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Форма подбора отправлена

compose_landing_submit

Собрать лендинг по ответам из формы.

Это НЕ инструмент для модели: его вызывает сама форма через мост хоста.
Модель для того же зовёт `compose_landing`.

Сборка идёт той же функцией, что и после квиза, — результат не должен
зависеть от того, каким клиентом пользуется человек.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNoЧто человек должен сделать.signup
toneNoТон.friendly
topicYesТема страницы одной строкой.
paletteNoПалитра.no_preference
audienceNoДля кого страница.
integrationNoКуда приходят заявки.skip
project_nameNoСлаг проекта.
design_systemYesВыбранное оформление.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
briefNo
filesNo
statusYes
structureNo
integrationNo
next_actionNo
design_rulesNo
project_nameNo
design_systemNo
category_guideNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.9/5.0
Behavior3/5

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

The annotation provides only openWorldHint=true, which the description doesn't address. The description does add one useful behavioral detail: that assembly uses the same function as the post-quiz path, meaning results are client-independent. However, it doesn't disclose whether this mutates state, requires auth, or what side-effects (if any) occur — though the open-world nature of the tool and its bridge-triggered design give reasonable transparency.

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

Conciseness4/5

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

The description is brief and front-loaded with the core statement, then adds essential usage disambiguation. The line about 'not for the model' is critical context that earns its place. Slightly more verbose than necessary (line breaks could be tightened), but every sentence contributes value.

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

Completeness4/5

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

Given the output schema exists (so return format needs no explanation), the annotation is minimal, and this is a straightforward landing-composition tool with a critical usage caveat (not model-callable), the description sufficiently covers the essential aspects. It could mention what makes this different from `compose_landing` beyond callers, but given the sibling relationship is clearly flagged and the tool is internally invoked, this is adequate.

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

Parameters3/5

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 8 parameters (topic, design_system required; goal, tone, palette, audience, integration, project_name with defaults). The description adds no parameter-specific detail beyond 'by answers from the form,' which is implicit given the tool name. Per the baseline rule, with full schema coverage, a 3 is appropriate.

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

Purpose4/5

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

The description clearly states what the tool does ('Собрать лендинг по ответам из формы' — assemble a landing from form answers) and explicitly distinguishes it from its primary sibling `compose_landing`. While the verb+resource is clear, it doesn't fully describe the broader scope of what the landing output entails, but the core purpose is well-communicated.

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

Usage Guidelines5/5

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

The description explicitly states WHO should use this tool ('Это НЕ инструмент для модели: его вызывает сама форма через мост хоста') and precisely names the alternative for the model (`compose_landing`). It also notes that the build uses the same function as after a quiz, ensuring consistent behavior. This is explicit when/when-not guidance with a named alternative.

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

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TDQS

A3.8/5.0
Disambiguation5/5

Each tool targets a distinct resource and action. The deploy lifecycle is cleanly separated into cancel, retry, rollback, diagnose, logs, list, and status, while the site_* tools each answer a different question. Even the two compose_landing tools are explicitly differentiated for model vs. internal use.

Naming Consistency3/5

Tool names mix verb-first patterns (check_domain, list_deploys, connect_analytics) with noun-first patterns (site_issues, deploy_logs, env_vars), and some are bare verbs (rollback, whoami). The naming is descriptive and readable, but not consistent enough to predict the style for a new tool.

Tool Count2/5

27 tools exceeds the typical well-scoped range and pushes into 'too many' territory. While the server covers a broad platform scope, many tools are highly specialized (check_copy, site_screenshot), and an agent may be overwhelmed by the sheer number of choices. Consolidation could reduce the load.

Completeness4/5

The core lifecycle is solid: compose, publish, monitor, diagnose, and rollback, with supporting tools for domains, analytics, performance, and content inspection. However, there are no delete/remove operations for projects, domains, or integrations, and integration management is limited to adding. These are minor gaps that agents can work around, but they are notable for a full platform.