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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it waits for the answer, supports optional free-text notes when allow_free_text is true, and returns the selected option label in answer/selected. It doesn't describe timeout behavior in prose, but the schema covers timeout_ms, so the description adds solid value without contradicting 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 compact and front-loaded: the core behavior is in the first sentence, followed by usage guidance and alternatives. Every sentence earns its place, and there is no redundant restating of schema fields. It is appropriately sized for a tool with this complexity.

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 is complete for an interactive choice tool. It covers what the tool does, when to use it, how it differs from siblings, the free-text extension, and the return value. The output schema exists, so return values need not be explained in prose. Nothing an agent needs to call it correctly is missing.

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

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

Schema description coverage is 100%, so the schema already documents all five parameters thoroughly. The description adds a little extra meaning by noting the free-text behavior tied to allow_free_text and the return value mapping, but it doesn't need to compensate for any schema gaps. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description states a specific verb and resource ('Ask the user to choose exactly one of 2-25 options and wait for the answer') and clearly distinguishes this from siblings by naming ask_confirm and ask_text as alternatives. It also specifies the return value, so an agent knows exactly what this tool does and how it differs from other ask 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 gives explicit when-to-use guidance: use when a decision has a small set of genuinely viable alternatives and the choice changes what you build. It also names alternatives and the conditions that select them ('Prefer this over ask_confirm whenever the real answer is a choice rather than yes/no, and over ask_text whenever the reasonable answers can be enumerated'). This is exemplary routing 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?

Annotations already cover read-only/non-destructive behavior, and the description adds that the tool waits for the user and returns both a boolean `confirmed` and a string `answer`. This is useful behavioral context beyond the annotations, with 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?

The description is concise and well-structured. The core purpose is front-loaded, usage guidance follows immediately, and the return behavior is summarized compactly. Every sentence 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 the input schema, annotations, and output schema, the description covers the essential usage context: when to call it, how to phrase the question, and what it returns. Nothing critical for invoking it correctly is missing.

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

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

Even with 100% schema coverage, the description adds meaningful guidance: it tells the agent to state the exact target and consequence in `question` rather than a generic 'are you sure?', and it clarifies when to omit or avoid pre-selecting a `default`. This goes well 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 explicitly states the action: ask the user a single yes/no question and wait. It distinguishes itself from siblings by emphasizing 'single yes/no' and by naming its return format, so an agent can differentiate it from ask_choice, ask_text, and ask_multi_select.

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 provides clear trigger conditions: use before destructive or irreversible actions and for binary decisions the user should own. It does not explicitly list when not to use it or name sibling alternatives, but the guidance is specific enough to route an agent correctly.

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

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

Annotations already declare readOnly and non-destructive, and the description adds the wait-for-answer blocking behavior plus the return format. It does not address timeout or cancellation behavior, but the schema's timeout_ms parameter and its description cover the timeout default.

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?

Four short sentences front-load the core behavior, then usage context, parameter advice, and return shape. Every sentence carries information, and there is no filler or repetition of schema details.

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

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

With 100% schema coverage, rich annotations, an output schema, and this description, the agent has everything needed to choose and call the tool: purpose, when to use it, min/max semantics, and return values. There are no obvious gaps.

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 documents all five parameters (100% coverage), so the baseline is 3. The description adds value with operational advice to set min/max only when the count genuinely matters and to omit them otherwise, helping an agent decide how to invoke the tool.

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 opens with 'Ask the user to select one or more options from a list and wait for the answer,' naming a specific verb, object, and behavior. The subset examples ('which platforms to target, which checks to enable') clearly set it apart from single-choice, confirm, and free-text 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?

It explicitly tells the agent when to use the tool ('Use this when the user should pick a subset') with concrete examples, plus guidance on min/max. It stops short of naming sibling tools as alternatives for non-subset cases, so the exclusion is implicit rather than stated.

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 provide readOnlyHint and openWorldHint, so the safety profile is covered. The description adds behavioral context beyond annotations: it states the tool 'waits for a typed answer' and 'Returns the typed string in `answer`,' clarifying the interaction model and return value. This is useful but not exhaustive; it doesn't mention cancellation or error handling, though that's minor given the annotations.

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

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

The description is two sentences with zero fluff. The core purpose is front-loaded, followed by targeted usage guidance and a clear return statement. Every sentence earns its place, making it highly efficient for an agent to parse.

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

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

For a tool with only one required parameter and three optional ones, the description covers all essential aspects: what it does, when to use it, and what it returns. The output schema exists, so return format is further documented. Annotations cover safety, and the alternative is explicitly named. No critical information is missing for correct 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?

Schema description coverage is 100%, so all four parameters already have meaningful descriptions in the schema. The tool description does not add additional parameter-specific semantics beyond what the schema provides. Per the rubric, baseline 3 is appropriate when the schema carries the parameter documentation burden.

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

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

The description states a clear verb+resource: 'Ask the user an open-ended question and wait for a typed answer.' It also explicitly distinguishes from ask_choice by naming the sibling and the condition for its use, making the tool's purpose unambiguous.

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 gives explicit when-to-use and when-not-to-use guidance: 'Use this only when the answer cannot be enumerated' with concrete examples, and 'If a handful of concrete answers would cover most cases, use ask_choice instead.' This directly routes the agent to the correct sibling 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 readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable context beyond annotations: 'never asks the user anything and never blocks,' which clarifies latency and interaction semantics without contradicting the annotations.

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

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

Three sentences, each earning its place: the first defines the diagnostic output, the second gives precise trigger conditions, and the third reassures about behavior. No redundancy or filler.

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

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

For a simple read-only diagnostic tool with one optional parameter, the description covers what it reports, when to use it, and its side-effect profile. Without an output schema, it still makes clear what the report will contain. Nothing essential is missing.

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

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

Schema coverage is 100%: the single optional 'verbose' parameter already has a clear description in the input schema. The description does not add any parameter-specific guidance, so the baseline of 3 applies.

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

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

The description opens with 'Diagnostic: report...' and specifies the exact outputs: whether the client supports MCP form elicitation, which fallback mode is configured, and the wait time. It distinguishes itself from the sibling ask_* tools by stating 'This tool never asks the user anything.'

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

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

Explicitly says when to call it: 'when the user asks why a question did not appear, or to verify that this MCP server is actually connected.' It also excludes the ask_* use case by saying it never asks the user anything, giving clear routing guidance.

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