Clickup

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true. The description adds significant behavioral context: default model (Workers AI Llama-3.3-70b free), BYO key option for Anthropic, and return structure (per-model {score, confidence, signals, raw_response} + combined view). 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?

The description is three well-structured sentences. The first sentence states the core function. The second explains model choice and authentication. The third lists return format and use cases. Every sentence provides essential information with 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 lacking an output schema, the description clearly explains the return format (per-model object with score, confidence, signals, raw_response plus combined view). All parameters are described with examples. No missing behavioral or usage 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 description coverage is 100%, so baseline is 3. The description adds value by explaining the purpose of each parameter, how they interact (e.g., '_apiKey' needed if 'anthropic' in models), and provides concrete examples ('Pipeworx', 'OpenInvoice'). This goes beyond the schema definitions.

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

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

The description clearly states the tool's function with a specific verb ('Probe'), resource ('LLMs'), and measurable output ('score visibility 0-100 per model'). It distinguishes from siblings by focusing on visibility scoring and AI marketing audits, which is different from other tools like 'ask_pipeworx' or 'entity_profile'.

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 three use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly state when not to use it or list alternatives, but the context makes it clear this is for probing LLM knowledge, differentiating it from sibling tools.

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

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

The description reveals that the tool picks the right tool and fills arguments, providing insight into its internal behavior. Since no annotations are provided, this adds necessary transparency about its autonomous nature.

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, using three short sentences plus examples. It is front-loaded with the purpose, and every sentence contributes meaningful information.

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

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

Given the single parameter and lack of output schema, the description is complete enough. It explains the tool's autonomous selection behavior and provides examples. A minor gap is not specifying what happens if the question cannot be answered.

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 context by explaining the parameter as 'Your question or request in natural language' and providing examples. This adds value beyond the schema, but the schema already adequately describes the parameter.

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 answers natural language questions by selecting the best data source, with specific examples like 'What is the US trade deficit with China?'. This distinguishes it from sibling tools like clickup_create_task 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 explicitly says 'No need to browse tools or learn schemas' and gives examples, implying when to use it. 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?

Beyond annotations (readOnlyHint, openWorldHint), the description adds that it resolves the market, classifies bets, fans out to packs, and returns an evidence packet with comparison. 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?

The description is front-loaded with the main purpose and structured logically: input, process, output. Every sentence adds value, though it could be slightly more concise.

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, the description covers inputs, processing steps, and the nature of the return value (evidence packet plus comparison). It provides sufficient context for an agent to use the tool 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 baseline is 3. The description expands on the 'depth' parameter by explaining quick vs thorough (2-3 vs full) and default, and clarifies that 'market' accepts slug, URL, or question text.

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 verb (research), resource (Polymarket bet), and how it differs from sibling tools by automatically resolving and fanning out to packs.

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 use cases are given: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"'. It implies when to use by contrasting with manual discovery but lacks an explicit when-not-to-use statement.

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; description clarifies it creates a task (mutation) and returns specific fields. However, lacks details on authentication requirements or side effects beyond creation.

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 redundancy. First sentence states action and result, second sentence lists return fields. 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 no output schema and 6 parameters, description covers basic return fields but misses details like whether due_date is optional, expected response shape, or error handling. Adequate but not exhaustive.

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 covers 100% of parameters with descriptions. Description adds no additional meaning beyond schema, e.g., does not explain date format or priority enum values beyond what schema already provides.

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

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

Description clearly states verb 'Create', resource 'task in a ClickUp list', and explicitly lists return values (ID, name, status, URL). Distinguishes from sibling tools like 'clickup_get_task' which retrieves tasks.

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?

Implied usage from description but no explicit guidance on when to use vs. alternatives like clickup_list_tasks. 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?

The description discloses that the tool returns full task details and lists the included fields, which is useful behavioral context. Since no annotations are provided, the description carries the full burden, and it adequately describes the read-only nature and scope of data returned.

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, zero waste. The first sentence clearly states the action and identifier, and the second summarizes the return fields. Every word is functional.

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 (two simple parameters, no nested objects, no output schema), the description is complete enough. It covers what the tool does and what it returns. No additional context is necessary for an agent to use it correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description does not add meaning beyond what the schema provides for '_apiKey' and 'task_id'. It mentions returning details but does not elaborate on parameter usage.

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 'a single ClickUp task by ID'. It distinguishes from siblings like 'clickup_list_tasks' (which returns multiple tasks) and 'clickup_create_task' (which creates). The details listed (name, description, status, etc.) clarify the return content.

