QuantApe Smart Lists

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

The readOnlyHint annotation already conveys it is a safe read operation. The description adds useful context about rate limits and pricing (daily allowances, overage cost via x402/USDC), which is beyond the annotation. However, it does not describe the response format, expected size, or any potential side effects—though for a read-only tool with a single parameter, the missing context is not critical.

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 concise sentences. The first sentence front-loads the core purpose with concrete details, and the second succinctly covers commercial terms. There is zero fluff, and every clause 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 simple read-only tool with one parameter and no output schema, the description provides the essential purpose and even includes cost/rate-limit info. However, it does not describe the return structure (e.g., a list of screen names or objects), nor does it mention error conditions. Since no output schema exists, the description could have compensated by outlining the expected response shape, which is a minor gap.

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 has a single required 'symbol' parameter with no description (0% coverage). The description clarifies that this is a 'ticker' symbol, which adds meaning beyond the bare schema. However, it does not specify the exact format (e.g., case sensitivity, whether it must match a stock exchange symbol, or if it accepts ETFs/indexes). For a single parameter, this level of clarification is adequate but not comprehensive.

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: 'Every stock screen a given ticker is currently in' and enumerates the categories (momentum, technical-signal, earnings, theme, thesis). This is specific and distinguishes it from sibling tools like list_smart_lists or get_smart_list, which focus on individual lists rather than a ticker's memberships.

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 when you want a ticker's list memberships, but it does not explicitly compare to alternatives or state when not to use it. With several sibling tools that deal with lists (list_smart_lists, get_smart_list, get_list_criteria), there is no guidance on which to choose, leaving an agent to infer from tool names.

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

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

Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds 'Always free', which is a useful behavioral note about cost. However, it discloses nothing else, such as error handling or what happens if the list_id is invalid, which could be expected for a metadata 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 a single sentence, front-loaded with the core purpose ('Metadata for one stock screen') and efficiently lists key return fields. No fluff or redundancy; every word contributes to understanding.

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 simplicity (one required id parameter, no output schema), the description provides a reasonable overview of returned fields. However, it omits any explanation of the list_id parameter, which is critical for invocation. Since the description does not compensate for the absent schema coverage, completeness is only partial.

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

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

Schema description coverage is 0%, and the description does not explain the list_id parameter at all. An agent cannot infer its format, meaning, or acceptable values from the description. The sole parameter is undocumented both in schema and description, so this dimension is severely lacking.

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 retrieves metadata for a single stock screen, listing the specific fields returned (display name, short description, icon, category ids, parent screen). This distinguishes it from siblings like get_smart_list_metrics or list_smart_lists, which serve different purposes.

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 no explicit guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or recommend other tools for different needs (e.g., full screen details versus metadata). The 'Always free' note is a cost hint, not a usage guideline.

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 readOnlyHint annotation is already present, and the description adds important behavior beyond it: daily allowance and 0.02-dollar cost per call after the quota, which could affect whether an agent should invoke the tool. It also notes that section selection can keep the answer small, showing output variability.

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 the main purpose, then a quick usage note, then pricing details in a separate sentence. Every sentence adds functional information, and there is no redundant filler, though the pricing sentence is a bit long.

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 only one optional parameter, the description covers enabled content, how to adjust output size, what the tool is good for, and cost. An output schema is absent but not critical here; the main remaining gap is not spelling out the expected response structure, but the tool is simple enough that this is a minor omission.

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

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

Schema coverage is 0%, so the description must compensate, and it does: it maps the selectable sections to content categories (daily narrative, fear/greed, sectors, rotation, news) and indicates the array controls answer size. It doesn't explicitly list the exact enum names, but an agent can map the prose to the enum 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 identifies a market overview tool and enumerates the distinct data categories it returns, making it clear this is a market-level view rather than a watchlist-level view. It does not explicitly contrast with a sibling tool beyond the get_my_watchlist pairing, but the resource is well-specified.

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

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

It gives actionable guidance to select sections for a smaller response and recommends pairing with get_my_watchlist for watchlist-relative questions, showing when it adds value. It also sets expectations that the output is descriptive, not a recommendation. It lacks explicit 'don't use this when...' exclusions, but the direction 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?

Beyond the readOnlyHint annotation, the description discloses the session-token auth requirement, daily usage allowances, overage pricing via x402, and explicitly says the output is descriptive and not a recommendation. No contradiction with annotations.

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

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

The description is a single information-dense paragraph with the core comparison and scope front-loaded, followed by auth, pricing, and disclaimers. Every clause carries useful information, though it could be broken into slightly tighter bullets.

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

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

With no output schema and 0% parameter documentation, the description covers the main purpose, auth, cost, and read-only nature, and uses the sibling comparison to supply return-shape context. The remaining gap is parameter semantics, which are not explained.

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

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

Schema description coverage is 0%, and the description does not explain limit, offset, or news_days beyond a general reference to 'latest headlines'. The parameter names are conventional, but the description does not compensate for the schema's lack of 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?

The description names a specific operation: return the same per-symbol metrics as get_smart_list_metrics plus latest headlines for the user's own watchlist. It distinguishes itself from siblings by adding headlines and narrowing scope to 'your own watchlist'.

