Mimiq - Synthetic User Testing

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

Annotations declare readOnlyHint and non-destructive nature. The description adds critical behavioral context beyond annotations: it clarifies the audience is 'synthetic' and 'simulated,' explains that personas 'vote independently with reasoning,' and discloses the return format ('each respondent's vote and reasoning') since no output schema exists.

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?

Excellent structure with zero waste: sentence 1 defines purpose, sentence 2 explains mechanism, sentence 3 provides use cases with parenthetical examples, and sentence 4 specifies return values. Information is front-loaded and every clause 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 7-parameter tool without an output schema, the description is complete. It compensates for the missing output schema by explicitly stating what gets returned. Combined with high schema coverage and clear annotations, the description provides sufficient context for correct invocation and result handling.

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 86% schema description coverage, the schema carries the primary documentation burden. The description adds minimal semantic value beyond the schema, primarily reinforcing the '2-10 answer options' constraint and implying the audience parameter through references to 'synthetic audience.' Baseline 3 is appropriate given the schema's completeness.

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 specific verb ('Run') and resource ('survey question on a synthetic audience'), clearly stating the tool gauges 'preferences, priorities, or opinions.' It effectively distinguishes itself from the testing-focused siblings (test_component, test_copy, etc.) by focusing on audience research rather than UX validation.

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 positive guidance with concrete use cases: product decisions ('which feature should we build next?'), naming, and positioning. While it lacks explicit 'do not use for' exclusions or named alternatives, the contextual examples strongly signal appropriate usage scenarios that clearly differentiate it from the test_* 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?

While annotations declare readOnlyHint=true and idempotentHint=true, the description adds valuable behavioral context by explaining that 'Simulated users evaluate clarity, trust signals, and whether they would interact with the component'. It does not contradict annotations and clarifies the evaluation methodology beyond the structured hints.

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 efficiently organizes four sentences that progress from core functionality to constraints, usage conditions, and behavioral mechanisms without redundancy. Every sentence contributes distinct value regarding the tool's purpose, requirements, and evaluation methodology.

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?

While the description adequately covers input parameters and usage context, it lacks information about the return value or output format despite the absence of an output schema. For a tool with six parameters and logical constraints between them, the description meets baseline needs but leaves gaps regarding the structure of evaluation results returned to the 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?

With 83% schema coverage, the description adds crucial semantic information that 'At least one of component_html or component_text must be provided', compensating for the schema's empty 'required' array. It also provides concrete examples of component types that guide parameter usage, though it does not describe the timeout_seconds 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 specifies the action 'Evaluate' and the resource 'UI component' with concrete examples including 'button, card, form, modal, navigation, pricing table'. It clearly distinguishes from sibling tools like test_page and test_flow by limiting scope to individual UI elements rather than full pages or user 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?

It provides clear context stating 'Use when you want feedback on whether a specific UI element is clear, trustworthy, and actionable'. However, it lacks explicit guidance on when to choose this over siblings like test_copy or test_text, though the component examples provide implicit differentiation.

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

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

Annotations declare readOnly/idempotent/destructive status. Description adds critical context that this uses 'simulated users' (not real ones) and details return formats: 'raw persona reactions and monologues' for single variant, 'side by side' comparison and 'winner' for A/B. 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 distinct clauses efficiently structured: purpose statement, usage examples, and return value explanation. No redundant text. The progression from general capability to specific use cases to output formats is logical and front-loaded.

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

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

Despite lacking an output schema, the description fully compensates by detailing exactly what gets returned in both single-variant and A/B modes. Combined with good annotations and high schema coverage, this provides sufficient context for agent invocation decisions.

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% (4/5 params described). Description adds semantic value by explaining how variant_a and variant_b interact (optional second variant triggers winner determination) and that 'count' refers to simulated users per variant. Does not compensate for missing timeout_seconds schema description, but adds meaningful context for the variant logic.

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 opens with specific verb ('Test copy') and resource ('simulated users'), clearly stating it performs A/B testing. It distinguishes from siblings by enumerating specific content types like 'headlines, taglines, value propositions, email subject lines' rather than generic 'text' or UI components.