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

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

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention that this tool is for retrieving a specific task by ID, while 'clickup_list_tasks' is for querying tasks in a list. The description implies usage but lacks explicit when-to-use or when-not-to-use 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?

No annotations provided, so description carries full burden. It reveals that it returns folder ID, name, and list count, but does not disclose any side effects, error conditions, or pagination behavior. Adequate for a simple read-only list operation.

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 concise sentences with no wasted words. Information is front-loaded. Could potentially be more structured, but 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 is a simple list operation with two well-documented parameters and no output schema, the description is mostly complete. However, it lacks details on output format or pagination, which would be helpful for agents.

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 parameters are already documented. The description does not add additional meaning beyond what the schema provides, which is acceptable given high 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 it lists folders in a ClickUp space, and specifies the returned fields (ID, name, list count). It distinguishes from siblings like clickup_list_tasks and clickup_list_spaces by focusing 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?

Implied usage for listing folders given a space_id, but no explicit guidance on when to use this versus other list tools (e.g., when to list folders vs tasks vs spaces) or 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?

Annotations are empty, so the description carries full burden. It states it is a read operation ('List') and provides output fields (space ID, name, status info). However, it does not mention pagination, rate limits, or any authorization requirements beyond the API key.

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

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

The description is a single concise sentence that efficiently conveys purpose and output. It is front-loaded with the action and resource.

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 nested objects, no output schema), the description is mostly complete. However, it could mention that spaces are top-level containers and that team_id is required. The lack of output schema means description could clarify return format.

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 no additional meaning beyond the schema. It does not explain what team_id represents or how to obtain it, but the schema's description is sufficient.

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 lists spaces in a ClickUp team/workspace and returns space ID, name, and status info. However, it does not explicitly distinguish itself from sibling tools like clickup_list_folders or clickup_list_tasks, which might be useful for disambiguation.

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 instance, if the agent wants to list folders or tasks, it might need to use different tools, but no such distinction is made.

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 mentions returned fields but does not disclose pagination behavior (page parameter is described in schema), rate limits, or authentication requirements. No annotations provided, so description carries full burden; it is 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?

Two concise sentences front-load purpose and output fields. No wasted words; could optionally include usage notes without sacrificing conciseness.

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 partially compensates by listing return fields. However, lacks pagination details, filtering options, or error handling info. Adequate for a straightforward list endpoint 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 description coverage is 100%, so baseline is 3. Description adds no extra meaning beyond schema for page, _apiKey, and list_id; it correctly omits redundancy.

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?

Clearly states it lists tasks in a ClickUp list and enumerates returned fields (task ID, name, status, etc.). Distinguishes from sibling tools like clickup_create_task and clickup_get_task by focusing on listing, though not explicitly differentiating from other list tools like clickup_list_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 vs alternatives. Does not mention that it returns tasks from a single list, nor when to use clickup_get_task for a specific task. Lacks usage context such as filtering or sorting capabilities.

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 type of data returned (financial metrics, counts, URIs) and implies a read-only operation. However, it does not explicitly state read-only or mention any side effects, leaving slight ambiguity.

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, front-loaded with the core purpose, then adding details. Every sentence is essential and no fluff, making it highly concise and 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?

Without an output schema, the description should more fully explain the return format. It only mentions 'paired data' and URIs, but does not specify the structure (e.g., JSON format, field names). This leaves some ambiguity for the agent on how to parse the response.

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

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

The schema already provides good descriptions for both parameters. The description adds value by giving examples of data fields returned per entity type and examples of input values, enhancing clarity 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: compare 2-5 entities side by side in one call. It specifies two entity types and the exact data fields returned for each, making it highly specific and distinguishable from other 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 implies when to use this tool by stating it replaces 8-15 sequential agent calls, suggesting efficiency. However, it does not explicitly name alternative tools or state when not to use it, which would make it score a 5.

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

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

No annotations provided, so the description must fully disclose behavior. It states the tool returns 'most relevant tools with names and descriptions', but does not mention whether it is read-only, destructive, or has any side effects. The description is neutral and does not contradict annotations (none), but it lacks explicit behavioral traits beyond the search result. Since there are no annotations, a score of 3 is appropriate: it gives basic behavioral info but not comprehensive.

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, each serving a purpose: what it does, what it returns, and when to call it. No extraneous words. Slightly more verbose than strictly necessary, but the instruction 'Call this FIRST' earns its place. Score 4 for being efficient but not maximally concise.

