Mimiq: test pages and sign-up flows on simulated people

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false), so the bar is lower. The description adds that "each person answers on their own" (independent, and implicitly variable per run), but omits cost, runtime, and non-determinism caveats relevant to a non-idempotent generation call.

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 short sentences, front-loaded with the core action and format, then use cases. The final sentence ("Each person answers on their own") is marginally redundant but does carry the independence point.

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 an output schema present, return values need not be explained, and 86% of parameters are self-documented. The description covers purpose and use cases adequately, though it says nothing about the cost/latency profile of a 300s-default, up-to-50-person generation call.

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 the schema documents audience, count, options, concurrency, and timeout. The description only restates the 2-to-10 options constraint already in the schema and adds no new parameter meaning. 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?

States a specific verb and resource ("ask simulated people a multiple-choice question") plus the returned artifacts (each person's pick and reasoning). It is distinguishable from siblings like test_copy or compare_copy, though it never explicitly names an alternative to contrast against.

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?

Gives concrete when-to-use contexts ("naming, positioning, or which feature to build next"), which is stronger than implied usage. It offers no exclusions or sibling routing, but the use cases are clear and relevant.

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 readOnlyHint=false already signalling a non-read operation, the description meaningfully extends it: it discloses credit consumption ('Uses count credits to recruit'), the keyless free allowance (10 people per version), and unusually honest accuracy caveats (76% on headlines, no better than chance on emails/ads, no edge on web pages). This is exactly the behavioral context an agent needs beyond 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?

Purpose, mechanism, return payload, pricing, and caveats are front-loaded in that order, which reads well. The final accuracy sentence is dense and long, but it earns its place as material disclosure rather than filler, keeping the overall structure 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?

For a moderately complex, cost-incurring comparison tool with an output schema, the description covers mechanism, cost, free-tier limits, output shape, and reliability caveats. Nothing an agent needs to invoke this correctly and set expectations 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 high at 83%, and the schema already documents count, format, and audience in detail, so the schema does the heavy lifting. The description reinforces the mechanics of A/B comparing the same audience and the count behavior, but adds little syntactic meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description states a specific verb+resource ('Compare two versions of copy or an email on the same simulated people') and clarifies the mechanism (same people see version A and B). It implicitly separates itself from mimiq.compare_urls via the copy/email scope, but never names a sibling to sharpen the boundary with test_copy or test_text.

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?

Usage context is implied and supported: it explains the format toggle (copy vs email), the free-tier A/B allowance, and the credit cost of count. However, there is no explicit when-to-use-this-vs-alternative guidance, e.g. how it differs from test_copy or compare_urls, leaving the agent to infer the choice.

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 substantial context beyond the annotations: public-URL requirement, the free-tier cap of one A/B test on 10 people per version with a daily allowance, key-based unlimited sizing, and an honest efficacy caveat (the forecast did not beat the simplest guess in a pre-registered run). This rate-limit, auth, and reliability disclosure is exactly the kind of value annotations cannot carry.

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?

Front-loads purpose and mechanism, then progression to returns, requirements, and caveat. It is dense but each sentence carries weight; the enumerated return list is partly redundant given an output schema exists, slightly inflating length.

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 an output schema exists, return values are already covered, and the description still supplies the requirement, quota, auth, and reliability context an agent needs. Only minor gaps remain (e.g., timeout semantics), which the schema already documents, so it is close to 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 80% and parameters are well documented in the schema itself (count, url_a, url_b, audience all have descriptions). The description reinforces the count/people concept and the free-tier 10-person limit but adds no syntax or format detail beyond the schema, so the baseline 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?

States a specific verb and resource ("Compare two web pages") with a concrete example (preview deployment vs production) and an explicit mechanism (same simulated people see both). It clearly differentiates from the sibling mimiq.compare_copy by scoping to URLs rather than 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?

Gives clear context for when to use it (comparing a preview against production, needing public URLs) plus auth/quota conditions for with/without a key. It does not explicitly name sibling alternatives like compare_copy or test_page to route the agent, so it falls 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 declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false. The description adds genuinely new context beyond those: 'Reads your account only' (auth scope) and 'spends no credits' (cost profile), which the annotations do not convey.

