Mimiq: test pages and copy on simulated people

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds that each person answers independently, which is a minor behavioral detail, but does not disclose other aspects like rate limits, concurrency effects, or the nature of simulation. It does not contradict 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 no waste. The core action is front-loaded, and the use cases are appended efficiently. 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?

For a tool with 7 parameters and no output schema, the description covers the essential purpose and return content. It does not mention error cases or performance characteristics, but given the read-only, non-destructive annotations and the tool's straightforward nature, it is sufficiently complete for an agent to call 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 description coverage is 86%, so most parameters are already documented. The description reinforces the options constraint (2-10) and adds context about output ('pick and reasoning') that is not in the schema. However, it does not add meaning beyond what the schema provides for other params like count, context, or timeout.

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

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

The description clearly states the action (ask) and resource (simulated people), specifies the format (multiple-choice, 2-10 options), and explains what is returned (each person's pick and reasoning). It also lists concrete use cases (naming, positioning, feature decisions) that distinguish it from sibling tools focused on testing copy, components, or flows.

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

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

The description provides explicit use cases ('Use it for naming, positioning, or which feature to build next'), giving clear context for when to invoke the tool. However, it does not explicitly state when not to use it or mention alternative sibling tools, though the sibling names are distinct enough to infer separation.

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

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

Annotations declare readOnlyHint=true and destructiveHint=false, so the safety profile is already covered. The description adds valuable behavioral context: the same people see both versions, the call combines forecast and movement, the free tier limits to 10 simulated people per version without a key, and the forecast's accuracy caveats (strong on headlines, weak on emails/texts/ads/web pages). This goes beyond annotations and helps the agent set expectations. It doesn't describe rate limits beyond the daily free allowance, but the key behavioral traits are disclosed.

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 dense but well-structured: it front-loads the core purpose and methodology, then lists outputs, then covers free tier/key requirements, and ends with accuracy caveats. Every sentence earns its place, though the accuracy caveats paragraph is long and could be trimmed without losing essential guidance. 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?

Given the tool's complexity (6 params, no output schema), the description is quite complete: it explains the comparison method, output fields, free tier limits, key requirements, and accuracy caveats. The only gaps are explicit rate limits for the daily free allowance and a clearer statement of when to use sibling tools (e.g., compare_urls for URLs). But for an agent deciding whether to call this tool, the essential context is present.

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 83%, so the schema already documents most parameters. The description adds meaning by explaining the comparison methodology (same people see both versions), the free tier limit (up to 10 simulated people per version without a key), and the output structure (call, reason, movement counts, objections, report link). It also clarifies the 'count' parameter's credit usage ('Uses count credits to recruit plus count for version B'). 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 states a specific verb ('compare'), a resource ('two versions of copy or an email'), and a clear outcome ('Mimiq's call on which to ship'). It also distinguishes itself from siblings by naming the comparison method (same simulated people see both versions) and the output (call, reason, movement counts, objections, report link). This is a clear, specific purpose that an agent can act on.

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 this tool: when comparing two versions of copy or email on the same simulated people. It also provides context about free A/B testing limits and key requirements. However, it does not explicitly name sibling alternatives or state when NOT to use this tool (e.g., when comparing URLs, use mimiq.compare_urls). The context is clear but exclusions are implicit.

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?

Even though readOnlyHint and destructiveHint already signal a safe read operation, the description goes well beyond annotations. It discloses the exact output shape, the order-balancing methodology, the free-tier limit, the key-based scaling, and the pre-registered result that the forecast did not beat the simplest guess. This is rich, honest behavioral disclosure.

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 organized logically through output, constraints, and caveat. It is mostly efficient, though the sentence 'The same people see page A and page B' somewhat repeats the earlier 'on the same simulated people' clause, adding minor 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?

Given there is no output schema, the description fully compensates by listing the returned call, reason, movement counts, top objections, and report link. It also covers key operational context: public URL requirement, free allowance, key-based scaling, and reliability caveat. An agent has enough information to invoke the tool correctly and interpret its results.

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 high at 80%, so the schema already documents most parameters. The description adds meaningful semantics beyond that: URLs must be publicly reachable, the same simulated people see both pages, and the free allowance caps tests at 10 simulated people without a key. These details clarify the count and URL parameters in ways the schema alone does not.

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 a specific verb and resource: compare two web pages and get Mimiq's call on which to ship. It also gives concrete examples like preview deployment versus production. However, it does not explicitly distinguish itself from sibling tools such as compare_copy or test_page, so it stops short of full sibling differentiation.

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 use context: comparing two pages on the same simulated audience to make a shipping decision. It adds practical constraints such as needing public URLs, the free allowance, and the caveat that the forecast should be treated as a fast first read. It does not explicitly state when not to use this tool or which sibling to prefer, so exclusions are missing.

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

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

Annotations already mark the tool as read-only and non-destructive. The description adds useful context: it runs a simulated-user test and, with a key, returns a report link. This goes beyond the annotations without contradicting them.

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 tight sentences with no filler. The first sentence establishes the core purpose and outcome; the second covers input requirements and output behavior. 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 absence of an output schema, the description does a good job conveying what the agent will get back ('hear whether they understand it...' and a report link). It could be slightly richer by clarifying the ambiguous 'key' and by differentiating from page/flow testing, but it is largely complete.

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

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

Schema coverage is high at 83%, so the baseline is 3. The description adds valuable semantic guidance by clarifying that component_html and component_text can be provided individually or together, helping agents understand the intended input combination 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 uses a specific verb ('show') and resource ('UI component'), enumerates concrete component types, and states the outcome ('understand it, trust it and would use it'). It is clearly distinguishable from sibling tools like test_copy or test_page.

