Tokenized Stocks & Agent Data (x402)

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

With no annotations, the description carries the full behavioral burden and does so thoroughly: it discloses the fixed data scope, cost ($0.005 via x402), payment rails, unknown-id 404 behavior, and that output shape matches /ai-progress/latest. This goes well beyond what annotations alone would have provided.

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?

Dense but every clause earns its place: front-loaded purpose, sibling differentiation, return-shape comparison, error behavior, cost, and HTTP twin. No filler or tautology; the length is justified by the amount of essential operational 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?

Despite having no output schema, the description compensates by linking the return shape to /ai-progress/latest, specifying the series count, and covering errors and cost. For a single-parameter dated-lookup tool, an agent has enough information 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 coverage is 100% and the parameter already has a clear description ('reading date YYYY-MM-DD, from the free menu'). The prose repeats the free-menu source and adds error behavior, but does not meaningfully extend the schema's 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 precise purpose: retrieving AI progress 'as it stood on a past date' and enumerates the 129-series composition. It also names /ai-progress/latest and /ai-progress/what-changed, making it easy to distinguish this tool from 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?

Explicitly says 'Use this when you need AI progress as it stood on a past date' and points to /ai-progress/what-changed for diffing instead. The guidance is directly actionable and gives the agent a clear decision rule among the sibling 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 of behavioral disclosure. It transparently states the output format ('One JSON row per series: latest and previous value with dates, % change, signal quality, source URL'), the cost ('$0.005 USDC'), a free preview ('Free 3-row sample at /preview'), and an HTTP twin. It does not mention rate limits or authentication, but the disclosed behaviors are substantial and beyond typical.

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

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

The description is dense but each sentence adds value: purpose, content categories, output format, cost, preview, and alternate access. It is front-loaded with the primary purpose and structured logically. While not the shortest, it avoids fluff and is appropriately sized for the amount of essential 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?

Since there is no output schema, the description must explain return values, and it does ('One JSON row per series...'). It also covers cost, preview, and an HTTP alternative. It does not address error handling or rate limits, but for a single-call data retrieval tool with no parameters, the description is sufficiently complete for an agent to invoke it correctly.

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

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

There are zero parameters, so the schema coverage is trivially 100%. The rubric sets a baseline of 4 for zero parameters, and the description does not need to add parameter details. It does provide context about the output and usage that indirectly explains what the tool returns, but no parameter semantics are necessary.

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 ('get the current state of AI progress') and a clear resource ('latest reading of 129 public series'), listing concrete categories. It distinguishes itself from siblings by emphasizing 'latest' and 'current state', which contrasts with ai_progress_as_of and ai_progress_what_changed. The purpose is unambiguous and action-oriented.

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 opens with 'Use this when you need the current state of AI progress in one call', providing clear context for when to use it. However, it does not explicitly mention alternatives or when not to use it, such as pointing to ai_progress_as_of for historical data. This is a clear usage context without exclusions, fitting the score 4 criterion.

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 disclosure burden and does meaningful work: it reveals the $0.01 cost, the x402 payment rails, the HTTP twin, and the date-id source. It does not discuss read-only behavior or error cases, but the important operational traits are disclosed.

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

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

The description is dense but every sentence serves a purpose: usage trigger, output contents, date id format, cost, and HTTP alternative. There is no filler and the main use case is front-loaded in the opening sentence.

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 two-parameter tool with no output schema, the description explains the return contents well enough and covers cost and id provenance. It lacks an explicit response example or error/ordering constraints, but an agent has enough context to select and invoke the tool correctly.

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

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

The schema already describes from/to as earlier/later reading ids, and schema coverage is 100%, so the baseline is 3. The description adds the YYYY-MM-DD format and the fact that ids come from the free menu at '/', which meaningfully clarifies how to obtain valid parameter values.

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 does: it reports what moved between two dates across 129 AI-progress series, with values, changes, and new observations. It is distinguishable from sibling tools by the explicit between-two-dates framing, though it never names a sibling directly.

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 first sentence gives an explicit 'Use this when...' trigger, namely when the question is what moved in AI progress between two dates. It does not explicitly state when not to use it or name alternatives, 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 burden. It adds useful behavioral context: the result is free and machine-readable, and it explicitly notes that some things are not verified. However, it does not disclose output format, latency, error behavior, or whether the data is static or dynamic.