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

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

Two sentences, front-loaded with the core action and its two modes, followed by input provenance and the read-only/no-credit note. No wasted text.

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?

An output schema exists so return values need not be described, and annotations cover the safety profile. The description supplies the remaining essentials (input source, comparison mode, auth scope, cost), leaving nothing an agent needs in order 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 0%, so the description must carry the load, and it does add meaning: it explains the provenance of session_id (an earlier result or report URL) and, via 'two saved tests compared', the paired semantics of compare_session_id. It still doesn't state that compare_session_id is optional, so it stops short of full 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?

States a specific verb+resource ('Show the finished result of a saved Mimiq test, or two saved tests compared') and enumerates the returned content. This clearly separates it from the sibling test_* creation tools, which all produce new tests rather than retrieve saved ones.

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?

Tells the agent where inputs come from ('session IDs from an earlier test result or report URL') and implies the compare mode by describing the two-session case. It gives clear context but no explicit when-not-to-use or exclusion against siblings.

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 only assert the tool is non-read-only, non-idempotent and non-destructive. The description usefully adds that supplying a key appends a link to the full report in the Mimiq app, which is real auth/output context. It stops short of disclosing cost, run duration semantics, or what the simulated responses contain.

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

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

Two sentences, front-loaded with the capability and the evaluation dimensions, followed by the input requirement and the key-dependent output note. Zero 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?

An output schema exists, so return values need no prose. Purpose and inputs are adequately covered; the only real gap is sibling disambiguation (component vs page vs flow) and any mention of cost or processing time.

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 83%, so the schema already carries most parameter meaning. The description still adds value the schema lacks: it resolves the zero-required-parameter ambiguity by stating at least one of component_html/component_text must be supplied, and it clarifies the component types the HTML may represent.

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 ('Show ... to simulated people') plus a precisely scoped resource ('a UI component (a button, card, form, modal, navigation or pricing table)') and the outcome measured (understand, trust, use). The component framing clearly separates it from siblings like test_page, test_flow, test_copy and test_text.

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 says to provide component_html, component_text, or both, which scopes the call, but it never states when to choose this tool over test_page, test_flow or test_text. Usage is implied by the resource name rather than explicitly routed.

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=false, idempotent=false, destructive=false, so the safety profile is largely covered. The description adds genuine value by disclosing the return contents (per-person reactions, objections, 'what would help', counts) and that a keyed run appends a report link, plus the notable behavior that variant_b silently upgrades the call to compare_copy. No cost/rate-limit info, but that is a minor gap against the 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?

Four sentences, front-loaded with the core action before the routing hint, and every sentence carries information. Slightly recursive sentence about variant_b ('still accepts variant_b and then runs that same comparison') repeats the routing point already made.

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 an output schema present, the description needn't describe return shapes, yet it helpfully summarizes them. For a 5-param tool with annotations and an output schema, the routing, behavior, and output summary make it largely complete; only the non-variant parameters and sibling differentiation are under-addressed.

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 baseline is 3. The description adds meaning only for variant_a and variant_b (and the variant_b behavior is already stated in the schema). The count, audience, and timeout_seconds parameters are left entirely to the schema, and audience auto-selection is only documented in the schema, not amplified here.

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?

States a specific verb and resource ('Show one piece of copy ... to simulated people') and enumerates exactly which copy types qualify (headline, tagline, value proposition, subject line, CTA). It also differentiates itself from the most confusable sibling by naming mimiq.compare_copy outright, so an agent can route without opening either schema.

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?

Gives clear routing advice: 'To choose between two versions, use mimiq.compare_copy,' and clarifies the fallback that passing variant_b still triggers that comparison. It lacks guidance on the other near siblings (e.g. test_text vs. test_copy, or when an audience test is preferable to ask_audience), so context is clear but not exhaustively discriminating.

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=false, idempotent=false, openWorld=true. The description adds real context beyond that: the audience-fit gate ('more than 1 in 5 not the requested audience'), the credit cost per person, the slower-than-test_page trait, and the audience_review continuation protocol. It doesn't mention timeout behavior, but the schema covers timeout_seconds.

