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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action of getting a list, without mentioning whether this is a read-only operation, if it requires permissions, what format the list returns in, or any rate limits. This leaves significant gaps for a tool that likely interacts with collections.

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 appropriately sized with two sentences: one stating the purpose and another explaining the parameter. It's front-loaded with the main action, though the parameter explanation could be integrated more seamlessly. There's no wasted text, making it 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the returned list looks like (e.g., structure, fields), any prerequisites like authentication, or how it differs from sibling tools. For a tool with one parameter but no structured context, this leaves too many unknowns for effective 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?

The schema description coverage is 0%, but the description compensates by explaining the single parameter 'collection_id' as 'The ID of the collection to get variables for'. This adds meaning beyond the schema's title 'Collection Id', though it doesn't detail format or constraints. With one parameter, this meets the baseline for minimal viability.

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 ('Get') and resource ('list of all variables in a collection'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_variable_collections', which could be a related alternative for finding collections rather than variables within one.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_variable_collections' or specify contexts where this tool is preferred, leaving the agent to infer usage based on the name alone.

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 of behavioral disclosure. It states the tool retrieves logs, implying a read-only operation, but doesn't specify permissions, rate limits, data format details, or potential side effects. The description adds minimal behavioral context beyond the basic action, leaving gaps for a tool with no annotation coverage.

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

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

The description is appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections add structure, though they could be integrated more seamlessly. Every sentence contributes value, with no redundant information, making it efficient overall.

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 low complexity (1 parameter, no nested objects) and lack of annotations and output schema, the description is minimally complete. It covers the purpose, parameter meaning, and return type, but lacks details on behavioral traits, error handling, or output structure, which could be beneficial for an AI agent.

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 description adds meaningful semantics for the single parameter 'dialog_id' by explaining it's 'The ID of the dialog to retrieve logs for,' which clarifies its purpose beyond the schema's title 'Dialog Id.' With 0% schema description coverage and only one parameter, this compensation is adequate, though it doesn't detail format constraints or examples.

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

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

The description clearly states the tool's purpose: 'Get the dialog logs for a specific dialog ID.' It specifies the verb ('Get') and resource ('dialog logs'), and distinguishes it from siblings like 'search_dialog_logs' by focusing on retrieval by ID rather than search. However, it doesn't explicitly differentiate from other siblings like 'get_collection_variables' beyond the resource name.

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 context with 'Useful for retrieving conversation history and analytics,' suggesting when to use it. It distinguishes from 'search_dialog_logs' by specifying retrieval by ID, but doesn't provide explicit when-not-to-use guidance or alternatives beyond this sibling. No prerequisites or exclusions are 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?

No annotations are provided, so the description carries full burden. It states it 'retrieves' data, implying a read-only operation, but doesn't disclose behavioral traits like error handling (e.g., what happens if ID is invalid), authentication needs, rate limits, or response format details beyond 'dictionary'. This leaves significant gaps for a tool with no annotation coverage.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a utility note and structured parameter/return sections. Every sentence adds value, though the utility note is somewhat generic. The structure with 'Args' and 'Returns' headings aids 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?

Given no annotations, no output schema, and low schema coverage, the description is incomplete. It covers the basic operation and parameter but lacks details on error cases, authentication, rate limits, and the structure of the returned 'dictionary'. For a retrieval tool with no structured support, this leaves the agent under-informed.

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

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

Schema description coverage is 0%, so the description must compensate. It documents the single parameter 'miniapp_id' with a brief explanation ('The ID of the miniapp to retrieve'), adding basic meaning beyond the schema's title. However, it doesn't specify ID format, constraints, or examples, which is insufficient given the low 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?

The description clearly states the tool's purpose: 'Get a specific miniapp by its ID' (verb+resource). It distinguishes from siblings like 'search_miniapps' by specifying retrieval of a single item rather than searching. However, it doesn't explicitly differentiate from 'get_orchestrator_app' or other get_* tools beyond the resource type.

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 minimal guidance: 'Useful to return various information about a miniapp' suggests when to use it but offers no explicit when-not-to-use advice or alternatives. It doesn't mention prerequisites like needing a valid ID or compare with 'search_miniapps' for when searching might be better than direct retrieval.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the output as 'a graph structure of nodes and edges that describes a dialog flow,' which adds useful context about the return format. However, it lacks details on permissions, error handling, or other behavioral traits like rate limits or side effects, which are important for a tool with no annotation coverage.

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

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

The description is well-structured and appropriately sized. It starts with the core purpose, adds user-friendly query examples, describes the output, and details the parameter in a separate 'Args' section. Each sentence adds value without redundancy, though the user query examples could be slightly trimmed for brevity.