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 simple search functionality, 2 parameters, no output schema, and no annotations, the description covers the core purpose and usage context. It explains when to use it (first, with many tools). It does not describe the output format (e.g., whether it includes tool IDs or just names), but the tool is straightforward and the description is sufficient for an agent to decide when to call it. A score of 4 reflects good completeness for this complexity level.

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%: both parameters ('query' and 'limit') have descriptions in the schema. The description adds no extra parameter-level meaning beyond the schema. Baseline 3 is appropriate because the schema already handles parameter documentation adequately.

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 specific verb 'Search' and resource 'Pipeworx tool catalog', clearly stating the tool's function: to find relevant tools by natural language description. It distinguishes itself from sibling tools (which are about ClickUp, memory, or AI chat) by being the discovery/search tool for the tool catalog.

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 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear guidance on when to use it (before other tools) and the context (large tool set). No sibling alternatives are mentioned, but the call-to-action is strong and unambiguous.

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 full burden. It details what data is returned (SEC filings, revenue, patents, news, LEI) and the output format (pipeworx:// citation URIs). However, it does not mention potential errors, rate limits, or authorization requirements, preventing a score of 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 two sentences, front-loaded with the main purpose, and every sentence adds unique value. No redundant or extraneous information. Excellent conciseness.

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 two simple parameters, no output schema, and no annotations, the description provides complete context: what the tool does, what data it returns, how to use the parameters, and when to use alternatives. The agent has sufficient information to invoke it correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant context beyond the schema: it explains that type is currently only 'company', gives examples for value (ticker or CIK), and clarifies that names are not supported, directing to resolve_entity. This justifies a score of 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 tool returns a full profile of an entity across multiple packs, listing specific data types (SEC filings, revenue, patents, news, LEI) and distinguishing itself from sibling tools like resolve_entity and usa_recipient_profile. The verb is specific and the resource is well-defined.

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 guidance: when to use (for comprehensive entity profiles, replacing 10-15 calls), when not to use (for federal contracts, use usa_recipient_profile), and how to prepare input (use resolve_entity for names). This is highly actionable for an AI agent.

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 exist, so the description carries full burden. It correctly describes a destructive action (delete) but does not disclose consequences (e.g., irreversible? requires auth?). The description is accurate but lacks depth.

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, no waste. Front-loaded with verb and resource. Ideal length 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?

The tool is simple (1 param, no output schema). The description fully captures the purpose and parameter. Additional context like auth requirements or side effects would be nice but not essential given the tool's trivial nature.

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 one parameter 'key'. The schema's description 'Memory key to delete' already explains it. The tool description adds no extra meaning, but given full coverage, the baseline is 3. However, the description clarifies the action context (delete), earning 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 uses a clear verb 'Delete' with a specific resource 'stored memory by key'. It uniquely identifies the action and distinguishes from siblings like 'recall' (read) and 'remember' (create).

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

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

The description states what it does but provides no guidance on when to use it vs alternatives. Sibling tools like 'recall' and 'remember' suggest a memory management context, but no explicit exclusions or prerequisites are given.

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 the tool's behavior (fetch page, extract title/description/key links, emit standard format) beyond the annotations. The annotations already indicate readOnly, openWorld, idempotent, non-destructive, and the description adds operational detail.

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 with no wasted words. The first sentence covers what the tool does, the second provides use cases. It is well front-loaded and 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?

Without an output schema, the description adequately describes the output as a single text blob for site-root/llms.txt. For a two-parameter tool, this provides a complete understanding of input, process, and output.

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 100% schema coverage, the description adds minimal meaning beyond the schema. It reinforces the purpose of the output but does not provide new parameter insight. 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 explicitly states the tool generates a production-ready llms.txt file for any URL, detailing the process (fetch, extract, emit) and the output format. It clearly distinguishes itself from unrelated 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 lists specific use cases: getting a client's site indexed, drafting for own project, auditing competitor. It does not include exclusions or alternatives, but the context is sufficiently clear for an AI agent to decide when to use.

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 rate-limiting and free usage, which are key behavioral traits. No annotations provided, so description carries full burden. Could mention response behavior (e.g., 'we'll review it'). 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?

Very concise, single paragraph. Purpose is front-loaded. Every sentence is essential, 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?

For a simple feedback tool, the description is complete: purpose, usage, rate limits, content guidelines. No output schema needed, nested object context optional.

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 100% description coverage, so baseline is 3. The description does not add significant extra meaning beyond schema's parameter descriptions. Adequate.

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: sending feedback to the Pipeworx team. It lists specific use cases (bug reports, feature requests, missing data, praise) and distinguishes itself from sibling tools like 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?

