TokenBank — tokenized real-world assets

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

Annotations already establish readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description complements this by revealing what the comparison covers (the decision fields), which is useful behavioral context beyond the annotations. No contradiction with annotations.

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

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

Two sentences, no filler. The core behavior and fields are front-loaded, and the usage guidance is compactly appended. Every sentence earns its place.

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

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

For a read-only, single-parameter tool with an output schema and rich annotations, the description is complete enough: it states what is compared, which fields matter, when to use it, and which sibling to avoid. No critical information 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 100% and there is only one parameter (tickers), so the schema carries the main burden. The description's 'two or more instruments' aligns with minItems=2 and adds mild semantic color, but it does not add new 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 states a specific verb ('Put ... side by side') and resource ('instruments') and enumerates the comparison fields (yield, risk, minimum, etc.). It differentiates from get_product_details by naming it as the alternative, but it does not distinguish itself from the other compare_* siblings like compare_tokenized_access or compare_with_savings_account.

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 gives a usage trigger ('X vs Y' questions) and names get_product_details as the inferior alternative. It lacks when-not-to-use guidance relative to the other comparison-focused siblings, but the main routing information is clear.

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 useful context by warning that exact counts come from the response and that figures in the description should not be quoted. It also explains why results can differ between tokens on the same security, adding meaningful behavioral nuance.

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 purpose is front-loaded, and the additional context around eligibility differences and the count-warning earns its place. The description is longer than average but every sentence contributes to correct selection or invocation.

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

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

Given a full parameter schema, rich annotations, and an existing output schema, the description supplies the essential decision context, a clear scope definition, and practical usage advice. An agent has everything it needs to call this tool correctly and know what to do with the result.

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 5 parameters at 100% with clear descriptions and examples, so the heavy lifting is already done there. The description mostly re-states the 'omit query to list all' behavior and adds no new parameter-level semantics 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 the tool's specific behavior: for a security tokenized by more than one issuer, it returns every token with a verdict on who may buy each. It goes beyond a generic comparison by emphasizing eligibility differences, reinforcing its unique purpose versus sibling tools.

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

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

The description explicitly identifies the target question ('which tokenized Apple should I buy?'), calls itself 'the right tool' for it, and instructs agents to call it before recommending any tokenized stock. It does not name specific sibling alternatives or state when-not-to-use it, 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?

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint false). The description adds the behavioral nuance that output includes 'honest trade-offs, not just the bigger number', but does not disclose other behavioral aspects like data freshness or rate limits. This is a modest but useful addition, earning a mid-range score.

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 exactly two sentences, both substantive and free of fluff. The primary action is front-loaded, and the second sentence adds practical value about output quality. No 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?

Given the presence of an output schema and two optional, well-described parameters, the description covers the essential purpose and output character. It does not discuss prerequisites or edge cases, but for a simple read-only comparison tool it is sufficiently 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%, with both eu_only and bank_rate_pct having clear default behavior in the schema. The description does not add parameter-specific meaning, 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 uses a specific verb ('compare'), a clear resource ('low-risk, instant-access crypto savings instruments'), and a well-defined point of comparison ('a typical bank savings account rate'). This distinguishes it from siblings like compare_assets by explicitly scoping the comparison to bank savings accounts.

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 intended use case: when you want to compare crypto savings instruments against a bank savings rate. It provides clear context but does not explicitly name alternatives or state when not to use this tool, so it falls just 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?

Annotations already declare readOnlyHint and idempotentHint, covering the safety profile. The description adds meaningful behavioral nuance: it is 'Strict by design' and excludes instruments with merely unverified access, and it explains the distinction between mint and buy gates. This goes beyond the annotations and helps the agent understand filtering semantics.

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 sentences, with the primary purpose stated first and the nuanced reasoning second. No wasted words, front-loaded and efficient.

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 explains the core concept and the strict filtering behavior. An output schema exists, so return format is covered. The only gap is explicit guidance on when to use this tool vs its siblings, but given the name and description, an agent can likely infer the use case. Overall, it is complete enough for correct invocation.

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

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

All six parameters are fully described in the input schema (100% coverage), so the description does not need to add parameter details. The description does not describe parameters directly, but it does imply the include_unverified behavior via 'Strict by design'. Since the schema covers everything, 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 states the tool's purpose: finding instruments a self-custody wallet can buy without KYC and hold itself. It distinguishes itself from a token list by explaining the mint vs. buy gate nuance, which is specific and unambiguous.

