Dropbox

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it returns per-model scores and cost implications for Anthropic (BYO key). No contradictions.

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 well-structured sentences with front-loaded purpose, followed by key details. Every sentence adds information 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?

Description explains return structure (score, confidence, signals, raw_response per model + combined view). Lacks details on 'signals' or combined view, but sufficient given no output schema and moderate 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?

All 4 parameters have schema descriptions (100% coverage). The description adds value by explaining default models, BYO key for Anthropic, and context usage for disambiguation, 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?

Description provides a specific verb (Probe/score) and resource (LLMs, brand), and clearly distinguishes from siblings like scan_competitor_ai_presence. It explains the output (score 0-100) and default model.

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?

Description spells out use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), giving clear context. It lacks explicit when-not-to-use or alternative tools, but the stated use cases are sufficient.

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 explains that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which discloses its autonomous behavior. It does not describe side effects or limitations, but given the tool's purpose (question-answering), the description is reasonably transparent.

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 concise (three sentences) and front-loaded with the purpose. Every sentence adds value: first sentence states the action, second explains the mechanism, third provides examples. No wasted words.

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

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

Given the tool's simplicity (single parameter, no output schema, no annotations), the description is complete. It explains what the tool does, how it works, and provides examples. There is no need for additional behavioral details for this type of 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 input schema has 100% coverage with a single 'question' parameter described as 'Your question or request in natural language.' The description adds value by explaining that the question should be in plain English and giving examples, which provides more context than the schema alone.

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 ('Ask a question') and resource ('Pipeworx') and clearly distinguishes from siblings by stating it 'picks the right tool, fills the arguments, and returns the result,' which contrasts with sibling tools that perform specific actions like dropbox_create_folder or dropbox_search.

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 usage context: 'No need to browse tools or learn schemas — just describe what you need,' and gives examples like 'What is the US trade deficit with China?' However, it does not explicitly state when not to use this tool or mention alternatives, so it loses a point.

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 provide readOnlyHint=true and openWorldHint=true. The description goes beyond by detailing the internal process: resolving the market, classifying the bet, fanning out to packs, and returning an evidence packet with a market-vs-model comparison. 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 moderately long (5 sentences) but each sentence adds value. It front-loads the main action and then explains the process and use cases. Could be slightly trimmed but remains clear and 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?

Given the complexity (multi-step research), the description covers all necessary aspects: inputs (market, depth), process (resolving, classifying, fanning out), and outputs (evidence packet, comparison). No output schema exists, but the return value is described sufficiently.

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?

Input schema has 2 parameters with 100% description coverage. The description adds meaning to the 'depth' parameter ('quick = 2-3 evidence sources, thorough = full fan-out') and clarifies the 'market' parameter's input formats (slug, URL, or question text). This adds value 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 clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the action (researching), the resource (Polymarket bet), and the method (pulling Pipeworx data), distinguishing it from siblings like 'ask_pipeworx' which is more general.

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 explicit use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"' It also positions the tool as the core demo product and explains why it's better than discovering packs. However, it does not explicitly state when not to use it or compare to siblings like 'validate_claim'.

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?

Given no annotations, the description accurately conveys that this is a read operation returning paired data and URIs, listing metrics for each type. It lacks mention of potential side effects or permissions, but for this tool they are 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 extremely concise with four sentences, each conveying unique and useful information (action, type-specific data, output mention, efficiency note). No redundant or vague phrases, earning a top score.

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 tool has no output schema, but the description provides a reasonable outline of the return (paired data and URIs). For a comparison tool, it adequately sets expectations, though specifying the exact structure of the paired data would improve completeness.

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 has comprehensive descriptions (100% coverage). The description adds value by contextualizing what data is returned for each entity type, helping the agent understand the parameters' purpose beyond the schema's basics.