Provides clear when-to-use context and explicit guidelines on content (describe what was tried in terms of Pipeworx tools/data, avoid including end-user prompt verbatim). Mentions rate limit of 5 per identifier per day. Lacks explicit alternatives, but this is a niche feedback 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?

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds context about caching (5min-1h), data source (CF analytics), and privacy (no PII), which are valuable beyond 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 efficient, using bullet-like structure and active voice. Each sentence adds value, though it could be slightly 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?

Despite no output schema, the description implies the return shape (pack, tool, count) and covers all relevant behavioral aspects. It is fully complete 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?

Schema coverage is 100% with a well-described enum parameter. The description reiterates and adds nuance (shorter vs longer windows for hot vs steady-state demand), slightly augmenting 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 returns trending tools, packs, and call volume over recent windows, and explicitly lists three use cases that differentiate it from sibling tools like 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?

Three specific use cases are provided, guiding when to use the tool. However, no explicit 'when not to use' or direct alternatives are mentioned, which would strengthen the guidelines.

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 destructiveHint=false, so the tool is safe. The description adds useful behavioral context: it searches, groups, and checks monotonicity, and returns ranked opportunities with reasoning.

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 and well-structured, front-loading the purpose and modes. Every sentence adds value, though it could be slightly more terse 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?

For a read-only search tool with no output schema, the description adequately explains the logic, return format (ranked opportunities with reasoning), and parameter semantics. It is complete enough for an agent to use correctly.

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

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

Schema coverage is 100% with both parameters having descriptions. The description enriches them by explaining the two modes, providing examples, and clarifying that event accepts slug or URL. This adds significant meaning 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: finding arbitrage opportunities via monotonicity violations. It introduces two distinct modes (event and topic), differentiating it from any 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?

The description explains when to use each mode with examples and highlights a scenario where topic mode is necessary. However, it does not explicitly compare to sibling tools or state when not to use 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?

Annotations provide readOnlyHint and openWorldHint. Description adds specifics: uses lognormal model from FRED + live coinpaprika, groups by asset, fetches price history once, computes probabilities, and ranks. No 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?

Long single paragraph but every sentence contributes. Could be restructured with bullets but remains clear and 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 no output schema, description explains what is returned (top N ranked by edge magnitude with trade direction). Covers input, process, output, and use case thoroughly. Annotations match described behavior.

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 covers all 3 parameters with descriptions. Description reinforces defaults (limit=10, max=25; window=1wk; min_edge_pp=0.5) and explains their purpose, adding value 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 scans high-volume Polymarket markets, compares Pipeworx data to market price for crypto bets, and returns top edges ranked by magnitude with trade direction. This distinguishes it from siblings 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?

Explicitly says it's for 'what should I bet on today' and avoids paging through markets manually. Does not explicitly state when not to use or alternatives, but context 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?

Annotations already indicate readOnly and idempotent. The description adds that the spread typically ranges 2-25pp and explains the return structure (leg-by-leg prices and spread), providing behavioral context beyond 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 concise and front-loaded with purpose, followed by mode details. Every sentence adds value, though a slightly more structured format could improve readability.

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

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

The description covers the tool's purpose, modes, parameter usage, and return structure. Given the absence of an output schema, it provides enough detail for an agent to invoke it correctly.

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

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

Schema coverage is 100%. The description adds meaning by listing topic values, giving examples, and explaining how explicit parameters override topic-mapped ones, improving understanding 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 'Cross-venue spread between Kalshi and Polymarket' and specifies the two modes, distinguishing it from sibling tools like polymarket_arbitrage or 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?

The description explains two modes (topic vs explicit) and provides examples, giving clear context on when to use each. It could be more explicit about when not to use, but the guidance is 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 provided, so description carries full burden. It discloses the dual behavior (retrieve by key or list all) and mentions persistence across sessions, but does not detail what happens if key is missing, or any error conditions.

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 concise sentences that front-load the core purpose. Efficient and clear.

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 retrieval tool with no required parameters and no output schema, the description sufficiently explains behavior. It mentions session persistence which adds valuable context.

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 the detail that omitting key lists all memories, which is already implied by optionality in schema. No additional parameter info 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 that the tool retrieves a memory by key or lists all memories when key is omitted, which distinguishes it from 'remember' and 'forget' 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?

It explicitly says when to use the tool ('retrieve context you saved earlier') and implies when not to (omit key to list all), but does not explicitly mention alternatives or 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?

No annotations exist, so description carries full burden. It transparently explains the parallel fan-out to three sources, output format (changes, count, URIs), and parameter formats. It does not disclose any side effects or rate limits, but the behavior is well-described.