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

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

The description provides context about the question it answers (permissionless assets for self-custody) and explains the strict filtering behavior, but it does not explicitly mention when to use this tool over siblings like search_yield_products or find_tokenized_security. No exclusions or alternative tools are referenced, leaving the agent to infer usage from the name and context.

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

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

The description goes far beyond the readOnly/idempotent annotations by disclosing important behavioral traits: results may contain multiple legitimate issuances of the same company, authority varies by source, aggregated rows lack eligibility/KYC/custody claims, and missing yield/minimum/risk fields should be treated as absent rather than zero. 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 long but every section earns its place: purpose, search coverage, critical usage caveats, and the alternative tool. The 'THREE THINGS MATTER' structure is front-loaded with the most decision-relevant caveats. It could be tightened slightly, but it is structured and purposeful rather than 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 tool with rich semantics, complex source nuances, and an existing output schema, the description covers everything an agent needs: what to search, how to interpret multiple issuances, how to distinguish authoritative sources from observations, what fields are intentionally absent, and which sibling tool to use for rate products. Nothing material 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 100%, so the baseline is 3. The description adds meaningful semantics around the query field (company name, ticker, or ISIN) and the conceptual difference between issuer catalogues and aggregated tiers, which clarifies how issuer filtering should be interpreted. It does not deeply explain line, limit, or offset, but those are already well documented in 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 opens with a specific, answerable question: 'Does a tokenized version of a given company, ETF or bond exist, and from which issuer?' and specifies the search scope (company name, ticker, or ISIN) across named sources. It clearly distinguishes this from sibling tools by saying 'Use this BEFORE saying a stock is not available on-chain' and later routing to search_yield_products for rate comparisons. This is a clear verb+resource+scope definition.

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

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

Contains explicit usage direction: 'Use this BEFORE saying a stock is not available on-chain' and 'For instruments that do carry a comparable rate, use search_yield_products instead.' It also gives practical warnings about when results should not be treated as authoritative (aggregated rows are not availability answers) and how to report eligibility. These are concrete when-to-use and when-not-to-use 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?

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds substantial behavioral context: the dataset excludes markets other Western aggregators miss, gives examples of hidden markets, explains legal-recognition phasing and venue-level retail caps, and clarifies that the tool returns coverage along with reasons. This goes well beyond the annotations.

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

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

The description is front-loaded with purpose and ends with a clear usage instruction and omit-country reminder. However, the middle section contains lengthy country-specific facts (Japan's ¥1tn consortium chain, Korea's 2027 legal recognition, India's lack of stablecoins) that are illustrative but not necessary for an agent to select and invoke the tool correctly.

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 lightweight 2-parameter schema, 100% parameter coverage, an output schema, and annotations covering safety, the description is complete enough. It explains what the tool reveals, why it matters, when to call it, and how to request all markets by omitting country. Nothing needed to invoke 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 100%, and both country and readable_only are already well documented in the input schema, so the baseline is 3. The description reinforces the omit-country behavior and gives country examples, but it does not add meaningful semantic detail for readable_only beyond what the schema already states.

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

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

The opening phrase 'Which countries this dataset can see into, which it cannot, and why' clearly identifies a specific verb, resource, and unique scope. It distinguishes the tool from a 'general search', but does not explicitly name or contrast sibling tools like get_market_stats or compare_tokenized_access, so sibling differentiation is only implicit.

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

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

The description gives strong when-to-use guidance: 'Call this before stating what a market holds, or before quoting any total as if it were the world.' It also says the tool should answer country-tokenization questions 'rather than from a general search.' However, it does not name specific sibling tools or state when not to use this tool in favor of one of them.

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

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds useful behavioral context by noting that the figures include freshness information and availability dimensions like EU and KYC-free status, going beyond the annotations 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?

Two compact sentences: the first lists the returned dataset dimensions, and the second states the intended usage. Every word earns its place, with no redundancy or filler.

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

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

For a parameterless, read-only tool with an output schema, the description covers the key output dimensions and the primary use case. It does not explicitly contrast with get_market_coverage, but the usage guidance sufficiently orients the agent for a correct first 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?

The tool has zero parameters and schema description coverage is 100%, so there is no parameter documentation burden. The baseline 4 applies because there is nothing for the description to add.

