agiscorecard-web3-workbench

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

Annotations already declare readOnly, idempotent, and non-destructive; the description adds meaningful behavior: remote processing without persistence, incompatible shares staying unallocated, and disclaimed legal/enforcement functions. This goes beyond the annotation hints without contradicting them.

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

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

The description is compact and front-loads the main function before the revenue split and boundary disclaimers. The list of disclaimed capabilities is a little long, but each sentence carries content and there is 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 an output schema and detailed input schema, the description covers the main operational context: remote processing, no persistence, exact input retrieval via fetch, and non-legal boundaries. It is complete enough for an agent to invoke the tool correctly, though it leaves detailed validation to the 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 the schema already documents each parameter at a basic level. The description maps 'intended AI use' to the use parameter and 'received revenue' to receivedRevenue, but it does not add deeper semantics for asOf, region, currency, or datasets 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 leads with a concrete action: 'compares an intended AI use with your recorded grant conditions' and includes the revenue-split calculation. It distinguishes the tool's core from fetch by pointing to fetch for retrieving permits, though it does not explicitly compare it against the other 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?

The intended use is implied: use when you need to match an AI use against grant conditions and split revenue. It tells users to retrieve exact inputs via fetch or the example resource, and lists disclaimed functions (no legal opinion, enforcement, etc.), but it does not explicitly state when to use this tool over 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context: all examples are fictional, no live GPU quotes are supplied, no automatic benchmarking or reservation occurs, and parameters are processed without persistence. This goes beyond the annotations and helps set accurate expectations.

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

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

The description is four sentences and front-loads the core purpose before adding exclusions and input-source guidance. Each sentence adds distinct information, though the list of negations could be slightly tighter. Overall it is appropriately sized for the tool's complexity.

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 rich input schema, output schema, and annotations, the description covers the essential context: what the tool compares, what it filters, what it does not do, and where to get exact inputs. It does not need to explain return values because an output schema exists. The only minor gap is not explicitly stating when to prefer this over sibling cost-planning tools, but the exclusions largely compensate.

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 schema already documents each parameter. The description adds semantic meaning by explaining that compute, setup, storage, and egress costs are combined 'per accepted output,' and that mismatched workload labels, currencies, quality, and memory limits are filtered. This clarifies how the parameters relate to each other beyond their individual schema descriptions.

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

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

The description names a specific verb ('compares measured workload runs') and a specific resource (compute costs) with the exact cost components considered. It also distinguishes itself from siblings by explicitly negating live GPU quotes, inventory, benchmarking, reservation, and infrastructure management, making its scope 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 gives clear context for when to use the tool: comparing entered workload runs rather than obtaining live data. It also provides a concrete instruction to 'Retrieve compute with fetch or read its example resource to obtain exact inputs,' which routes the agent to the correct data source. It does not explicitly name sibling tools as alternatives, but the exclusions effectively communicate what this tool is not for.

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

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description adds significant behavioral detail: it does not verify reserves, issuer solvency, or redemption rights, and states that parameters are processed remotely without application persistence. This goes far beyond the structured metadata and clearly sets expectations about limitations.

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 reasonably structured, starting with the core purpose and moving to limitations and usage, but it contains redundancy: 'It does not verify reserves, issuer solvency or redemption rights' is largely repeated by 'No live asset feed, reserve audit, redemption verification, credit rating or investment recommendation'. This repetition could be trimmed for tighter prose.

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 complexity of 4 required parameters with arrays of objects, the description covers key aspects: how to obtain exact inputs via fetch/example, what the tool does and does not do, and remote processing without persistence. The presence of an output schema reduces the need to describe return values. It is complete enough for an agent to use correctly, with minor gaps around expected output shape being covered by the 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 the schema already documents all parameters with descriptions. The tool description adds context about the before/after arrays being disclosure fields and mentions retaining units/dates/source references, but does not elaborate on individual parameters beyond that. This is adequate but does not significantly enhance the schema-level semantics.

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

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

The description clearly states a specific verb and resource: 'compares two sets of supplied asset disclosure fields while retaining units, dates and source references'. This distinguishes it from siblings like compare_compute_costs or reconcile_stablecoin_records, which target different domains. The scope 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 usage by stating the tool compares asset disclosure fields and flags issues, but it does not explicitly state when to use this tool versus alternatives. It does advise 'Retrieve disclosures with fetch or read its example resource to obtain exact inputs', which is a practical prerequisite, but it lacks explicit exclusions or alternative routing compared to 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?