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

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

The description is a single front-loaded sentence that lists key content areas without padding. The word 'Free' is a minor extra, but it is short and does not detract from readability.

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 a simple, parameterless metadata tool, naming the main content areas. However, without an output schema it does not specify the return format well enough for an agent to know exactly how to interpret 'machine-readable' output, and 'per product' is ambiguous given no product parameter.

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 the schema is fully covered, so the description does not need to explain parameter meaning. The phrase 'per product' hints at context, but with an empty schema there is nothing to clarify.

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 identifies a specific resource: machine-readable limits per product, and enumerates the covered dimensions (sources, freshness, cache, rate limits, failure modes, unverified items). It is not a tautology and is distinguishable from sibling tools like health or menu, though it lacks an explicit verb such as 'retrieve' or 'list'.

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 is given about when to call this tool versus alternatives, nor any conditions or exclusions. The content list implies it is for checking product limits, but the description does not explicitly state that or mention any sibling 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 of behavioral disclosure. It mentions 'Free' and 'Liveness' but does not explain what 'settled-call counters per route' means concretely, nor does it disclose rate limiting, response format, or error behavior. The description adds minimal context beyond the name.

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

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

The description is extremely concise, using a single sentence with no filler. It front-loads key information ('Free', 'Liveness') and uses efficient punctuation. It earns a high score for efficiency, though it could add a bit more context without much bloat.

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 simplicity (0 params, no output schema), the description is thin but might be minimally sufficient for a basic health check. However, it lacks detail on what 'settled-call counters' actually measure, and with no annotations or output schema, an agent lacks understanding of return values. More context is needed to be fully 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?

The tool has 0 parameters, so the schema provides nothing. The description adds meaning by explaining what the tool reports (liveness and counters), which is necessary since there are no parameters to document. This is a baseline 4 for a no-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 clear purpose: it reports liveness and settled-call counters per route. However, it does not explicitly differentiate from siblings like 'st0x_pulse' or 'st0x_registry', which might also provide health-like status. The purpose is clear but sibling differentiation is missing.

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 tool versus alternatives. It does not mention any exclusions or specific scenarios. For a health-check tool, one might expect instructions like 'Use when checking service availability' or 'alternative for detailed metrics', but none are provided.

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 supply useful context: the tool is free, returns a list-style catalog, and is positioned as an entry point. However, it does not explicitly state side effects, authentication needs, or response shape, though the read-only nature is strongly implied.

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 packs the cost, scope, fields, and a directive to start here. No word is wasted and the key guidance 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?

For a zero-parameter menu tool with no output schema, the description fully covers what the agent will receive (products, prices, chains, usage guidance, URL) and when to use it. 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?

The tool has zero parameters and an empty schema, so there are no parameter semantics to document. Baseline 4 applies; the description does not need to add parameter 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?

Description clearly defines the tool as a menu of every product the server sells, including price, chains, selection guidance, and HTTP URL. This is a specific resource with no ambiguity and is immediately distinguishable from sibling tools by its catalog scope.

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?

"Start here." explicitly tells the agent to use this tool first, providing clear situational context. It does not name sibling alternatives or state when not to use it, so it falls just short of full exclusion guidance.

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

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

There are no annotations, so the description carries the burden of behavioral disclosure. It adds useful context that the operation is free and that the receipt id originates from every paid call, and 'look up' implies a read-only operation. However, it does not describe behavior for invalid or missing ids, nor does it explain the format or usage of the verification key.

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

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

The description is a single concise sentence that front-loads the 'Free' signal and immediately states the operation, target resource, and return value. There is no wasted wording.

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

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

For a simple one-parameter lookup tool with no output schema, the description names both the input (receipt id) and the expected return elements (receipt and verification key). It is slightly incomplete about error handling or how the verification key should be used, but enough for an agent to invoke it correctly.

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

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

The schema only defines 'id' as a UUID with a pattern, and schema description coverage is 0%. The description compensates by explaining that this id is the delivery receipt id returned from paid calls and that the tool returns the verification key alongside 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?

