Losbeto — Market Data for AI Agents

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It conveys that this is a read-oriented status check ('Check') and lists returned information, but does not state whether the call has side effects, what happens on failure, or any cost/latency implications. For a simple status check this is adequate but it leaves behavioral nuance unstated given zero annotation coverage.

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

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

Two packed sentences cover purpose and return contents, followed by a usage trigger. The purpose is front-loaded and each sentence carries weight. The final trigger sentence adds value but its logic is slightly tangential, so it is concise but not perfectly economical.

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 tool with no output schema, the description covers the essential return values (subscription status, credit, expiry, subscribe instructions) and a usage condition. Minor gaps remain around return format and failure behavior, but the agent has enough to 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 tool has zero parameters, so per the baseline this scores 4. The empty schema plus 100% (vacuous) coverage means there is nothing for the description to explain — it correctly avoids mentioning parameters that do not exist.

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, specific action: check subscription status, credit remaining, and expiry date for this connection. It is clearly distinguishable from the market-data siblings (get_market_data, market_snapshot, etc.) because it addresses account/subscription state rather than market data. The verb 'Check' plus the explicit resource makes the purpose unambiguous.

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

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

Provides an explicit trigger scenario: 'Call this if a request returned delayed data and you need live.' This is a clear when-to-use condition. However, the rationale is somewhat confusing — checking account status to resolve delayed data is not immediately obvious, and no when-not-to-use guidance or alternative tools are named, though siblings are not genuine alternatives here.

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

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

No annotations are present, so the description carries the full behavioral disclosure burden. It does this well: it reveals that without a subscription the tool returns a real but delayed sample, that live data requires an active subscription key, and that live data can also be purchased per call in USDC via x402. This is valuable operational context an agent needs to predict what the call will do.

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

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

Three short sentences, with the core action front-loaded and the subscription/cost details following. There is no filler, and each sentence adds distinct information: what the endpoint is, what a free call returns, and what a paid/live call involves.

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

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

For a two-parameter fetch tool with no output schema, the description is largely sufficient. It explains the data mode, cost, and how to preview the response shape. The main gap is that it mentions an active subscription key without specifying how the key is supplied (header vs parameter), which an agent may need to know for a real live-data 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 schema already documents both parameters at 100% coverage, so the baseline is 3. The description adds the meaningful constraint that the endpoint value must come from search_market_data, but it doesn't explain parameter syntax or formats beyond what the schema already provides.

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

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

The description opens with a specific verb-resource pair: 'Fetch data from an endpoint returned by search_market_data.' This clearly distinguishes it from the search sibling and makes the tool's role as a follow-up fetch operation immediately obvious. While it doesn't compare against every sibling, 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?

It states when to use the tool: after search_market_data has returned an endpoint, and it explains the subscription-based branches (sample vs live data). It doesn't explicitly say 'do not use this to search for endpoints,' but the precondition 'endpoint returned by search_market_data' makes the intended workflow 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?

With no annotations, the description carries the full burden. It discloses what the tool returns (categories, counts, cheapest price) which implies a read-only listing behavior, but it does not explicitly state safety characteristics, limits, or side effects. For a zero-parameter tool this is adequate but not rich, so a mid-range score 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 a single, concise sentence that front-loads the core output ('catalog at a glance') and then provides usage context. Every word earns its place, with no fluff 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?

For a simple zero-parameter tool with no output schema, the description is nearly complete: it tells the agent exactly what will be returned and when to use it. It could explicitly note that it is a read-only, non-mutating operation, but given the lack of complexity, that omission is minor.

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% (trivially). Baseline for zero parameters is 4, and the description adds no parameter-specific meaning because none are needed. It does not repeat schema information, so it meets the baseline.

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

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

The description states a specific verb (list) and resource (categories), and elaborates on the exact output: every category, endpoint count, and cheapest price. It also hints at its role as an overview tool, distinguishing it from search_market_data without naming it. This goes beyond a tautology.

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 clear use context ('when you want to know what this service covers before searching') that implicitly positions it as a precursor to search operations. However, it does not explicitly name alternatives or specify when not to use it, so it stops 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 full burden. It is transparent that the tool aggregates multiple data categories into one snapshot, and it details the content per scope. It also makes the tool's substitute-for-many-calls nature explicit, though it does not describe output shape or potential response-size tradeoffs.

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 with no filler. It lead: an opening value proposition, a necessary content breakdown per scope, and a closing usage rule. Every line earns its place and the structure makes the key guidance easy to scan.

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

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

For a tool with one enum parameter and no output schema, the description is quite complete: it names all three scope options, describes their returned categories, and clarifies the intended use case. A minor gap is that the response format, field structure, or "definitives" size are not described, but that is not essential for deciding when to call this simple tool.

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

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

The schema already covers 'scope' at 100%, but the description adds real semantic value by mapping each enum value to concrete response contents (e.g., 'brazil' returns central-bank macro, the real interest rate, Ibovespa, and B3 blue chips). This goes beyond the schema's simple 'Which area to snapshot' and helps an agent decide which scope to pass.

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, meaningful claim: this tool returns a whole-area snapshot in one call. It backs that up by enumerating exactly what each scope ('brazil', 'global', 'crypto') returns, so an agent can distinguish this tool from get_market_data or search_market_data without opening the schema.

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

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

It explicitly says to use this when the question is broad rather than about one number, which establishes both a when and a when-not. It does not name the specific sibling alternative for single-number queries, so it falls just short of fully explicit guidance.

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

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

With no annotations provided, the description carries the full burden of disclosing behavior. It states clearly that the tool returns 'the endpoints that cover it, with their price and parameters,' which sets expectations for output structure and scope. It also enumerates covered asset classes (Brazil, US equities, forex, etc.) and includes examples that hint at query handling (e.g., 'is this Solana token a rug pull'), which discloses some interpretation behavior. However, it does not mention read-only guarantees, error cases, or query limits, though these are less critical for a search 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 description is highly scannable, opening with an attention-grabbing 'FIRST STEP' followed by a clear one-sentence summary. The list of coverage areas and examples are useful but slightly long, making it a bit verbose for a tool description. Overall, it is well-organized and every sentence adds value, though some examples could be trimmed without loss.

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 search/discovery tool with no output schema and no annotations, the description provides sufficient context for an agent to invoke it correctly: it explains what to input, the expected response (endpoints with price and parameters), and the supported domains. It does not detail the response format for endpoints (e.g., JSON structure) but given the tool's role as a router, this level of detail is adequate. The absence of output schema is compensated by the explanatory text.

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 already provides clear descriptions for both parameters ('query', 'limit'), giving 100% coverage. The description goes beyond the schema by offering realistic query examples and clarifying the expected style of input ('in plain language'), which helps an agent formulate effective queries. The limit parameter is not explicitly addressed in the description, but the schema covers it, so the baseline of 3 is elevated by the practical examples.

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

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

The description clearly identifies the tool as the 'FIRST STEP for any market-data question,' with a specific verb (search) and resource (market data), and explains it returns endpoints with price and parameters. It differentiates from siblings by positioning itself as the entry point for discovery. The inclusion of specific coverage areas and multiple plain-language examples leaves no doubt about its function.

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

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

The phrase 'FIRST STEP' implies usage as a starting point, but the description does not explicitly state when to prefer this over siblings like get_market_data or market_snapshot. There are no exclusionary criteria or alternative-route guidance for an agent that already has an endpoint ID and needs raw data. The usage context is implied through examples but not directly contrasted with alternatives.

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