Annotations already mark readOnly, idempotent, and non-destructive. The description adds meaningful operational facts: local decoding, no wallet connection, no contract-safety evaluation, no application persistence, and remote processing. These constraints materially affect invocation expectations and go well beyond annotation coverage.

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

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

The description is compact and front-loaded with the core purpose, followed by exclusions and input-retrieval instructions. Every sentence earns its place; there is no filler or redundant restatement of the tool name.

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 an output schema, the description covers purpose, exclusions, input retrieval, and persistence behavior. The only notable gap is that ERC-1155 appears in the enum but the description only illustrates the ERC-20 vs ERC-721 distinction.

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 covers all seven parameters with descriptions and an enum, so the description does not need to repeat per-parameter details. The text adds conceptual orientation around recipient/spender and atomic amount but no concrete parameter-level guidance 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 a specific verb ('decodes') and object ('supported token-call shapes'), and frames the comparison against recipient, spender, and atomic amount. The negative capability list separates it from safety and simulation tools. Clear and differentiating.

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 fetch and read as the way to retrieve calls and exact inputs, providing an explicit path to invocation. It also lists exclusions such as no chain simulation and no safety verdict, so an agent knows when not to use it. It does not name specific alternative tools for those excluded jobs.

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

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

Annotations already declare readOnlyHint, idempotentHint, and no destructiveness. The description adds that the tool returns methodology, limits, official source links and fictional worked examples, contains no user records, and that the canonical URL should be cited. This goes beyond the annotations and clarifies the content and expected usage.

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

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

Three sentences, each with a distinct job: the action, the contents, and the citation instruction. The key action is front-loaded, and there is no redundant 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?

With an output schema and annotations covering the safety profile, the description covers what the tool returns, what it does not contain (no user records), and how to use the result (cite the canonical URL). Nothing an agent needs to call it correctly is missing.

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

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

Schema description coverage is 0%, so the description must compensate. It does by explaining that 'id' is the ID returned from search and refers to a public tool reference. The enum values are already in the schema, and the description tells the agent where to get the ID rather than how to construct it, which is sufficient for a single-parameter tool.

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 ('Retrieve'), a specific resource ('public tool reference'), and how to obtain the identifier (from search). It also enumerates the contents (methodology, limits, official source links, fictional worked examples), which distinguishes it from a generic fetch and from sibling search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies the workflow: call search first to get an ID, then fetch the reference with that ID. It does not explicitly name alternatives or provide when-not-to-use conditions, but the context is strong enough for an agent to select this tool correctly. The note 'no user records' also steers away from misuse.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by stating 'Parameters are processed remotely without application persistence,' clarifying no state is stored. It also explicitly notes the worksheet does not establish Sybil resistance or distribute tokens, which are important limitations not captured by annotations. This goes beyond what annotations provide, so a 4 is appropriate.

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 well-structured with a clear lead sentence stating the core function, followed by limitations and usage hints. It is moderately concise, though the list of exclusions ('No live subnet data, fraud detection, Bittensor emission replication, staking recommendation or token distribution') is a bit redundant. Overall, it is efficient and 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 calculation tool with an output schema, the description adequately covers purpose, limitations, and input sourcing. It mentions how to obtain exact inputs (via fetch or example resource) and clarifies it is a worksheet without persistence. While it does not detail output structure, that is covered by the output schema. It lacks explicit when-to-use guidance, but given the exclusions and clear scope, it is reasonably 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 coverage is 100%, so all parameters are described. The description adds relational meaning by explaining that customerRevenue and tokenSubsidy are separated, and that budget is allocated using 'accepted work and quality weights'—tying to contributors' acceptedTasks and quality fields. It also mentions 'Contributor caps' (capPercent) and 'exact budget conservation,' which clarifies how budget is handled. This exceeds the literal schema descriptions, warranting a 4.

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: it separates customer revenue from token subsidy and allocates a declared reward budget based on accepted work and quality weights. It also lists specific exclusions (no Sybil resistance, no token distribution) that distinguish it from sibling tools like those handling live subnet data or fraud detection. The purpose is unambiguous and the resource (Incentive Lab) is named.

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 implicit usage guidance through exclusions (e.g., 'No live subnet data, fraud detection, Bittensor emission replication') but does not explicitly state when to use this tool versus alternatives. It mentions retrieving inputs via fetch or reading an example resource, but this is about input sourcing, not tool selection. There is no explicit 'use this when...' guidance, so the guidance is implied but not fully actionable.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds important context beyond those: it is offline, calls no model or payment, performs no live requests, and processes parameters remotely without application persistence. This is exactly the kind of behavioral disclosure an agent needs to avoid misusing the 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?

