Ethereum and EIP-191 personal_sign, so an agent can infer this is the EVM message-signature verification tool rather than the Solana equivalent. It does not explicitly name alternatives or exclusions, which keeps it just below the top score.

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 behavioral burden. It usefully discloses a concrete cost of 0.001 USDC per call via x402 and implies a read-only data lookup, but it does not describe the first-call 402 payment handshake, rate limits, caching, or error behavior. The payment proof schema covers the handshake mechanics, so the description's cost disclosure is meaningful but incomplete.

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 short sentences with zero filler. The primary capability is front-loaded, followed by the payment cost, making the definition 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?

For a simple read-only data tool, the description covers the core return values and payment cost. However, with no output schema and no annotations, it leaves gaps around the days parameter limits, payment flow behavior, and response format details that would help an agent invoke it confidently.

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 only 50%: paymentProof is well documented in the schema, while days has no schema description. The description says 'previous days for trend,' which hints that days controls lookback, but it does not mention the parameter name, default of 7, or maximum of 30, so it does not compensate for the undocumented half of the parameters.

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 resource and returned content: the current 0-100 crypto Fear & Greed index, its classification, and previous days for trend. It is readily distinguishable from sibling tools like crypto_price or technical_indicators, though it does not explicitly name or contrast those 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 description explains what data is returned but gives no guidance on when to use this tool versus alternatives. There are no exclusions, prerequisites, or named sibling tools to help an agent route between this and related crypto data tools.

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 does carry useful behavioral weight: it discloses the per-call cost and the x402 payment model, which is a critical trait an agent must know before invoking. However, it omits other important behavior for a paid call — what happens on an unpaid call (the 402 handshake is only in the schema), failure/insufficient-payment behavior, and rate limits.

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 dense sentences with zero filler: the data payload and venues come first, the pricing model second. Nothing would be lost if trimmed further, and nothing extraneous is present.

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?

There is no output schema, so the description appropriately enumerates the returned fields (rate, next funding time, mark/index, open interest, annualised funding). Combined with the fully-documented params, an agent has nearly everything needed; the only gap is the unpaid-first-call behavior, which lives in the schema rather than the description.

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 parameters (symbol and paymentProof) are already fully documented in the schema, including the two-step 402 payment flow. The description adds no parameter-level detail beyond that, 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?

The description names the exact data points returned (funding rate, next funding time, mark/index price, open interest) and the two venues (Bybit, OKX), which is far more specific than the name alone. It does not explicitly contrast itself with sibling tools, but no sibling covers perpetual funding, so the distinction is implicit rather than stated.

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 says what the tool returns and that it is paid (0.002 USDC via x402), but gives no guidance on when to call it versus alternatives or which symbols are supported beyond the schema's BTC/ETH/SOL examples. The caller must infer the use case from the data field list.

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?

Since there are no annotations, the description must carry the burden of behavioral details. It includes crucial operational data regarding cost ('0.002 USDC per call') and payment flow ('via x402'), which are essential behaviors for an agent to know before invocation.

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 brief, front-loading the most critical information—the data type, scope, and cost—in a single, efficient sentence with no extraneous language.

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 is complete regarding the data request itself, defining what data and how it is scoped (ECB, date range). While the complex multi-step payment process for the 'paymentProof' parameter is detailed in the schema, the description is fully sufficient for the agent to understand the tool's function.

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 provides 100% coverage of parameter definitions and types. The description does not offer additional semantic context or constraints that are not already present in the structured input 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 is highly specific, stating that it retrieves 'Daily ECB reference rates' over a 'date range.' This clearly defines the resource, the specific scope (ECB), and the nature of the data (daily historical rates).

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?

By mentioning 'Daily ECB reference rates over a date range,' it strongly implies the operational boundary, distinguishing it from real-time rate tools (like fx_rates) or daily summary tools. The specificity of the data set is a strong usage constraint.

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. It does disclose the meaningful commercial behavior — metered cost of 0.001 USDC per call via x402 — and names the data source (ECB), which implies daily reference-rate freshness. It omits rate limits, caching, and what happens on payment failure; the 402 handshake is only in the schema.

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 short sentences, both front-loaded with the capability first and the cost second. Every clause carries information an agent needs; nothing is repeated from schema or title.

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 5-parameter, no-annotation, no-output-schema tool, the description is adequate on capability and cost but silent on return shape, single-date vs range history, supported currency list, and error behavior. An agent knows what it does and what it costs, but not what it gets back.

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 80%, so baseline is 3, but the description adds real meaning: 'optional amount conversion' explains what the undocumented 'amount' parameter does, and 'latest or historical' clarifies that 'date' is optional and mutually exclusive with the default latest mode. Only 'symbols' and 'paymentProof' semantics come purely from 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 resource (European Central Bank reference rates), scope (latest or historical), coverage (30+ fiat currencies) and an extra capability (optional amount conversion). That is enough to distinguish it from crypto_price and funding_rates, though it never names 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?