The description uses a specific verb ('Look up') and a clear resource ('signed delivery receipt') plus the associated 'verification key'. It also distinguishes itself from the sibling tools by describing a receipt-lookup workflow that is not covered by health, menu, same_stock, or the st0x_* family.

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 note that the id is 'returned with every paid call' gives the agent clear context for when to invoke receipt: after executing a paid call. It lacks explicit exclusion statements or named alternatives, so it falls just short of an explicit when-not/alternative distinction.

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 $0.05 cost, the verification of contracts against issuer registries, and the scope of results, which gives the agent a solid understanding of side effects and outputs. However, it does not explicitly state whether the operation is read-only or mention rate limits, though the nature of the tool implies a non-destructive data lookup.

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

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

The description is a single dense paragraph listing many return fields, cost, and an HTTP twin. While every sentence adds value, it is not concise; a bulleted list or separation of core purpose from details would improve scanability. It is front-loaded with the use case, but the volume of information makes it less concise than ideal.

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's complexity and lack of an output schema, the description is thorough: it enumerates the specific data points returned (genuine contract, live price, premium/discount, pool depth, volume, spread, impostor tokens) and mentions edge cases (xStocks not on Base). It also provides cost and an HTTP twin. It lacks error-handling details, but for a data lookup tool this is sufficient.

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

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

The schema already describes the ticker parameter and its accepted forms (stock ticker, ST0x symbol, Coinbase symbol). The description adds examples (NVDA, AAPL, TSLA) but does not introduce new meaning beyond the schema. With 100% schema coverage, the baseline of 3 applies.

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

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

The description opens with a specific use case ('need the same stock from every issuer on Base in one call') and enumerates the exact data returned, making the tool's purpose unambiguous. It clearly distinguishes itself from any tool that would return a single issuer's stock or a different aggregation.

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 states a clear trigger condition ('Use this when you need the same stock from every issuer on Base') but does not explicitly name alternative tools or state when not to use it. It implies usage for cross-issuer equity queries, but lacks explicit exclusions or comparisons 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?

With no annotations, the description carries the full behavioral burden hydra, and it does so well: it discloses that the grade is rule-based and reproducible, that the output is entertainment and not trading advice, and that the call costs $0.02 via x402 with an HTTP twin for direct access. It also enumerates all returned facts, making side effects and response contents clear 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 front-loaded with a practical use-case sentence and immediately names the verdict categories. The rest is a tight, well-structured list of returned facts plus payment and HTTP-alternative details; every sentence earns its place and none is redundant with 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?

For a single-parameter endpoint with no output schema, the description is remarkably complete. It specifies the input, the output grade categories, the underlying facts, the pricing/billing behavior, and the direct HTTP alternative, giving an agent everything it needs to decide and invoke 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 coverage is 100%, so the baseline is 3; the mint parameter is fully described with type, pattern, and base58 explanation. The description adds only the mild constraint that exactly one mint address should be sent and provides the URL template, which does not materially elevate the semantics 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 opens with the exact question this tool answers ('is this Solana token safe, a joke, or a rug?') and names a concrete resource: one Solana token mint address. It also describes the output with specific grade categories and supporting facts, making the tool's function and scope 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?

The description provides an explicit 'Use this when...' trigger statement and describes the natural-language intent it serves. It does not explicitly mention when not to use it or name alternatives, though the sibling tools do not appear to overlap with this functionality.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it excels. It details live data sources (deepest USDC pool, last Raindex fill), corrections (ERC-4626 wrapper rate), additional outputs (market session, leg age, pool depth, cross-issuer pool, one-sentence read), error behavior (404 for unknown ticker, no charge), and pricing ($0.02 via x402). This is comprehensive and leaves little ambiguity about what happens when the tool is invoked.

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

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

The description is a single dense paragraph, but every clause serves a purpose. It front-loads the trigger condition, then enumerates the computation and outputs efficiently. While it is longer than some, the information density is high and there is no fluff. It earns a 4 for being appropriately structured and informative without excessive verbosity.