The main function is front-loaded and clear, but there is redundancy: 'It is an offline planning tool; no model or payment is called' is immediately followed by 'Offline planning only. No live requests, model judge, payment, dynamic bandit or production routing.' This repeats the offline and payment exclusions without adding new information. Otherwise, the description is compact.

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

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

With an output schema present, full parameter schema, and strong annotations, the description does not need to explain return values. It covers offline behavior, non-persistence, and how to obtain exact inputs via fetch or an example resource, giving an agent sufficient context to call the tool correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds relational meaning by framing cost, success, and latency as assumptions, explaining that budget is checked against the worst-case total, and stating that feasible options are sorted by expected cost. This helps the agent understand how the parameters interact beyond their individual schema descriptions.

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

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

States a specific verb and resource: 'compares short supplier sequences' using cost, success, and latency assumptions, then checks worst-case total against budget and sorts feasible options by expected cost. It also differentiates itself from siblings by explicitly declaring it is an offline planning tool and excluding live requests, model judge, payment, dynamic bandit, and production routing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly scopes use to offline planning and enumerates exclusions: no live requests, model judge, payment, dynamic bandit, or production routing. It also names an alternative path for obtaining inputs: 'Retrieve route with fetch or read its example resource,' giving an agent clear when-to-use and when-not-to-use 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?

Beyond the readOnly, idempotent, and non-destructive annotations, the description adds meaningful behavioral detail: it preserves hashes, checks a latency SLA, treats file hashing as byte-only verification rather than proof of correctness, and states that parameters are processed remotely without application persistence. No contradiction with annotations exists.

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

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

The description is front-loaded and generally economical, but it has redundancy: 'it does not generate or verify a zero-knowledge proof' is immediately restated as 'No ZK proof generation or cryptographic verification.' The remaining sentences each add distinct caveats or usage context.

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 high complexity (15 required parameters) and the presence of an output schema, the description covers the essential call semantics, safety profile, input provenance, and limitations. It could mention failure behavior when the SLA/budget check fails, but that is a minor gap given the schema and 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?

Schema coverage is 100%, so the baseline is 3. The description adds useful semantic context by framing the numeric inputs as user measurements, tying the per-job latency target to the SLA field, and identifying model/input/verification-key hashes as preserved values; individual parameter formulas and units are still left mostly to the schema.

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

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

The description states a specific action and resource: it estimates proving compute and onchain verification costs from user-supplied measurements, checks a per-job latency target, and preserves hash inputs. It explicitly distinguishes itself from proof-generation tools by stating it does not generate or verify a zero-knowledge proof.

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 for when to use the tool: when you have your own measurements and want an integration cost plan. It also names exclusions (no proof generation/verification) and points to fetch or the example resource for exact inputs, though it does not fully route among all siblings such as compare_compute_costs.

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

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

Annotations already establish readOnly/idempotent/non-destructive behavior; the description adds useful context beyond them: 'Parameters are processed remotely without application persistence,' the snapshot/dated-review semantics, and the 'Five initial profiles' scope limit. No contradiction with annotations.

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

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

The opening sentence is informative and front-loaded, but the description becomes padded with a browser-vs-JSON-API comment, a redundant 'Five initial profiles' clause, and the vague 'Retrieve protocol with fetch or read its example resource' sentence. It is serviceable but not crisp.

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 strong annotations, complete input schema, and existing output schema, the description provides enough operational context: it describes the data scope, honors read-only semantics, and discloses non-persistence. The main remaining gap is not explicitly saying what the returned profiles are for, but that is largely covered by the tool name 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?

The input schema has 100% description coverage and already explains all four parameters, including enums and the 'preserve source text' instruction. The tool description adds no meaningful parameter-level meaning beyond the schema, so the baseline of 3 is appropriate.

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

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

The description clearly frames the tool as a 'free, dated reference' for payment, agent-identity, and token interfaces and states its limits ('no market prices, ... real-time protocol monitoring or paid endpoint'). It stops short of a crisp 'returns protocol profiles filtered by status/purpose/asOf' statement and does not differentiate itself from sibling read/search tools by name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'dated reference' framing plus explicit exclusions ('no market prices, exhaustive schema coverage, real-time protocol monitoring or paid endpoint') gives an agent usable guidance about when not to pick this tool. It also points to fetch/example resources for exact inputs. However, it never explicitly names sibling alternatives or states a clear 'use this when...' condition.