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 explicitly states the action ('compare 2–5 entities side by side'), the entity types ('company' or 'drug'), and the specific data fields for each type. It distinguishes from siblings by noting it replaces multiple sequential calls, making the purpose 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 indicates when to use (comparing multiple entities) and highlights efficiency gain over sequential calls. However, it does not explicitly specify when not to use it or mention alternative tools for single entities, which would strengthen 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?

The description reveals that the tool performs a search and returns relevant tool names and descriptions, but does not specify any additional behavioral traits like whether it modifies data or requires authentication. However, given no annotations are provided, it adequately covers the core behavior.

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, all front-loaded with the action, and no superfluous information. Every sentence adds value: purpose, mechanism, and usage guidance.

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 (2 parameters, no output schema, no nested objects), the description is complete. It explains what it does, when to use it, and what it returns. No additional information is necessary.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add extra meaning beyond what the schema provides for parameters; it mentions 'Natural language description' for query which aligns with schema. No additional parameter details are needed.

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 verb 'Search', the resource 'Pipeworx tool catalog', and the mechanism 'by describing what you need'. It distinguishes the tool's purpose from siblings by specifying it returns tool names and descriptions, and instructs to call it first when many tools are available.

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 states when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' It implies this tool is for discovery, not execution, and suggests alternatives are the specific tools returned.

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 does not disclose behavior like whether the folder is created under a parent path, any side effects, or error conditions (e.g., if path is invalid).

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 efficiently states the purpose. However, it could be slightly improved by adding context about parameters.

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 (2 params, no output schema), the description is minimally adequate but lacks behavioral details and usage context that would help the agent decide when to invoke 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 baseline is 3. The description adds no additional meaning beyond the schema; it repeats 'Create a new folder' without elaborating on the 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 verb ('Create') and resource ('folder in Dropbox'). It distinguishes from siblings like 'dropbox_download' or 'dropbox_list_folder' by specifying the create action on folders.

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 (e.g., if the folder already exists, or using other tools). No mention of prerequisites or limitations.

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 must disclose behavioral traits. It states that the tool returns file content as text and metadata, which implies a read-only operation. However, it does not mention potential limitations like file size, encoding, or authentication requirements, 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 two sentences long, each conveying essential information without redundancy. It is front-loaded with the action and resource, and the second sentence clarifies the return value. No unnecessary words.

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

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

Given the simplicity of the tool (one parameter, no nested objects, no output schema), the description is mostly complete. It explains what is returned. However, it could benefit from mentioning that only text files are suitable, or that binary files may not be returned 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 input schema has 100% description coverage, with a single parameter 'path' described as 'File path to download'. The description adds no additional meaning beyond the schema. With high schema coverage, a baseline of 3 is appropriate, but the description's clarity earns a 4.

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 action (download), the resource (a file from Dropbox), and what is returned (file content as text and metadata). It effectively distinguishes itself from sibling tools like dropbox_get_metadata (which only retrieves metadata) and dropbox_list_folder (which lists folder contents).

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 for downloading files as text. It does not explicitly state when not to use it (e.g., for binary files) or mention alternatives like dropbox_search for finding files. However, the context is clear, and with sibling tools listed, an agent can infer when to choose this tool.

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

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

The description does not disclose any behavioral traits beyond what is obvious from the name. With no annotations, the description should explain what metadata is returned, any restrictions (e.g., only for files, not folders), or if it can handle both. It lacks detail on the return format or potential errors.

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 with no extra words. It is concise but could be slightly more informative without adding length.

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 parameter and no output schema, the description should provide enough context to understand what 'metadata' means. It is insufficient: the agent doesn't know if it returns file size, modification time, or permissions. No output schema exists to compensate.

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

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

The schema covers the single parameter 'path' with 100% coverage, and the description does not add additional semantics. Since coverage is high, baseline 3 is appropriate, though the description could clarify if the path must be relative or absolute.