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 moderate complexity (1 parameter, no annotations, no output schema), the description is partially complete. It covers the purpose, parameter semantics, and output format, but lacks behavioral details like error cases or usage prerequisites. Without an output schema, the description does explain the return value, which helps, but more context 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 has 0% description coverage, so the description must compensate. It explains the 'canvas_id' parameter as 'The ID of the canvas to get' and specifies that it's 'contained in the search_orchestrator_apps results,' adding meaningful context beyond the schema. This effectively documents the single parameter, though it could provide more details like format or validation rules.

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: 'Get an Orchestrator application canvas by ID.' It specifies the verb ('Get') and resource ('Orchestrator application canvas'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_miniapp' or 'search_orchestrator_apps,' which might retrieve similar resources.

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 some usage context by mentioning that users might ask for this with phrases like 'show me the app' and noting that the canvas_id is 'contained in the search_orchestrator_apps results.' This implies a workflow but doesn't explicitly state when to use this tool versus alternatives (e.g., 'get_miniapp' or 'search_orchestrator_apps'), leaving gaps in guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It partially succeeds by detailing the return type ('dict: Search results containing matching dialogs') and default values for parameters like 'from_date' and 'size.' However, it misses critical behavioral aspects such as pagination, rate limits, authentication requirements, or error handling, leaving gaps for a tool with 10 parameters.

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 well-structured with a purpose statement, usage examples, and a parameter breakdown. Most sentences earn their place, such as the warning about 'apps' IDs. However, the user request examples ('find sessions', etc.) could be more tightly integrated, and the 'Returns' section is slightly redundant given the parameter details, though it provides closure.

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 10 parameters, no annotations, and no output schema, the description is moderately complete. It excels in parameter semantics but lacks behavioral context like pagination or error details. The absence of an output schema means the description should ideally elaborate more on return values, though it does state the return type. It's adequate but has clear gaps 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?

Given 0% schema description coverage, the description compensates fully by providing detailed semantic explanations for all 10 parameters. It clarifies the 'apps' parameter's distinction from orchestrator app IDs, specifies formats for date parameters, explains ANI as 'phone number of the caller,' and defines defaults and filtering logic (e.g., 'steps_gt' filters dialogs with steps greater than a number). This adds significant value beyond the bare 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 as 'Search dialogs using various filter criteria,' which is a specific verb+resource combination. It distinguishes from some siblings like 'get_dialog_logs' by emphasizing search/filtering capabilities, though it doesn't explicitly contrast with all search-related siblings like 'search_miniapps' or 'search_numbers.'

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 by mentioning user requests like 'find sessions' or 'search logs,' which helps an agent understand when to use this tool. It includes a specific prerequisite for the 'apps' parameter (must get IDs from 'search_orchestrator_apps' first), but lacks explicit when-not-to-use guidance or alternatives compared to other search tools in the sibling list.

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 of behavioral disclosure. It states the tool returns a list of miniapps, but doesn't describe key behaviors such as pagination, rate limits, authentication requirements, error handling, or whether the search is case-sensitive. This leaves significant gaps for an AI agent to understand how to interact with it effectively.

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 the first sentence stating the purpose and the second elaborating on the return value. The parameter documentation is brief but relevant. There's no wasted text, though it could be more structured (e.g., separating usage notes).

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 (a search function with one parameter), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover behavioral aspects, output format, error cases, or usage context, making it inadequate for reliable tool invocation by an AI agent.

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 description adds minimal semantics beyond the input schema, which has 0% description coverage. It clarifies that 'search_term' is optional and used to filter miniapps, but doesn't explain the search scope (e.g., by name, description), format, or examples. With one parameter and low schema coverage, this provides basic but insufficient detail.

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

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

The description clearly states the tool's purpose with a specific verb ('Search') and resource ('miniapps'), and it explains what the tool returns ('a list of miniapps that match a search term'). However, it doesn't explicitly differentiate from sibling tools like 'search_dialog_logs' or 'search_orchestrator_apps', which perform similar search operations on different resources.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_miniapp' (which might retrieve a specific miniapp) or other search tools, nor does it specify any prerequisites, exclusions, or contextual cues for usage.

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 of behavioral disclosure. It only states the basic action ('search') and mentions an optional parameter, but lacks details on permissions, rate limits, pagination, or what the search returns (e.g., list of numbers, metadata). For a search tool with no annotation coverage, this is a significant gap in transparency.

