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tailtest-cline

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tailtest-cline

Вы создаете. Мы покрываем. Сразу в 8+ редакторах.

tailtest-cline — это плагин для Cline, который автоматически генерирует и запускает тесты каждый раз, когда агент редактирует исходный файл. Использует тот же уровень правил R1-R15, что и другие варианты tailtest. Режим состязательного тестирования (V13) доступен с первого дня.

Охват Cline: VS Code, Cursor, IDE от JetBrains, Antigravity, Zed, Neovim, VSCodium, Windsurf, а также Cline CLI. Один плагин, более восьми редакторов.

Полная документация на tailtest.com/docs/cline

Чем он отличается от других вариантов

У Cline нет хуков (как PostToolUse в Claude Code, afterFileEdit в Cursor или Stop в Codex). Вместо этого tailtest-cline использует три нативных примитива Cline:

  • .clinerules/ содержит уровень правил (R1-R14 + состязательный режим R15)

  • Сервер tailtest-mcp (этот репозиторий) предоставляет агенту структурированные инструменты: tailtest_setup, tailtest_scenario_plan, tailtest_classify_failures, tailtest_pick_template, tailtest_ping

  • Банк памяти (tailtestContext.md) хранит профиль проекта между сессиями Cline

Детерминированная политика (уровни глубины, количество состязательных проверок R15, шаблоны фреймворков, пути к файлам) находится в коде MCP-сервера, а не в тексте правил. Агент вызывает инструменты, когда ему нужны шаблоны, вместо того чтобы полагаться на запоминание правил в течение длительных сессий.

Related MCP server: OpenTester

Установка

Три способа установки:

1. Маркетплейс Cline MCP (рекомендуется; в один клик)

Откройте панель расширений Cline в вашей IDE. Введите tailtest. Нажмите «Установить». Cline автоматически клонирует репозиторий, установит зависимости и зарегистрирует сервер в cline_mcp_settings.json.

2. Прямая установка через GitHub

Вставьте https://github.com/avansaber/tailtest-cline в диалоговое окно установки MCP-сервера в Cline. Cline выполнит тот же процесс клонирования, установки и регистрации.

3. Ручное редактирование cline_mcp_settings.json

{
  "mcpServers": {
    "tailtest": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/tailtest-cline/mcp_server",
      "alwaysAllow": [
        "tailtest_ping",
        "tailtest_scenario_plan",
        "tailtest_classify_failures",
        "tailtest_pick_template"
      ]
    }
  }
}

Первый запуск

После установки введите в чате Cline: "set up tailtest in this project".

Cline вызовет tailtest_setup. Инструмент определит язык / фреймворк / средство запуска, создаст .clinerules/, .clinerules/workflows/, инициализирует memory-bank/tailtestContext.md, а также .tailtest/config.json + .tailtest/session.json. Идемпотентность: существующие файлы сохраняются.

Важно: Cline не перезагружает .clinerules автоматически в середине диалога. Начните новый диалог (или перезагрузите окно), чтобы активировать изменения.

Два режима работы

Ручной режим (по умолчанию)

Вы запускаете цикл тестирования явно:

  • /tailtest-test <file> — запустить цикл для конкретного файла

  • /tailtest hunt <file> — однократный состязательный проход для конкретного файла

  • Или на естественном языке ("протестируй файл, который я только что отредактировал")

Каждый шаг (запись файла, команда терминала, вызов MCP) требует подтверждения. Более безопасный вариант по умолчанию для новых пользователей.

Автоматический режим (по выбору)

Разрешите Cline автоматическое подтверждение для: редактирования файлов (рабочая область), выполнения безопасных команд, использования MCP-серверов. Затем выполните /tailtest-mode auto и перезагрузитесь. После каждого редактирования tailtest будет автоматически запускать цикл тестирования.

Режим планирования / действия (бонус UX, нативный для Cline)

Переключатель Plan / Act в Cline соответствует соглашению SCENARIO PLAN в tailtest: режим Plan создает ПЛАН СЦЕНАРИЯ (только для чтения); вы проверяете и вносите правки; режим Act записывает файл теста, запускает его и применяет классификацию R12. Рекомендуется для критически важных файлов.

Конфигурация

.tailtest/config.json:

{
  "depth": "standard",
  "mode": "manual"
}

Опции глубины:

  • simple — 2-3 сценария «счастливого пути»

  • standard — 5-8 сценариев, включая 2+ состязательных проверки (по умолчанию)

  • thorough — 10-15 сценариев, включая 4+ состязательных проверки

  • adversarial — 8-12 сценариев с уклоном на поиск ошибок

См. tailtest.com/docs/config для получения всех опций и tailtest.com/docs/adversarial для подробностей о состязательном режиме.