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 'Get metadata for a file or folder in Dropbox', which specifies the verb (get), resource (metadata), and scope (file or folder). It is clear but does not differentiate from siblings like 'dropbox_list_folder' or 'dropbox_download', which could also involve reading metadata.

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. For example, 'dropbox_list_folder' might also return metadata for items in a folder, and there is no mention of when to prefer this tool. No exclusions or alternatives 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?

No annotations provided, so description carries full burden. It does not disclose behavioral traits like pagination, error behavior, or that it only lists immediate children (not recursive). Minimal info beyond listing.

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?

Single sentence, front-loaded with purpose. No wasted words.

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

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

Given simple tool (list folder), no output schema, but lacks details on recursive listing, pagination, or error cases. Adequate for basic listing but could be more complete.

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

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

Schema coverage is 100%, so baseline is 3. Description does not add meaning beyond schema; it's generic. No mention of format for path or limit defaults beyond 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?

Description uses specific verb 'List' and resource 'files and folders in a Dropbox directory'. It clearly distinguishes from siblings like dropbox_create_folder (create), dropbox_download (download), dropbox_get_metadata (metadata), and dropbox_search (search).

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 explicit guidance on when to use vs alternatives, but description implies listing use case. Context signals show siblings with distinct purposes, so agent can infer usage from 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?

With no annotations, the description should disclose behavioral traits. It does not mention if the search is case-sensitive, supports wildcards, or any rate limits. The description is too brief to cover these aspects.

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 with 10 words, no waste. It is front-loaded with the action. Could benefit from a bit more detail without becoming verbose.

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 tool with 2 params and no output schema, the description is minimal. It lacks details on search scope, behavior, or return format, making it incomplete for an agent to use effectively.

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 the parameters. The description adds no extra meaning beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description states it searches for files and folders by name or content, which is clear but lacks differentiation from sibling tools like dropbox_list_folder. It does not specify that it is a full-text search or distinguish it from listing.

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 vs alternatives. For example, when to use dropbox_search vs dropbox_list_folder is not mentioned. No context on prerequisites or limitations.

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 provided, so description carries full burden. Discloses read-only nature (no mention of mutations), efficiency (replaces many calls), scope limitations (only company type), and output format (citation URIs). Lacks explicit statement on safety but 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?

Description is well-structured with clear sections: purpose, data list, output format, efficiency claim, and exclusion. Every sentence adds value, though slightly verbose. Could be tightened but effective.

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, description adequately explains return values (citation URIs for listed data categories) and limitations (only company, no person/place). Provides alternative for federal contracts. Missing explicit return format details but sufficient for agent understanding.

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?

Input schema already has 100% description coverage, with both 'type' and 'value' parameters well-documented. The description adds no new parameter information beyond what schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool aggregates a full entity profile from multiple Pipeworx packs in one call, listing specific data types (SEC filings, revenue, patents, news, LEI) and output format (pipeworx:// URIs). It distinguishes itself from siblings like resolve_entity and compare_entities by its breadth.

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?

Explicit when-to-use: replaces 10-15 sequential calls. Explicit when-not-to-use: federal contracts require usa_recipient_profile. Explicit alternative for name resolution: use resolve_entity first. Provides clear context for tool selection.

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 provided, so description carries full burden. It discloses the destructive action (delete) but does not specify behavior if key is missing, whether deletion is irreversible, or any side effects. Adequate but minimal.

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?

Single sentence, zero waste. Essential information front-loaded. Perfect for a simple tool.

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 (1 param, no output schema, no nested objects), the description is mostly sufficient. However, it lacks details on return value (e.g., success/failure indication) and error handling, which could be inferred but not explicit.

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% with the 'key' parameter described as 'Memory key to delete'. The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states it deletes a stored memory by key, with a specific verb ('Delete') and resource ('stored memory'). It distinguishes from siblings like 'remember' (store) and 'recall' (retrieve), though could explicitly contrast them.