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 what the tool provides: a dataset summary with instrument counts split by pillar, category, risk, EU availability, KYC-free buyability, and data freshness. It is specific and informative, but it does not explicitly distinguish itself from the get_market_coverage sibling, 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?

The description gives an explicit use case: 'Use to ground a claim about coverage before making one.' This provides clear context for when to call the tool, but it does not mention when not to use it or name alternative tools, so it lacks the full exclusionary guidance required for a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds valuable context: case-insensitive ticker matching and the specific fields returned (yield, risk, custody, availability, how to buy, source link, verification date). It also confirms this is a single-record retrieval, not a list operation. No contradictions with annotations.

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

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

The description is one concise sentence, front-loaded with the primary purpose ('Full record for one instrument by ticker'), followed by a compact list of the record's contents. No filler, no redundancy with annotations or 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?

Given the tool has a single parameter with a documented example, a full output schema (so return format is already defined), and annotations covering safety and idempotency, the description provides everything an agent needs: the scope, the lookup method, case-insensitivity, and the set of fields. Nothing essential is missing.

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

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

Schema coverage is 100% for the single ticker parameter, so the baseline is 3. The description adds meaning beyond the schema by noting case-insensitivity and explicitly enumerating the fields included in the full record, which helps the agent understand what the result will contain. This adds value without being redundant.

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 clear verb-resource pair: 'Full record for one instrument by ticker' – it names the specific resource (instrument) and the access method (by ticker), with case-insensitivity noted. It clearly distinguishes itself from sibling tools that search or compare multiple assets, since this returns a full record for a single instrument.

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: use this when you need the complete record for a single instrument identified by ticker. It doesn't explicitly name alternatives or exclusions, but the context is clear that this is the go-to for full details on one product, as opposed to comparison or search tools. No misleading guidance.

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

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

Annotations already communicate read-only, idempotent, non-destructive behavior, so the description's extra value comes from explaining the net rental yield formula and the concrete attributes returned (token price, monthly rent, occupancy). This adds operational context beyond the structured annotations 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 tight: two sentences, with the core purpose and the most important computed metric front-loaded, followed by direct usage guidance. Every sentence adds information and none repeats 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?

Given the rich input schema, output schema, and annotations, the description covers what an agent needs: scope, use case, metric definition, and result content. It does not need to restate return formatting or pagination because those are already present in the structured definitions.

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 input schema already documents all seven parameters thoroughly. The description reinforces the meaning of yield by giving the formula, but it does not need to add per-parameter detail; this is 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 clearly identifies a specific verb-resource pair: drilling into the RealT portfolio to inspect individual tokenized rental properties. It also distinguishes itself by framing the object as the properties behind the REALT entry and naming the specific metrics returned, so an agent can separate it from sibling search tools.

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

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

The description gives explicit use guidance: use this when someone asks which specific tokenized property to buy, or when examining the yield/price spread within an issuer. It does not explicitly name alternatives or state when not to use it, but the context is clear enough to route an agent correctly.

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, covering the safety profile. The description adds meaningful behavior beyond annotations: it explains the default tier and what each tier contains, and notes that output is compact records requiring get_product_details for full detail. This adds useful context without contradicting annotations.

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

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

The description is three sentences, each adding value: purpose, tier distinction, and pointer to sibling. No fluff, front-loaded with the core purpose. Efficient and well-structured.

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 complex search tool with 8 parameters and an output schema, the description covers the high-level decision context (what each tier is for and when to use get_product_details). It doesn't enumerate all parameters or return format, but the schema and output schema cover those. The description is complete enough for an agent to select and invoke it correctly.

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

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

Schema description coverage is 100% and each parameter (especially tier) has a detailed description with usage guidance. The main description adds no parameter-specific semantics beyond what the schema already provides; it only frames the overall tool purpose. Baseline 3 is appropriate because 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?

The description clearly states the tool's function: 'Search and filter TokenBank's instruments for earning yield with crypto.' It specifies the verb (search/filter), resource (TokenBank's yield instruments), and scope (crypto earning). It also distinguishes between the curated and imported tiers, making it distinct from sibling tools like get_product_details, which is explicitly mentioned as the follow-up tool.

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 tells the agent when to use which tier: curated for yield questions ('what does this pay'), imported for identity questions ('is X tokenized') and never for yield rankings. It also directs to get_product_details for full records, creating a clear decision path among siblings.

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