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 when' clause with concrete examples (headlines, CTAs, product descriptions). Explains behavioral difference between single variant (raw reactions) and A/B mode (comparison). Lacks explicit 'when not to use' or named sibling alternatives, preventing 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?

Adds critical behavioral context beyond annotations: performance ('slower'), mechanism ('uses real browser sessions'), simulation depth ('clicking links, filling forms'), and return format ('raw per-persona journey data') since no output schema exists. 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?

Four dense sentences with zero waste: defines function, explains mechanism, states usage criteria, and documents return value. Front-loaded with the core capability.

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 lack of output schema, description compensates by detailing return format ('step-by-step actions and drop-off points'). Covers sibling differentiation, performance trade-offs, and safety profile (via annotations). Complete for tool 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?

With 83% schema coverage (>80%), baseline is 3. Description adds no parameter-specific semantics beyond schema (e.g., no elaboration on timeout_seconds which lacks description, or detailed guidance on goal/audience strings).

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?

Specific verb 'simulation' with clear resource 'multi-step user flow' and concrete examples (signup, onboarding, checkout). Explicitly distinguishes from sibling 'test_page' by contrasting depth vs speed.

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

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

Explicit when-to-use: 'complex flows where you need to find exactly WHERE users get stuck'. Implicit when-not-to-use via performance comparison 'Slower than test_page', guiding users toward test_page for simpler needs.

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 readOnly/idempotent/safe properties, so the description appropriately focuses on behavioral mechanics rather than safety. It adds valuable context about the simulation process ('scroll through the entire page like real visitors'), what they evaluate ('react honestly about what confused them'), and output structure ('raw per-persona results with actions, monologues, objections'). Does not mention latency implications of high count values.

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 sentences efficiently structured: (1) core function, (2) usage timing, (3) behavioral mechanism, (4) return values. Every sentence earns its place. The 'Returns:' clause is particularly valuable given the absence of an output schema. No redundant or filler content.

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 compensates by detailing the return structure ('raw per-persona results with actions, monologues, objections, and suggestions'). Combined with comprehensive annotations and 80% schema coverage, the description provides sufficient context for an agent to understand the tool's complete lifecycle from input to 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?

Schema description coverage is 80% (high), establishing a baseline of 3. The description conceptually maps to parameters (e.g., 'simulated users' implies 'count' and 'audience', 'conversion blockers' implies 'goal') but does not add technical details, constraints, or usage guidance beyond what the schema already provides for url, goal, count, and audience. The timeout_seconds parameter remains undocumented in both schema and description.

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 'tests a web page on simulated users to find UX issues, confusing copy, and conversion blockers'—specific verb, resource, and outcome. It distinguishes from siblings (test_component, test_copy, test_flow) by emphasizing 'entire page' testing, noting users scroll through 'hero, features, pricing, CTAs, and footer' as opposed to isolated components or text snippets.

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 timing guidance: 'Use this BEFORE shipping any landing page, pricing page, signup flow, or marketing page.' Lists specific applicable scenarios. However, it does not explicitly contrast with sibling tools like test_component or test_flow to clarify when to prefer those alternatives over this full-page approach.

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. The description adds valuable behavioral context about what the tool returns: 'raw persona reactions, objections, and what they say would help,' which compensates for the missing output schema. 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 well-structured sentences with zero waste. Front-loaded with the core action, followed by examples (em-dash usage), use case guidance, and return value disclosure. 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 lack of output schema, the description effectively documents return values ('raw persona reactions, objections...'). Combined with good annotations and high schema coverage, this provides sufficient context for tool invocation, though it could clarify how this differs from 'test_copy'.

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% (high), so the schema carries the primary documentation burden for parameters like 'goal', 'count', and 'audience'. The description mentions 'text content' aligning with the required 'text' parameter but does not add semantic details beyond what the schema provides. Baseline 3 is appropriate.

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

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

Opens with specific verb 'Test' + resource 'text content on simulated users' and provides concrete examples (positioning statements, error messages, onboarding copy). The specificity distinguishes it from sibling tools like test_component or test_flow by clearly scoping to text-only 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?

Contains explicit when-to-use guidance: 'Use when you want honest reactions to written content.' However, it lacks explicit differentiation from the sibling tool 'mimiq.test_copy' which likely overlaps in functionality, and does not state 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.