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 usage context is implied by the enumerated component types and the instruction to provide component_html or component_text. However, it does not explicitly say when to choose this tool over test_page or test_flow, nor does it state any 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?

The description reveals behavior beyond the annotations and schema: it returns each person's reaction, objections, helpful input, counts, and optionally a report link. It also clarifies that the people are simulated, which sets expectations about the tool's nature. Annotations already cover read-only safety, so the description does not need to repeat that.

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 primary purpose and is generally tight. The only minor redundancy is the back-to-back explanation about variant_b and mimiq.compare_copy, but it does not seriously bloat the description.

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 includes the tool's purpose, its sibling distinction, return contents, and the report-link behavior. Since there is no output schema, the explicit description of outputs is valuable. Parameter semantics are mostly handled by the input schema, so nothing critical is missing for an agent to invoke 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 description coverage is 80% (only timeout_seconds lacks a description), so the schema carries most parameter meaning. The description adds context about copy types and clarifies that variant_b triggers a comparison, but much of the variant_b behavior is already stated in the schema. This is a passable baseline.

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 and resource ('Show one piece of copy to simulated people') and gives concrete examples of what counts as copy. It also explicitly distinguishes itself from mimiq.compare_copy by naming the sibling and describing the single-version case.

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 an explicit usage directive: to compare two versions, use mimiq.compare_copy. It then notes that this tool still accepts variant_b and runs the same comparison, which slightly muddies the recommendation but still tells the agent the available options and their relationship.

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

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

Beyond the annotations, the description discloses that this performs real browser interaction (clicking and typing), has a meaningful cost (30 credits per person), is slower than test_page, and returns per-step progress with stop reasons. This is substantial behavioral context that the annotations alone do not provide.

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 deliver purpose, method, cost, performance comparison, and return value with no filler. The most important information is front-loaded, making it easy for an agent to quickly grasp the tool's role.

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 rich schema and useful annotations, the description fills the remaining gaps: what the tool does, how it behaves, what it costs, and what it returns. A caller has enough information to select and invoke this tool appropriately.

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 83%, so the schema already documents most parameters well. The description adds some context around credit usage and the purpose of the flow, but it does not clarify individual parameters beyond what the schema already states. A baseline 3 is appropriate.

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

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

The description states a specific verb and resource: "Send simulated people through a multi-step flow" in a real browser to find where they get stuck or give up. This clearly distinguishes it from siblings like test_page and test_component by emphasizing multi-step journeys.

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 clear context for when this tool applies: multi-step flows like signup or checkout in a real browser. It also provides a comparative signal by noting it is "Slower than test_page." It does not explicitly state exclusions or name all alternative tools, so it stops short of 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?

The annotations already mark the tool as read-only and non-destructive, and the description adds substantial behavioral context: simulated people scroll, voice confusions/doubts, and the call returns actions, thoughts, objections, and counts. It also discloses the public URL requirement and the optional report link with a key, enriching the agent's understanding beyond the structured 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 front-loaded with the core purpose and then provides usage guidance, prerequisites, and output expectations. Each sentence adds useful information, though some phrases like 'who would stay, who would leave, and why' are illustrative rather than strictly necessary.

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, when to use it, prerequisite URL handling, and the high-level output shape, which is especially valuable since there is no output schema. It does not need to restate all parameter details because the schema is well documented, but it could have been slightly more explicit about how goals and audiences influence the test.

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 80%, so the schema already explains most parameters. The description adds little beyond what the schema provides for the url parameter (public reachability and tunnel), and it does not add meaning for goal, count, audience, or timeout_seconds. This is acceptable given the high baseline but not a standout.

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

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

The description clearly identifies the tool's job: show a web page to simulated people and collect their reactions, including who stays, who leaves, and why. This distinguishes it from the sibling tools, which target components, copy, text, flows, or audience research rather than full-page reactions.

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 when to use it: 'after you build or change a landing, pricing or signup page, then fix what they raised and test again.' It also notes the public-URL prerequisite and localhost tunnel alternative. It does not explicitly name sibling tools or when not to use it, so it stops short of 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?

Annotations already mark the tool readOnly and non-destructive; the description adds that the response contains each simulated person's reaction, objections, and suggested help, plus a report link when a key is present. This is useful post-condition context beyond the annotations, though it doesn't discuss non-determinism or timeout behavior.

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

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

Three sentences with no filler; the core behavior is front-loaded and the parentheses compactly illustrate supported text types. 'With a key' is slightly vague, but the overall structure is tight and scannable.

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 what the tool does, what to pass, and what comes back, which is sufficient given there is no output schema. It does not explain timeout_seconds or explicitly route among sibling tools, so it is helpful 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 coverage is 80%, so the baseline is 3. The description reinforces the meaning of goal ('Pass goal to say what the text should achieve') and references the text input, but adds no information about count, audience, or timeout_seconds beyond what the 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?

The description opens with a concrete verb ('Show') and a specific resource ('any text' with examples) plus the purpose ('hear how they react'), so an agent knows exactly what the tool does. It is broadly distinguishable from page/component/flow siblings, though it does not explicitly contrast with the closely-named test_copy or compare_copy.

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 this is the tool for evaluating any text with simulated people and instructs the agent to pass a goal for what the text should achieve. However, it never states when to prefer this over siblings like test_copy or compare_copy, nor does it give any exclusion criteria.

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