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's complexity (multiple data sources, adjustments, and a rich set of outputs) and the absence of an output schema, the description provides a thorough account of what the agent will receive: premium/discount in bps, session, age, depth, cross-issuer info, and a one-sentence read. It also covers cost, payment rail, and error cases. Nothing essential is missing for correct invocation and expectation setting.

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

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

Schema description coverage is 100%: the schema already documents the ticker parameter with allowed forms (stock ticker, ST0x symbol, Coinbase symbol) and examples. The tool description adds minor illustrative examples ('wtAAPL, FGI') but does not introduce new meaning beyond the schema. Since the schema fully documents the parameter, the baseline of 3 applies; the added examples are marginal and do not push it higher.

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 precise purpose: determine whether an ST0x tokenized stock trades above or below the real share, returning a premium/discount in basis points. It specifies the resource (ST0x on Base) and clearly differentiates from siblings like st0x_pulse (likely a market snapshot) and st0x_registry (likely a listing tool). The verb 'send' and expected output give unambiguous intent.

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 opens with an explicit trigger: 'Use this when the question is whether an ST0x tokenized stock on Base trades above or below the real share.' This is a clear when-to-use statement. However, it does not explicitly mention when not to use it or name alternative tools, though the sibling list implies some are related. The cost and error handling are noted, but the exclusion guidance is absent, so it falls 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 full responsibility. It discloses that it reads from public subgraphs and RPC (implying read-only), refreshes every 5 minutes, costs $0.01, and provides an HTTP twin. It does not explicitly state 'no writes' but the phrase 'Read straight from...' and the nature of the data strongly indicate a non-destructive query.

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 efficient and front-loaded: the first sentence captures the core use case and output details, followed by data source, refresh, cost, and an alternative endpoint. Each sentence adds distinct value with no filler or repetition.

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 no parameters, no output schema, and no annotations, the description is fully self-sufficient. It enumerates the output fields, explains the data source and freshness, notes the cost, and provides an HTTP fallback. Nothing critical is missing for an agent to decide to call it and interpret 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 tool has zero parameters and schema coverage is 100% (empty schema), so the baseline is 4. The description adds no parameter information because none is needed, and it does not create any ambiguity about inputs.

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 ('know what is actually trading'), a precise resource (tokenized stocks on Base on ST0x's DEX Raindex), and the exact metrics returned (USDC volume, trade count, last price, vault supply, wrapper rate). It is clearly distinct from siblings like st0x_registry or st0x_premium by focusing on live trading data rather than registry or premium info.

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 opens with an explicit 'Use this when you want to know...' condition, giving clear context for when to invoke it. It does not explicitly state when not to use it or name alternatives, but the purpose is specific enough that an agent can infer it is the right tool for live market data on tokenized stocks.

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 discloses that the data is versioned, refreshed hourly, and costs $0.001 (with payment details), plus an HTTP twin for alternative access. This is valuable behavioral context beyond a simple registry lookup, though it doesn't explicitly state read-only status.

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

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

The description is dense but front-loaded with the use case, then efficiently lists registry contents, cost, and HTTP twin. Every sentence adds useful information without unnecessary fluff, though it is somewhat lengthy due to the detailed data field enumeration.

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 no output schema and no parameters, the description does a good job of specifying what the registry contains (wrapped/unwrapped addresses, ERC-1155 vault, ISIN, TradingView symbol, listed flag) and provides cost and update frequency. It doesn't detail the output format, but the agent can infer it returns a structured list. Missing only explicit instructions on how to interpret or use the returned data.

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, so the baseline is 4. However, the description's phrasing ('resolve a stock ticker', 'check whether a contract') implies an input, yet the schema is empty. It does clarify it's a 'complete issuer registry' for all names, suggesting it returns a full dump, but the mismatch between implied input and no parameters could confuse an agent. The description doesn't compensate for this ambiguity.

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: resolving a stock ticker to its ST0x on-chain token or verifying a contract. It identifies itself as the complete issuer registry across four chains, listing key data fields. This differentiates it from sibling tools like st0x_pulse or st0x_premium, which likely handle market data or premium features.

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 opens with 'Use this when you need to...' and specifies two concrete scenarios (ticker resolution and contract verification). While it doesn't explicitly mention alternatives, the conditions are clear enough for an agent to decide when this tool is appropriate.

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