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 a memory needs to be removed, but lacks guidance on when not to use it (e.g., if key doesn't exist) or alternatives. No exclusions or prerequisites mentioned.

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 safe/idempotent behavior. The description adds context that it fetches an external page and emits a text blob, consistent with openWorldHint. No contradictions. Could elaborate on error handling for unreachable URLs.

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?

Well structured with clear purpose, process, output, and bulleted use cases. Slightly verbose but every sentence adds value.

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, the description explains the output format. With annotations covering safety and idempotency, only minor gaps remain (e.g., error messages, rate limits).

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?

Baseline 3 due to 100% schema coverage. The description restates the max_links default/max but adds no new meaning beyond the schema's parameter descriptions.

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 verb ('Generate...llms.txt file'), resource ('any URL'), and output ('standard llms.txt markdown format'). It distinguishes itself from sibling tools like ai_visibility_check by focusing on file generation rather than visibility checks.

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 lists three specific use cases (client indexing, own project drafting, competitor auditing), giving clear guidance on when to use. However, it doesn't mention when not to use or any prerequisites (e.g., URL accessibility).

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 discloses the rate limit of 5 messages per identifier per day and notes the tool is 'Free'. However, it does not explain what happens after sending (e.g., confirmation) or any side effects. This is adequate but could be more 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 three sentences long, each serving a purpose: stating the action and intended uses, providing usage guidance, and noting constraints (rate limit, free). No wasted words, well-structured and front-loaded.

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 tool is simple with no output schema. The description covers purpose, usage, and constraints. However, it does not mention what the tool returns or any post-send confirmation. For a feedback tool, this is a minor gap, but overall it is fairly complete given the 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?

Schema description coverage is 100%, so baseline is 3. The description adds value by reinforcing the enum options and providing specific guidance on how to write the message (e.g., 'do not include the end-user's prompt verbatim', '1-2 sentences typical, 2000 chars max'). This extra context improves 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 clearly states 'Send feedback to the Pipeworx team' and lists specific use cases: bug reports, feature requests, missing data, or praise. It uses a specific verb and resource, and the list of feedback types distinguishes it from sibling tools.

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

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

The description tells when to use the tool ('Use for bug reports...') and provides a usage guideline: describe what you tried in terms of Pipeworx tools/data, and avoid including the end-user's prompt. It also mentions a rate limit. However, it does not explicitly state when not to use it, but context from sibling tools (e.g., ask_pipeworx) provides implicit differentiation.

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 indicate safety, and description adds context about data source (CF analytics-engine), no PII, data format (pack, tool, count), and caching window. No contradictions.

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?

Well-organized with bullet points for use cases, clear and efficient language, no unnecessary words.

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

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

Despite no output schema, description covers data structure, caching, privacy, and purpose, making it fully actionable.

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 description adds meaningful guidance on choosing window length (hot vs steady-state), beyond 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 states it returns trending tools, packs, and call volume over configurable windows, distinguishing it from sibling tools like discover_tools or ask_pipeworx.

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 lists three specific use cases (discovery, confirmation, alignment) and provides context for each. No explicit when-not-to-use, but use cases are comprehensive.

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?

Description goes beyond annotations by detailing the calculation logic (monotonicity check), data extraction (dates/thresholds), and output structure. 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?

Concise (~120 words), front-loaded with purpose, efficiently explains concept, usage, and output. Every sentence adds value.

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 no output schema, description fully specifies return format (list of objects with market_a, market_b, gap_pp, suggested_trade). No critical missing information.

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 already describes the 'event' parameter as slug or URL. Description reiterates this without adding new parameter-level details, so baseline 3 is appropriate given 100% schema coverage.

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 states the tool finds arbitrage opportunities within a Polymarket event via monotonicity violations. It explains the logic and distinguishes from sibling tools like polymarket_edges.

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?