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 appropriately sized and front-loaded, with the core purpose in the first sentence and parameter details in a brief 'Args' section. There's no unnecessary verbosity, though it could be slightly more structured (e.g., bullet points). Every sentence adds value, making it 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 complexity (search tool with no annotations, no output schema, and 1 parameter with 0% schema coverage), the description is incomplete. It doesn't explain what the tool returns, how results are formatted, or any behavioral aspects like error handling. This makes it inadequate for an agent to use the tool effectively without additional 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?

The description adds minimal semantics beyond the input schema. It explains that 'search_term' is optional and used to filter numbers, which aligns with the schema's 'anyOf' type and default null. However, with 0% schema description coverage, the description doesn't compensate by detailing format, examples, or constraints, leaving the parameter only partially documented.

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: 'Search (phone) numbers with optional search term.' It specifies the verb ('search'), resource ('phone numbers'), and scope ('with optional search term'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'search_dialog_logs' or 'search_variable_collections' beyond the resource type.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions an optional search term but doesn't explain scenarios for using it, prerequisites, or comparisons to other search tools in the sibling list. This leaves the agent without 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 are provided, so the description carries the full burden of behavioral disclosure. It only states the basic action ('search') without details on permissions, rate limits, pagination, or response format. This is insufficient for a tool with no annotation coverage.

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

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

The description is brief and front-loaded with the purpose, followed by a simple parameter explanation. It avoids unnecessary words, though the structure could be slightly improved by integrating the parameter note more seamlessly.

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 annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on behavior, error handling, and output, which are crucial for a search tool with potential 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?

The description adds minimal semantics by noting the parameter is 'optional' and 'to filter apps,' but schema description coverage is 0%, so the schema provides no details. The description partially compensates but doesn't fully explain usage, format, or constraints, resulting in a baseline score.

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 as 'Search Orchestrator apps with optional search term,' which specifies the verb ('search'), resource ('Orchestrator apps'), and optional filtering. However, it doesn't explicitly differentiate from sibling tools like 'search_miniapps' or 'get_orchestrator_app,' which would be needed for a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_miniapps' or 'get_orchestrator_app,' nor does it specify contexts, prerequisites, or exclusions for usage.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the basic action of searching with optional filtering, lacking details on permissions, rate limits, pagination, or what the search encompasses (e.g., partial matches, case sensitivity). This leaves significant gaps for a search tool.

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

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

The description is brief and front-loaded with the main purpose, followed by parameter details. However, the 'Args:' section is somewhat redundant since the schema already documents the parameter, and it could be more integrated into the flow.

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 annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover behavioral aspects like search behavior, result format, or error handling, making it inadequate for effective tool selection and invocation.

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 description adds minimal semantics by noting the parameter is optional and for filtering, but with 0% schema description coverage and only one parameter, it doesn't fully compensate. It doesn't explain how the search term is applied (e.g., to names, descriptions, or content), so the value beyond the schema is limited.

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') and resource ('variable collections'), making the purpose understandable. However, it doesn't differentiate this tool from sibling search tools like 'search_dialog_logs' or 'search_miniapps' beyond the resource type, so it doesn't fully distinguish from alternatives.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_collection_variables' or other search tools. It mentions an optional search term but doesn't explain when filtering is appropriate or what this tool offers that others don't.

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 states this is a 'set' operation (implying mutation/write), but doesn't disclose behavioral traits like required permissions, whether changes are reversible, rate limits, or what happens on success/failure. The description adds minimal context beyond the basic action.

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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by a structured 'Args' section. The prompt_type list is lengthy but necessary for clarity. No wasted sentences, though the structure could be slightly more streamlined.

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 3 parameters, no annotations, and no output schema, the description is moderately complete. It excels at parameter semantics but lacks behavioral transparency and usage guidelines. For a mutation tool with no safety annotations, more context on permissions, side effects, or response format 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?

Schema description coverage is 0%, so the description must compensate. It provides detailed semantics for all 3 parameters: miniapp_id ('The ID of the miniapp to set the prompt for'), prompt_type (with 15 specific enum-like values and explanations), and prompt ('The prompt text to set'). This adds significant meaning beyond the bare 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: 'Set various types of prompts for a specific miniapp. Unified interface for setting welcome, initial, error and reaction prompts.' It specifies the verb ('set'), resource ('prompts for a specific miniapp'), and scope ('various types'), though it doesn't explicitly differentiate from sibling tools (which appear to be mostly read/search operations).

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing miniapp_id), when not to use it, or how it relates to sibling tools like get_miniapp or search_miniapps. Usage is implied through the description but not explicitly stated.

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