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 focused sentences: purpose, behavior (fan-out), and parameter details. Every sentence adds value; no filler. Front-loaded with purpose.

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 covers output structure (structured changes, total_changes, URIs) and sources. Lacks details on error handling or rate limits but is sufficient for understanding main behavior.

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 clear param descriptions. The tool description reiterates some param info (since formats, type enum) but adds little new meaning beyond schema. 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's purpose: reporting what's new about an entity since a time. It specifies the fan-out for 'company' and gives explicit use cases ('brief me on what happened', 'change-monitoring'), distinguishing it from siblings like 'entity_profile'.

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 ('use for change-monitoring') but does not explicitly exclude alternatives or state when not to use. It implies use for change monitoring, not full profile, which is good but lacks exclusions.

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 adds behavioral context beyond what annotations provide (none here): authenticated users get persistent memory, anonymous sessions last 24 hours. 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?

Three sentences, front-loaded with action and use cases. Efficient and informative, though could be slightly more concise.

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 only 2 parameters, the description is complete enough: explains persistence behavior, use cases, and key-value nature. No missing context for typical use.

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 description adds little beyond schema. It gives example keys like 'subject_property' and clarifies value as 'any text', but these are also present in 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?

Description clearly states 'Store a key-value pair in your session memory', with specific verb 'store' and resource 'session memory'. It distinguishes itself from siblings (recall, forget) by explicitly mentioning memory persistence and use cases.

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 explicit use cases: 'save intermediate findings, user preferences, or context across tool calls'. However, it does not explicitly state when not to use or compare to siblings like recall or forget.

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

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

With no annotations, the description carries full burden. It discloses return values (ticker, CIK, name, URIs), version limitations (company only), and performance benefit (replaces 2-3 calls). Could mention idempotency but acceptable for a lookup 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?

Three sentences, front-loaded with purpose, each sentence adds new information (purpose, examples/return, benefit). No redundancy or fluff; every word 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?

Given no output schema, description adequately explains return values. It covers versioning and input options. Lacks error handling info but is complete for a simple lookup tool in the context of Pipeworx siblings like ask_pipeworx.

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 description adds value by providing concrete examples (AAPL, 0000320193, Apple) and clarifying accepted formats beyond the terse schema descriptions. The enum for type is reinforced.

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 (resolve), resource (entity), and outcome (canonical IDs across Pipeworx data sources). It distinguishes itself from siblings by emphasizing it replaces multiple lookup calls and specifies version 1 scope.

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

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

The description provides clear context for when to use (single call for canonical IDs) and gives examples of acceptable inputs. However, it lacks explicit guidance on when not to use it or alternatives like ask_pipeworx for general queries.

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 no destructiveness. The description adds valuable behavioral details: the tool probes each entity internally, ranks by score, and surfaces most/least recognized. 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?

The description is three sentences, front-loaded with the main action, and every sentence contributes essential information. 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 no output schema, the description covers inputs (4 parameters well-documented), process (probing, ranking), and output (ranked list with score, confidence, signal density). It could mention error handling or edge cases, but overall it is sufficiently complete 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?

Schema coverage is 100%, so the schema documents all parameters. The description adds meaning by explaining that the first entity is treated as the 'subject' for narrative, and clarifying the relationship between models and _apiKey. 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: comparing AI visibility across multiple entities side-by-side. It specifies the mechanism (probes each entity with ai_visibility_check), output (ranked list by score), and provides a concrete use case example distinguishing it from sibling tools like ai_visibility_check.

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 frames the tool as useful for competitive AI-marketing audits and gives an example question. While it does not name specific siblings for when not to use, the context of comparing multiple entities is clear from the description.

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?

Without annotations, description details the return value (verdict, structured form, actual value with citation, percent delta). It does not mention potential limitations like data availability or rate limits, but for a read-like fact-checking tool this is adequate.

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 efficient sentences, front-loaded with the core purpose, and every sentence adds essential information including scope, output, and value proposition.

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 one parameter, no output schema, no annotations, the description is remarkably complete: it explains input, output, supported domains, data sources, and comparative advantage over alternatives.

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?

Single parameter 'claim' has a clear description with examples (100% schema coverage). Description adds value by providing concrete examples that clarify format 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 fact-checks natural-language claims using authoritative sources (SEC EDGAR + XBRL) for company-financial data, lists possible verdicts, and distinguishes itself from sibling tools by replacing multiple sequential calls.

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 specifies supported claim types (company-financial, US public companies) and provides examples. Implies when not to use (non-financial or non-US claims) and frames it as a replacement for alternative multi-step approaches.

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