The 'latest or historical' phrasing implies the two usage modes and the 'fiat currencies' scope implies when to prefer it over crypto or conversion tools. However there is no explicit when-to-use, no exclusion of alternatives, and no stated prerequisite beyond the payment note.

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 and does disclose the two most consequential traits: the tool is paid (0.001 USDC per call via x402) and the first invocation does not return data but payment instructions. It does not state data freshness/update cadence or whether results are cached, which an agent quoting gas prices would want.

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 short sentences, each carrying distinct information: EVM metrics, Solana metrics, price. No filler and the most important content (what you get) is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read tool with no output schema, the description plus schema cover what is returned, in what units, and how payment works. Only freshness/cadence and failure behavior are left unstated, which is a minor gap rather than a blocking one.

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%, including a full explanation of paymentProof and its 402 handshake, so the schema does the heavy lifting. The description adds per-chain output-unit context that helps the agent pick a chain value, but nothing about the chain enum or parameter behavior beyond what the schema already says.

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 exactly which metrics are returned and splits them by chain family (EVM base/priority fee/gas price in gwei vs Solana prioritization fee percentiles in micro-lamports per CU). That is specific enough to distinguish it from siblings like crypto_price or solana_network, though it never says so 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?

Usage is only implied by the resource fetched; there is no statement of when to prefer this over crypto_price or solana_network, and no prerequisites beyond the payment note. The two-step 'first call returns 402, then retry with proof' procedure is the only concrete usage guidance 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?

With no annotations, the description still discloses the key behavioral trait an agent must know: '0.001 USDC per call via x402'. That cost/payment dimension is real value beyond structured fields. It stops short of describing the 402 challenge flow or any rate limits, though the schema's paymentProof field covers the mechanics.

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 tightly packed sentences: the capability and outputs first, the cost second. Nothing is redundant and the most important information leads.

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?

Although there is no output schema, the description compensates by naming the returned fields (lat/lon, region, country, timezone, elevation, population). Combined with the schema's payment flow, an agent has nearly everything needed; only multi-result/ambiguity behavior (relevant to the limit param) is unaddressed.

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 75% with descriptions on lang, place, and paymentProof, so parameters are largely self-documenting. The description adds no detail about limit (result capping) or lang, so it does not improve on the schema; 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?

States a specific verb and direction ('Forward geocoding') plus the input resource ('city or place name') and enumerates the return fields, so the operation is unmistakable. It does not, however, distinguish itself from nearby siblings such as address_check or weather, which also take place names.

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?

Usage is implied by 'forward geocoding of a city or place name', which is enough for an agent to recognize the use case. There is no explicit when-to-use/when-not guidance and no named alternative for related tasks like address validation or reverse geocoding.

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 exist, so the description carries the full burden. It does disclose two valuable traits: the per-call price (0.002 USDC via x402) and that only public repositories are supported. It says nothing about rate limits, failure modes for private/missing repos, or that the first call returns a 402 challenge — though the latter is covered in the paymentProof schema field.

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 compact, front-loaded statements: the payload contents first, then the price. No filler. Minor weakness is that the opening is a bare noun phrase without a leading verb, which slightly slows intent recognition.

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 read tool with 100% schema coverage and no output schema, the description adequately signals what comes back by naming the returned fields, and the schema handles the payment flow. Error behavior on private or nonexistent repos is the remaining gap, but it is minor for this tool class.

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 'repo' documented as owner/name with an example and 'paymentProof' documenting the full 402 handshake. The description adds no parameter meaning 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?

The description enumerates exactly the resource returned (stars, forks, issues, language, license, topics, last push, latest release) and scopes it to a public GitHub repository. It lacks an explicit verb such as 'fetch' or 'retrieve', but the field list makes the retrieval intent unambiguous and no sibling tool overlaps.

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?

There is no statement of when to use this versus alternatives (e.g. web_extract or http_check) and no prerequisites beyond the implicit 'public repo' scope. The only usage-adjacent content is the price, which is cost, not routing 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?

With no annotations, the description carries the full burden, and it does disclose the paid-call model (0.001 USDC via x402) which is genuinely useful behavioral context. However, it omits what happens for nonexistent/suspended users, whether authentication or rate limits apply, and how the payment retry flow behaves beyond what the schema states.

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 compact sentences with the data payload front-loaded and cost appended; nothing is wasted. It is a fragment rather than a complete sentence, which slightly weakens the framing but not the efficiency.

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 usefully enumerates the return payload (followers, repos, top repos by stars, account age) and discloses the cost, which is close to complete for a simple single-entity lookup. It stops short of covering error/not-found 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?

Schema coverage is 100%, with both 'user' and 'paymentProof' fully documented including the 402 flow, so the baseline of 3 applies. The description adds no 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?