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 clearly identifies the target use case ('What's going on with my watchlist today?') and implies this is the personalized watchlist version of get_smart_list_metrics. It does not explicitly state when to prefer one sibling over another, but the context is strong 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 include readOnlyHint=true, and the description adds significant behavioral context beyond that: it mentions paging with a max of 200 per call, the snapshot nature ('currently in a stock screen'), and cost/allowance details (free tiers, then $0.01 per call). This goes beyond the annotation and helps the agent set expectations.

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

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

The description is a single sentence that packs a lot of information but remains readable. It front-loads the core purpose, then adds examples and pricing. While lengthy, it avoids redundancy and every detail serves a purpose, though it could be split into clearer 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 tool with 3 parameters and no output schema, the description covers the return content and paging but omits explanation of the list_id parameter (which is required) and offset. The cost/allowance info is helpful, but an agent might not know how to obtain a valid list_id without additional context from sibling tools.

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?

With 0% schema description coverage, the description must compensate for parameter meaning. It mentions a max of 200 implying the limit parameter, and 'paged' suggests offset, but it does not explain the required list_id or clarify parameter formats. The description adds only partial value, leaving list_id and offset meanings ambiguous.

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 ('get') and resource ('smart list') and clarifies with concrete examples (earnings, golden-cross buy signals, AI beneficiaries). It distinguishes itself from siblings like list_smart_lists (which lists lists) and get_list_criteria (which gets criteria) by focusing on the symbols contained in a screen.

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 on the output being descriptive and not a recommendation, but it does not explicitly state when to use this tool versus alternatives like get_market_overview or get_my_watchlist. There is no direct 'use this for X' or 'for Y use that' guidance, leaving the agent to infer from the sibling names.

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

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

Annotations already mark readOnlyHint=true, and the description goes further with valuable behavioral details: baseline can span multiple rebuilds, and usage is metered with anonymous/signed-in/power-user allowances and an overage price via x402. 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?

One well-organized sentence that front-loads the core behavior and then adds pricing context. Slightly dense, but each clause earns its place and no filler 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 single-parameter read-only tool, the description covers scope, examples, and rate limits well. The main omission is any guidance on the list_id argument, and the output format is not described since no output schema exists.

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

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

Schema coverage is 0% and the description never explains list_id or how to obtain it. The parameter name hints at a list identifier, but the description doesn't compensate for the complete lack of schema documentation.

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 states a specific query: which symbols entered or left a stock screen since its last baseline, with concrete examples (new golden crosses, dropped momentum names). This clearly distinguishes it from sibling tools like get_smart_list or get_smart_list_metrics.

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 'entered or left since baseline' framing makes the intended use-case explicit and separates it from point-in-time snapshot tools. It does not explicitly name alternatives or state when not to use it, but the context 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?

Annotations declare readOnlyHint=true, and the description adds meaningful behavioral context: it is 'Descriptive, not a recommendation,' it is paginated with a max of 200 per call, and it discloses rate limits and pricing tiers. This goes beyond the annotation and helps an agent understand side effects and constraints.

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 sentence that front-loads the core purpose and metric list, then adds pagination and pricing. It is efficient and every clause adds information, though the long metric enumeration makes it slightly heavy.

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 metrics tool with no output schema, the description covers the return content, pagination, and usage limits. It does not describe the exact response shape or error cases, but the metric list is sufficiently detailed for an agent to know what to expect.

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

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

Schema description coverage is 0%, so the description must compensate. It explains the meaning of the resource (list_id) implicitly by describing the output, and it mentions pagination (limit/offset) via 'paged, max 200 per call.' However, it does not explicitly define list_id, limit, or offset semantics beyond that, so the agent must infer some parameter meaning from the schema names.

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') and resource ('per-symbol metrics for a stock screen') and enumerates the exact metric categories, distinguishing it from sibling tools like get_smart_list (list metadata) and get_smart_list_changes (changes). It is immediately clear what this tool 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?

The description clearly implies this is for retrieving metrics for a stock screen and notes pagination ('paged, max 200 per call'), which guides usage. It does not explicitly name alternatives or when-not-to-use conditions, but the sibling context and the detailed metric list make the use case 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=true, and the description adds useful behavioral context such as 'Always free' and the kind of pricing/allowance data returned. It does not go into detail on response shape or category filtering behavior, but for a read-only list tool the added context is sufficient.

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 the core purpose and a useful enumeration of categories, followed by two short actionable sentences. The category list is long but meaningful; the reference to 'the metered tools below' is slightly vague outside the original page 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?

For a read-only listing tool with no output schema, the description names the fields returned, the categories available, and the cost implication. It is mostly complete, though it leaves the role of the optional category_id parameter and the default full-list behavior to inference.

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

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

Schema description coverage is 0% and the description never explicitly documents the optional category_id parameter. The category list implies what values might be relevant, but the tool does not directly connect the parameter to the behavior or explain what happens when it is omitted.

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 browsing stock screens by category and enumerates specific categories and output fields. It also frames the tool as the entry point for finding a screen id, which distinguishes it from the get_smart_list and find_symbol_lists siblings.

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

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

The phrase 'Start here to find a screen id' gives clear when-to-use guidance and establishes this as a discovery endpoint. It does not explicitly name alternatives for when a user already knows the screen id, so it stops short of a full 5.

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