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. It mentions 'FREE' and returning live data, but does not explicitly state the operation is read-only, potential error cases, or authentication requirements. For a simple status check, this is adequate but not thorough.

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. It packs purpose, additional info, and usage guidance into two sentences without redundancy.

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

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

For a zero-parameter tool with no output schema, the description fully explains return values and provides a usage trigger. Nothing critical for calling 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?

The tool has zero parameters and the schema is empty, so the description must explain outputs. It lists subscription status, credit, expiry, and subscribe plans, effectively compensating for the absence of parameters.

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

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

The description clearly states the tool checks subscription status, remaining credit, expiry date, and returns subscription plans. It is distinct from sibling tools (market data tools) by focusing on account-level information.

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 a concrete trigger: 'Call this if a request returned delayed data and you need live.' It does not explicitly name alternatives, but the purpose and trigger imply when to use it.

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

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

No annotations are provided, so the description carries the full burden. It transparently discloses that without a subscription, it returns a delayed sample, and with a subscription, live data. It also mentions pricing and payment via x402, which is important behavioral context. However, it doesn't mention rate limits or error behaviors (e.g., what happens if endpoint is invalid), so not a 5.

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 concise, starting with the key differentiator (free delayed sample) and then explaining live data. It mixes pricing and usage, which is a bit dense but each sentence earns its place. Front-loading the key 'FREE' element is effective. Slightly overlong with the URL and pricing details, but still 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?

The description provides the essential information for calling this tool: how to get the endpoint, the free vs. paid behavior, and the response shape visibility. It doesn't describe the return format or pagination, but there is no output schema, so that could be a gap. However, given the tool's simplicity (just two parameters), this is fairly complete for an agent to decide to call it.

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 describes both parameters adequately. The description adds context about the endpoint coming from search_market_data, which is helpful but does not detail parameter formats beyond the examples. Baseline 3 is appropriate as the description offers marginal added value.

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 fetches data from an endpoint returned by search_market_data, distinguishing it from siblings. It specifies the resource (data endpoint) and the verb (fetch), and even mentions that endpoint comes from search_market_data, which is the only sibling it relates to. It could be more explicit about what 'data' means (e.g., time series vs. snapshots) but the core purpose is clear.

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

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

The description explains when to use it: after search_market_data returns an endpoint, and contrasts live vs. delayed data based on subscription. However, it doesn't explicitly say when NOT to use it or when to use alternatives like market_snapshot. The guidance is contextual but lacks explicit exclusions, so a 4 rather than 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 provided, the description carries the full burden. It mentions 'FREE' which is a behavioral note (no cost), and it describes the return contents (categories, endpoint counts, cheapest price). It lacks details on response format or pagination, but for a zero-parameter read-only-like tool, this is adequate. The description adds value beyond the schema.

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

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

The description is a single, compact sentence that front-loads the most important information (FREE, catalog overview) and then details the contents. It avoids fluff and is easily scannable. The use case guidance is integrated efficiently. Perfect balance of brevity and completeness.

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 provides all essential information: what it returns (category, endpoint counts, cheapest price) and when to use it (before searching). The tool is simple enough that no additional context is needed. Completeness is high for the tool's complexity.

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?

Since there are no parameters, the schema is trivially covered. The description doesn't need to explain parameters, but it does add value by explaining what the tool returns in terms of data (categories, counts, prices). Thus it compensates for the empty schema, earning a baseline of 4 as per the rubric.

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: listing categories, endpoint counts, and cheapest prices. It distinguishes itself by framing as a catalog overview, which is distinct from the more specific search and market data tools. The verb 'list' plus the resource 'categories' is specific and actionable.

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 a clear use case: 'Use when you want to know what this service covers before searching.' This gives context for when to use it, and the sibling names imply alternatives for other purposes, though it doesn't explicitly say when not to use it or name alternatives directly. Still, the guidance is clear enough.

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 cost behavior (billing at x402 price for live components) and the free tier for delayed samples, which is essential for an agent to avoid surprise costs. However, it does not mention rate limits, authentication, or any side effects, leaving some gaps.

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 moderately long but every sentence serves a purpose: it covers pricing, scope meanings, and usage guidance in a structured way. The information is front-loaded with the pricing note, which is critical for decision-making.

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 tool with no output schema, the description provides sufficient context: it explains the three scopes, pricing, and when to use it. It lacks details on return format, but that is not required without an output schema, and the description is otherwise comprehensive.

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 says 'Which area to snapshot,' but the description elaborates each enum value with what it returns (e.g., 'brazil' returns central-bank macro, real interest rate, Ibovespa, and B3 blue chips). This adds substantial semantic value beyond the schema, helping the agent choose the right scope.

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

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

The description clearly states that this tool provides a broad market snapshot across three scopes (brazil, global, crypto), distinguishing it from tools that return a single number. It uses a specific verb ('snapshot') and resource ('market'), and the scope enumeration gives concrete examples of what each returns.

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 instructs to use this when the question is broad rather than about one number, and notes the pricing difference (free for delayed samples, billed for live components). This gives clear guidance on when to prefer this tool over alternatives that might focus on specific data points.

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 are none, so the description carries the full disclosure burden. It discloses the FREE cost signal and the return shape (endpoints with price and parameters), but does not mention failure modes, what happens with no matches, or pagination behavior. For a simple search tool the core behavior is disclosed, though edge behavior is left open.

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 front-loads 'FREE. FIRST STEP' then states purpose, scope, and examples in a logical order. The example list is somewhat long but each example demonstrates legitimate query patterns and earns its place. No redundant sentences.

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

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

For a simple tool (2 params, no output schema, no nested objects, no annotations), the description covers what it does, when to use it, what it returns, its coverage scope, and input examples. The only gaps are failure behavior and result limits, which are minor for a discovery tool.

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

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

Schema coverage is 100%, so the schema already documents both parameters, setting the baseline at 3. The description adds real value for 'query' by explaining it should be in plain language and giving five concrete examples, but adds nothing about 'limit' beyond what the schema 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 description states a specific verb+resource: it searches/discoveries market-data endpoints from a plain-language query. It is clearly distinct from the sibling get_market_data by framing itself as the discovery step ('FIRST STEP') that returns endpoints rather than data, so an agent can tell it apart without opening schemas.

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?

'FIRST STEP for any market-data question' is an explicit positional directive telling the agent when to invoke it, and the example queries illustrate expected input shape. It does not explicitly name get_market_data as the follow-up or state when not to use it, but the FIRST STEP framing 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.