Другие варианты tailtest

Тот же уровень правил R1-R15, тот же состязательный режим тестирования, другая интеграция с хостом. Этот репозиторий — вариант для Cline.

  • tailtest — плагин для Claude Code (на основе хуков)

  • tailtest-cursor — плагин для Cursor (на основе хуков)

  • tailtest-codex — плагин для Codex CLI (на основе хуков)

  • tailtest-cline — плагин для Cline (на основе MCP; этот репозиторий)

См. tailtest.com/demo/cline для живого прохождения этого варианта или tailtest.com/comparison для матрицы функций всех четырех вариантов.

Лицензия

MIT.

Available Tools

5 tools
tailtest_classify_failuresA

Parse runner output (pytest, jest, etc.) into structured failure records and apply heuristic R12 classification. Returns failures with type (real_bug / environment / test_bug / unknown), reason, test name, file, line, error type, message, and a summary count per R12 category. The agent verifies or overrides the heuristic when context warrants.

ParametersJSON Schema
NameRequiredDescriptionDefault
runner_outputYesStdout (and optionally stderr) from the test runner.
runnerNoRunner name. Defaults to pytest.

TDQS

A4.3/5.0
Behavior4/5

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

No annotations exist, so the description must fully disclose behavior. It details the return fields (type, reason, test name, etc.) and mentions the agent can verify/override. It does not mention side effects, which seems acceptable for a read-only classification. Could note that no state is modified.

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

Conciseness5/5

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

The description is three sentences, each adding essential information: what it does, what it returns, and how the agent interacts. No unnecessary words, front-loaded with the main action. Highly concise and well-structured.

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

Completeness5/5

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

Without an output schema, the description fully covers the return value details (fields and categories). It explains the heuristic and agent override capability. No gaps are apparent for the tool's complexity. Enough for an agent to correctly invoke and interpret results.

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 coverage is 100% with clear descriptions for both parameters. The description confirms the 'runner' parameter defaults to pytest, which is already in the schema. It does not add significant new meaning beyond the schema, but it does tie parameters to the overall classification context, earning a baseline score.

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

Purpose5/5

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

The description clearly states the tool's purpose: parsing runner output and applying heuristic R12 classification. It specifies the exact resources (runner output, runner) and outputs (failure types, categories). The tool distinguishes well from siblings (none involve classification).

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

Usage Guidelines4/5

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

The description implies usage context: use when you have test runner output to classify. It does not explicitly exclude scenarios or name alternatives, but sibling tools are clearly different (template, ping, plan, setup), so confusion is low. A brief 'when not to use' would improve it.

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

tailtest_pick_templateA

Return the full framework R2 template for a given source file: language baseline scenarios, framework baseline scenarios, framework-specific test pattern (e.g., NestJS Test.createTestingModule, Spring @WebMvcTest, Flask test_client), and test file path pattern. Returns just language baseline when no framework matches.

ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathYesRelative or absolute path to the source file under test.
project_rootNoProject root directory. Defaults to the current working directory.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries full responsibility. It discloses the fallback behavior ('Returns just language baseline when no framework matches') and describes the output components. However, it does not mention error conditions or side effects, but given the read-only nature, this is acceptable.

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

Conciseness5/5

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

The description is two sentences, starts with the verb 'Return', and contains zero redundant information. Every phrase contributes to understanding the tool's function.

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 two parameters and no output schema, the description adequately explains the return value and the fallback. It lacks mention of error cases or supported languages, but is otherwise complete for a straightforward read tool.

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

Parameters4/5

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

Schema description coverage is 100%, but the parameter descriptions are minimal. The tool description adds value by explaining that the output is determined by the file_path and project_root, and contextually connects parameters to the template resolution. This goes beyond the schema.

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

Purpose5/5

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

The description clearly states it returns a framework R2 template for a given source file, listing specific components (language baseline scenarios, framework baseline scenarios, etc.). This is a specific verb+resource and differentiates from sibling tools like tailtest_classify_failures.

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

Usage Guidelines3/5

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

The description implies usage for picking a template for a source file but does not explicitly state when to use this tool versus alternatives. It lacks guidance on when not to use or naming sibling tools for comparison.