The description is a noun phrase enumerating returned data (profile, followers, repos, top repos by stars, account age) rather than a verb+resource statement, so the agent must infer that this fetches a GitHub user's profile. The mention of 'GitHub' is only implicit via the parameter name, though the sibling github_repo makes the pairing reasonably inferable.

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 use this versus github_repo or any other sibling, and no conditions or exclusions are stated. The only contextual cue is the implicit distinction that this is user-scoped rather than repo-scoped.

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. It does disclose cost ('0.05 USDC per call via x402') and the shape of the returned data, which is useful. However, it omits whether the fee is per chain, whether paymentProof is reusable, rate limits, and error behavior — all material for a paid 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?

Five compact sentences, front-loaded with what the tool returns and followed by usage, chain coverage, and cost. There's little waste, though the sentences could be combined and the sibling overlap left unaddressed.

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 2-parameter read tool with no output schema and no annotations, the description covers what is returned, which chains, and the payment cost. It leaves gaps around return structure, per-chain pricing nuances, and why an agent should pick this over gas_price or multi_chain_gas.

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. The description's 'Covers Solana, Base, BSC and Ethereum' loosely maps to the chain enum values, and the payment model is described at a high level, but the schema already documents both parameters and the 402 flow in detail.

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 resource and its contents: gas price tiers (high/average/low), estimated confirmation times, MEV-protection fee and native token USD price, plus supported chains. It's clear what the tool returns, but it doesn't distinguish itself from sibling tools that appear to do the same thing (gas_price, multi_chain_gas), which is a requirement for a 5.

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 one usage scenario — 'Agents use this to set optimal priority fees for snipe transactions' — which implies when to reach for it. But it never states when not to use it or how it differs from the gas_price and multi_chain_gas siblings, so the agent has no explicit routing 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?

With no annotations, the description carries the full burden and does well: it discloses the per-call cost (0.05 USDC via x402), names the risk metrics returned, and the schema spells out the 402 payment flow. It still omits rate limits, pagination/ordering defaults behavior, and whether results are cached over the interval window.

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 core purpose, then risk-data specifics, then usage, then payment terms. The metric list is dense but each item is meaningful to an agent scoring tokens. 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 and no annotations, so the description must convey return content and cost — and it does via the enumerated risk fields and the x402 pricing. It falls short only on failure modes (402 handling is in the schema, not the description) and on distinguishing itself from sibling trending 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?

Schema description coverage is 67%, below the high-coverage threshold, so the description should compensate more than it does. It names the chains and three ranking dimensions (swaps, volume, market cap) but leaves filters, interval semantics, and the meaning of orderBy 'default' or holder_count/change1h undocumented anywhere but the raw enum.

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 ('Real-time trending tokens') and enumerates supported chains and ranking dimensions, so an agent knows exactly what it returns. However, it never distinguishes itself from close siblings like trending_tokens or new_token_profiles, which an agent would also consider for the same discovery task.

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 sentence 'Agents use this to discover newly launched tokens and assess risk before sniping' gives an implied use context, but there is no explicit when-to-use vs when-not, no mention of alternative tools such as trending_tokens or token_audit, and no prerequisites beyond the payment note.

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 behavioral burden. It usefully discloses the output formats and the 0.001 USDC per-call cost, but does not explain that the first call may return a 402 payment challenge or that hashing is deterministic/stateless beyond the schema's paymentProof note.

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 compact sentences with the core return behavior front-loaded, followed by the default algorithm and payment cost. There is no filler, and each sentence contributes context an agent needs.

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 hashing utility with no output schema, the description covers what is returned and the default algorithm. The payment flow is complex but is fully delegated to the schema's paymentProof field, so the description is nearly complete, with only minor room to mention the 402 handshake.

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 input schema already documents text, algo, and paymentProof in detail. The description adds only the default algorithm and pricing context, which is useful but does not meaningfully extend parameter semantics beyond 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 states a specific verb and resource: it returns the hex and base64 digest of input text. It also names the default algorithm and the payment model, so an agent can immediately distinguish this hashing tool from siblings like base64 or text_stats.

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?

Usage is implied by 'digest of the input text' and the default sha256, but the description does not explicitly say when to use this tool versus alternatives such as base64 or other utility tools. It also omits the practical first-call/payment flow, leaving that to the schema.

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 burden, and it does disclose the paid nature (0.001 USDC per call via x402), which is meaningful context. However it omits the payment round-trip behavior (first call returns 402, then retry with proof) that only appears in the paymentProof schema field, and says nothing about output format or whether conversion is lossless.

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, front-loading what it converts followed by the pricing note. No filler; every clause 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 simple codec with full schema coverage and no output schema, the description covers the formats and the payment cost adequately. The payment retry mechanic is left to the schema, which is acceptable, though noting the direction ambiguity (converting to byte length) would round it out.

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 'from' (text/hex/decimal) and 'value' are already documented. The description adds that 'byte length' is a conversion target (a direction not evident from the 'from' values alone), giving slight extra value, but the baseline 3 holds.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.