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 opens with a dense, hard-to-parse sentence about audience gating ('Starts immediately unless the recruited crowd is wrong...') before it ever says what the tool does. The core purpose is buried in sentence two. Several clauses about review_id/confirm_audience/audience_correction duplicate the schema descriptions and could be trimmed.

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 complex, 10-parameter, open-world tool with an output schema, the description covers cost, speed relative to peers, audience-review branch handling, and what is returned (steps, where they stopped and why). Missing explicit timeout guidance and a tie-breaker against other flow-related siblings, but substantially 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 90%, so the parameters are already well documented in the schema. The description reinforces the purpose of goal/max_steps implicitly but mostly restates the audience-review flow rather than adding syntax or format meaning for url, count, or timeout. 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 uses a specific verb and resource: 'Send simulated people through a multi-step flow ... in a real browser, clicking and typing, to find the step where they get stuck or give up.' It clearly distinguishes the tool from test_page ('Slower than test_page'), though it is oddly front-loaded with audience-gating mechanics before the core purpose.

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 names test_page as a faster alternative and the cost trade-off ('each person uses 5 credits'), which implies when to prefer the page tool. But it never explicitly states when to use this flow tool versus siblings like compare_urls or test_component, and the usage instructions are tangled in the audience-review branch logic.

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 (readOnlyHint=false, openWorldHint=true, idempotentHint=false), it discloses non-obvious behavior: the run starts immediately unless the recruited crowd is >1 in 5 wrong, and only those runs return audience_review requiring an explicit confirm/correct step. Cost behavior (each simulated person consumes a credit) also appears. It stops short of stating runtime, auth requirements, or rate limits.

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 content is dense and mostly earns its place, but it is poorly ordered: the first sentence describes a rare exception branch before the tool's actual purpose, and the localhost/tunnel note is restated between description and schema. Front-loading is weak for a multi-sentence definition.

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 9-parameter, branching, open-world tool this covers the essentials: public-URL requirement, the tunnel workaround for localhost, credit cost, the audience-review path, and the returned actions/thoughts/objections counts. An output schema exists, so return-value detail is not required, and the description even points to the full report link when a key 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 coverage is 89%, so the schema already documents url, goal, count, audience, timeout and the review fields; baseline would be 3. The description adds real meaning by tying review_id, confirm_audience, audience_fit_acknowledged and audience_correction together as a conditional workflow (warn → confirm/continue → or correct and re-recruit), which the schema does not convey on its own.

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+resource: "Show a web page to simulated people and hear how they react: who would stay, who would leave, and why," plus the concrete artifact it acts on (landing, pricing, signup pages). That clearly distinguishes it from a generic test tool, though it never names the sibling it is not (e.g. test_component/test_text/test_flow). The purpose is also undercut by being buried after the opening exception-branch sentence.

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?

"Use it after you build or change a landing, pricing or signup page, then fix what they raised and test again" gives explicit when-to-use timing and an iteration loop. It also spells out the audience-review continuation path (review_id, confirm_audience, audience_correction). It lacks any when-not-to-use or sibling 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 declare the safety profile (readOnly=false, destructive=false, idempotent=false), so the bar is lower. The description adds real value: it discloses the shape of the return ('each person's reaction, objections and what would help') and the conditional report-link behavior when a key is supplied, which annotations do not cover.

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, each front-loaded with a distinct fact: what it does, how to steer it with a goal, what comes back, and the report-link behavior. No filler or repetition.

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?

An output schema exists, so it need not describe return values in depth, and the description still summarizes them usefully. The one real gap is the unresolved overlap with test_copy and the other test_* siblings, which the description never addresses.

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% and already documents goal, text, count and audience, so the baseline is 3. The description only restates the meaning of goal and says nothing about count, audience, or the undocumented timeout_seconds, adding little 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?

States a specific verb and resource ('Show any text ... to simulated people and hear how they react') and enumerates the kinds of text it accepts, which clarifies scope. It does not, however, distinguish itself from the very similar sibling mimiq.test_copy, so an agent must infer the boundary.

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 sentence 'Pass goal to say what the text should achieve' gives practical guidance on how to use the optional goal field, and the audience field's omissibility is documented in the schema. But there is no when-to-use/when-not guidance and no routing to alternatives like test_copy or compare_copy, leaving selection ambiguous.

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