Specifies when to use (events with multiple date/threshold markets) and how to pass input (slug or URL). Lacks explicit exclusions or alternatives but 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 already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: uses a lognormal model from FRED + live coinpaprika price, groups by asset, fetches price history once, computes model probability, ranks by |edge|, and returns top N with suggested direction. This goes well beyond the annotations.

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

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

The description is a single paragraph with multiple sentences covering purpose, model details, and process. It front-loads the main action but includes some verbose technical explanation (e.g., 'lognormal model from FRED + live coinpaprika price'). It could be more concise, but no sentence is wasted.

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 three parameters, no output schema, and a clear explanation of the algorithm and intended use case, the description is fairly complete. It explains the model, the grouping, and the ranking procedure. It does not specify return format (e.g., JSON fields), but that is acceptable without an output schema.

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% (all three parameters have descriptions). The tool description adds some context (e.g., 'limit' as top N after ranking, 'window' for volume filter, 'min_edge_pp' for minimum edge) but does not significantly enhance what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool scans high-volume Polymarket markets, finds where Pipeworx data disagrees with market price, and returns top N by edge magnitude with suggested trade direction. It distinguishes itself from siblings like polymarket_arbitrage and bet_research by focusing on disagreement-based opportunity discovery.

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 targets the 'what should I bet on today' question and mentions it saves agents/users from manual paging. While it doesn't explicitly state when not to use or name alternatives, the context (e.g., scanning top markets, rank by edge) implies it is for initial opportunity discovery, not for specific market analysis.

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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds that returns include leg-by-leg prices and spread in percentage points. However, it does not mention data freshness, accuracy, or error handling, which reduces transparency slightly.

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 uses structured sentences. It is slightly long but every sentence adds value. Could be tightened without losing clarity.

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, the description explains the return structure. It covers the main behavior but omits details on precedence when both topic and explicit are provided, and does not mention error cases. This leaves minor gaps for a tool with zero required parameters.

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%, but the description adds significant value by explaining the topic parameter's pre-mapped macros, the override behavior of explicit parameters, and providing examples for each parameter. This goes beyond the schema's basic descriptions.

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 the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It includes specific verbs and resources, and the purpose is distinct from sibling tools like 'polymarket_arbitrage'.

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 two modes (topic vs. explicit) and when to use each. It provides context for arbitrage signal, but does not explicitly state when NOT to use this tool or differentiate from alternatives like venue-specific pricing 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?

No annotations provided, but description clarifies behavior (retrieve vs list, session persistence). However, does not mention side effects or 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?

Two sentences, no wasted words, essential information front-loaded.

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 one optional parameter, description is sufficient. Could mention return format but not critical.

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 description adds purpose of key ('to retrieve') and behavior when omitted, which is clear and helpful beyond 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 clearly states it retrieves a memory by key or lists all memories, distinguishing it from remember and forget.

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 when to omit key (list all) and provides context ('context you saved earlier'), but does not explicitly say when not 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?

With no annotations, the description carries the full burden. It discloses the parallel fan-out to three sources, the return format (structured changes, count, URIs), and the supported entity type. It lacks details on potential rate limits or authentication, but is otherwise transparent.

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, well-structured paragraph that front-loads the purpose, then provides details. Every sentence adds value; no redundancy or fluff.

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 covers all required parameters, explains the tool's behavior (parallel fan-out), and describes the response structure (since no output schema). It is complete for a tool of moderate complexity, though it could mention typical use cases explicitly.

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 description adds value by explaining the 'since' parameter accepts ISO dates or relative strings (with examples), the 'type' is limited to 'company', and 'value' can be ticker or CIK. This goes beyond the schema descriptions.

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 defines the tool's purpose: getting 'what's new' for an entity since a time point. It specifies the entity type (company) and the sources (SEC EDGAR, GDELT, USPTO), distinguishing it from sibling tools like 'entity_profile' or 'compare_entities'.