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

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

Annotations already mark readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds meaningful behavior: parameters are processed remotely without application persistence, there are no independent finality checks, and it separates records by state. These details go beyond the schema and annotations.

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

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

Three dense sentences with zero filler. The core comparison is front-loaded, followed by usage guidance, exclusions, and input retrieval hints. Every sentence earns its place.

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

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

Given the rich schema (100% parameter description, output schema, no nested objects) and strong annotations, the description covers the essential purpose, usage, and behavioral constraints. The phrase 'Retrieve reconcile with fetch or read its example resource to obtain exact inputs' is slightly cryptic but not a blocker. An agent can perform a local exception review with this definition.

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 arrays are fully documented, so the baseline is 3. The description's mention of 'exact decimal totals' and retrieving exact inputs via fetch/example resource adds minor context, but it doesn't elaborate on how invoices and transfers relate beyond what the schema already states. No need for more.

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

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

The description opens with a specific verb and resources: 'compares your declared invoices with recorded stablecoin transfers.' It further separates payments by category (partial, pending, wrong-network) and names exact decimal totals, giving a precise scope. The 'No wallet sync, automatic matching, FX...' line helps distinguish it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use it for a local exception review before updating your books' clearly states the intended scenario. The long exclusion list provides when-not-to-use signals, such as no FX or tax accounting, though no sibling tool is explicitly named as the alternative. This is good context but slightly implied rather than fully 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered structurally. The description adds meaningful behavioral context beyond that: parameters are processed remotely without application persistence, sample labels are deduplicated, and outputs cover failures/exclusions/uncertainty. It also discloses non-capabilities such as no reputation ranking, identity verification, or Sybil detection. No contradiction with annotations.

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

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

The core purpose and scope are front-loaded in the first sentence, with exclusions and usage pointers following. Nearly every sentence earns its place, though the negative list ('No public reputation ranking, identity verification, calibrated prediction or Sybil detection') partially overlaps with the earlier exclusion about not verifying reviewer identities. Minor redundancy keeps it from a 5.

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 tool with 5 required parameters, full schema coverage, and an output schema, the description covers the scope, intended review context, processing semantics, persistence behavior, and key limitations. The output schema presumably handles return-value details, so those need not be restated. The only minor gap is the ambiguous 'read its example resource' instruction, which could be more precise.

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

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

The input schema has 100% description coverage with per-parameter descriptions, so the baseline is 3. The description reinforces the task/version/date-window framing that maps to 'task', 'version', 'asOf', and 'maxAgeDays', but it does not add type, format, or constraint details beyond the schema. It earns the baseline but not more, since the schema already carries the parameter semantics.

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

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

The description states a specific verb and resource: it 'reviews imported task outcomes' scoped to 'one task, version and date window,' and enumerates concrete behaviors such as deduplicating sample labels and showing failures, exclusions, and uncertainty. It also differentiates itself from evaluation and identity-verification tools with explicit negative scope. This goes well beyond the title 'Agent Evidence' and gives an agent a clear operational identity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly frames the intended use case ('helps review a supplier comparison') and states what it does not do: run evaluations or verify reviewer identities. It also directs the agent to 'fetch' or 'read its example resource' for exact inputs. It stops short of naming specific sibling alternatives for excluded use cases, so the when-not-to-use guidance is clear but not fully mapped to alternatives.

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

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

Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds valuable behavioral context: bilingual (English and Chinese) name support, public scope, and the empty-query listing behavior. These details go beyond what annotations or schema provide without contradicting them.

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 clean sentences with no filler. The primary action is front-loaded, and the supporting usage details are kept minimal and relevant.

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

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

For a single-parameter search tool with annotations and an output schema, the description is complete: it defines the search scope, supported query forms, and the empty-query behavior. No essential information for invoking the tool correctly is missing.

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

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

Schema coverage is 0%, so the description carries the burden of explaining the query parameter. It clarifies that queries are names, that English and Chinese are accepted, and that an empty query has special meaning. It does not fully specify matching behavior (e.g., exact vs partial), but it compensates well for the schema gap.

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 ('Search') and resource ('this server’s public AI/Web3 tools and citation pages'), making the tool’s function immediately clear. It is distinct from all sibling tools, which focus on checking permissions, comparing costs, or reading profiles rather than searching a corpus. The title and description align well.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly indicates when to use the tool: when searching for AI/Web3 tools or citation pages on this server. The note about empty query listing available worksheets provides a concrete usage scenario. It does not explicitly name alternatives or exclusion conditions, so it misses the top score.

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