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 explicit use cases: 'brief me on what happened with X' or change-monitoring workflows. It also gives parameter guidance for 'since' (typical '30d' or '1m'). However, it doesn't mention when not to use it or alternatives among 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?

Since no annotations are provided, the description carries full burden. It discloses key behavioral traits: persistence differences for authenticated vs anonymous users ('Authenticated users get persistent memory; anonymous sessions last 24 hours'). No contradictions with annotations (none exist).

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 concise with three sentences, each adding distinct value: purpose, usage guidance, and behavioral transparency. No redundant or unnecessary text.

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 2 parameters, no output schema, and no annotations, the description provides sufficient completeness: purpose, usage, behavioral persistence, and key-value semantics. It could mention that value is a string (implied by schema) but overall adequate.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add extra meaning beyond the schema; it only mentions 'key-value pair' generally. The schema already describes key and value with examples and usage notes.

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 explicitly states 'Store a key-value pair in your session memory' and provides specific use cases like saving 'intermediate findings, user preferences, or context across tool calls'. It clearly distinguishes itself from siblings like 'recall' (retrieve) and 'forget' (remove).

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 says 'Use this to save intermediate findings, user preferences, or context across tool calls', giving clear usage guidance. It does not explicitly mention when not to use it or alternatives, but the context signals show siblings like 'recall' for retrieval and 'forget' for deletion, which helps differentiation.

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 provided, so description carries full burden. It discloses return fields (ticker, CIK, company name, URIs) and version constraints, but lacks details on authorization or rate limits.

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

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

Two sentences that are efficient and front-loaded. Every word adds value; no fluff.

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 (2 params, no output schema, no annotations), the description covers the essential return fields and usage. Slightly incomplete on potential edge cases like invalid input.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds minimal extra meaning (v1 details, examples) 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 clearly states the tool resolves an entity to canonical IDs with specific examples for type="company". It distinguishes from sibling tools like ask_pipeworx or discover_tools.

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

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

The description notes it replaces 2-3 lookup calls, implying efficiency use. However, it does not explicitly state when not to use it or mention alternatives.

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

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

The description discloses that it probes each entity with ai_visibility_check, returns a ranked list with score, confidence, and signal density. Annotations already indicate read-only and idempotent behavior, and the description adds detail without contradiction.

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

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

Two sentences, no wasted words. First sentence states the core action, second provides an example and output format. Highly efficient.

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

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

Given the tool's complexity (4 parameters, no output schema), the description covers input semantics, process (probes each entity), and output (ranked list with metrics). No gaps remain.

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% with descriptions. The description additionally explains that the first entity in the array is treated as 'subject' and the rest as competitors, which goes beyond the schema and adds useful context.

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 it compares AI visibility across multiple entities side-by-side, using ai_visibility_check, and surfaces rankings. This distinguishes it from the sibling tool ai_visibility_check which likely handles a single entity.

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 use case ('competitive AI-marketing audits') and an example question, giving clear context for when to use. It does not explicitly exclude other scenarios but the purpose is well-defined.

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?

Discloses return format and supported claim types, but without annotations, it does not detail limitations (e.g., only US public companies, specific financial metrics) or operational constraints like rate limits or authorization.

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 succinct sentences front-loaded with essential details, no 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?

Despite no output schema, description enumerates return fields (verdict, structured form, actual value, citation, delta). Coverage is sufficient for the focused domain, though error handling is omitted.

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?

Only one parameter (claim) with 100% schema coverage; description adds minimal extra meaning beyond the schema's existing examples, so baseline score 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?

Description clearly states it fact-checks natural-language claims against authoritative sources, specifies supported domain (company-financial claims) and sources (SEC EDGAR + XBRL), and differentiates from siblings like compare_entities and ask_pipeworx.

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?

Mentions that it replaces 4-6 sequential agent calls, implying efficiency for claim verification, but does not explicitly state when to use versus alternatives or exclude inappropriate claims.

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