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

tailtest_pingA

Health check. Returns server version and confirms the MCP server is reachable.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.3/5.0
Behavior4/5

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

No annotations provided, but the description honestly discloses the read-only nature and return of server version. No hidden behaviors.

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

Conciseness5/5

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

Single concise sentence with no redundancy, efficiently conveying the tool's purpose.

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

Completeness5/5

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

For a simple ping tool with no parameters or output schema, the description fully covers its behavior and return value.

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

Parameters4/5

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

No parameters exist; schema coverage is 100%. Baseline for 0 params is 4.

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

Purpose5/5

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

The description clearly states it is a health check that returns server version and confirms reachability, distinguishing it from sibling tools like classification or setup.

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

Usage Guidelines3/5

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

Usage is implied as a health check, but no explicit guidance on when to use vs alternatives or when not to use.

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

tailtest_scenario_planA

Return structured scaffolding the agent uses to write its SCENARIO PLAN: language, framework, depth, R15 adversarial count requirement, language and framework baseline scenarios, test file path, and prose instructions. The agent uses this scaffolding to compose the actual SCENARIO PLAN scenario lines.

ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathYesRelative or absolute path to the source file under test.
project_rootNoProject root directory. Defaults to the current working directory.

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries full behavioral burden. It details the returned content but does not disclose side effects, permissions, or whether the operation is read-only. It is adequate but lacks depth on behavioral traits.

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

Conciseness5/5

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

The description is two sentences, front-loaded with purpose, and efficiently lists all components without redundancy or unnecessary words.

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?

The description lists the fields returned but does not specify the output format (e.g., JSON structure) or address error cases. Given no output schema, the agent may need to infer the structure, but the listing is helpful.

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 descriptions already cover file_path and project_root with 100% coverage. The description does not add additional meaning beyond what the schema provides, so it is at baseline.

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

Purpose5/5

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

The description clearly states the tool returns 'structured scaffolding' for writing a scenario plan, listing specific components like language, framework, depth, R15 adversarial count, baseline scenarios, test path, and instructions. It distinguishes from sibling tools by focusing on scaffolding generation.

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

Usage Guidelines4/5

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

The description provides context that the scaffolding is used before composing the actual scenario plan, but it does not explicitly exclude other uses or compare with alternatives like other tailtest tools. The usage is implied.

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

tailtest_setupA

Bootstrap entry point for tailtest in a Cline project. Detects language / framework / runner, writes the .clinerules/ rule pack, writes .clinerules/workflows/ slash workflows, seeds Memory Bank with tailtestContext.md (a 7th file alongside the 6 core ones; existing files are not overwritten), and initialises .tailtest/config.json + session.json. Returns a structured report including the user-facing 'reload required' warning (Cline does not auto-reload .clinerules mid-conversation).

ParametersJSON Schema
NameRequiredDescriptionDefault
project_rootNoProject directory. Defaults to the current working directory.
modeNomanual (default): user invokes /tailtest-test after edits. auto: tailtest fires after every edit (requires Cline auto-approve).

TDQS

A4.6/5.0
Behavior5/5

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

No annotations exist, so the description fully carries the burden. It discloses all major effects: file creation (rules, workflows, memory bank, configs), non-overwriting of existing files, and the reload warning. No contradictions.

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

Conciseness5/5

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

The description is concise (3-4 sentences) yet packed with information. It front-loads the main purpose and each sentence serves a clear role without redundancy.

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 no output schema, the description partially explains the return value (structured report with reload warning) but omits details about report fields or structure. For a tool that modifies multiple files, this is a minor gap.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds valuable context beyond the schema by explaining the implications of the mode parameter (manual vs. auto) and what triggers each. This adds semantic richness.

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

Purpose5/5

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

The description clearly states the tool's purpose as the bootstrap entry point for tailtest, listing specific actions like detecting language, writing rule packs, and initializing configs. It is distinctly different from sibling tools (classify, pick, ping, scenario plan).

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

Usage Guidelines4/5

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

The description implies usage as the first step in tailtest setup, but it does not explicitly state when to use or avoid it. It provides clear context but lacks explicit exclusions or alternative tool guidance.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 5 tool updatesv1.0.1
    • First observedtailtest_classify_failures
    • First observedtailtest_pick_template
    • First observedtailtest_ping
    • First observedtailtest_scenario_plan
    • First observedtailtest_setup
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A4.4/5.0

Scored across 5 tools

Disambiguation5/5

Each tool serves a distinct purpose: setup, health check, template selection, scenario planning, and failure classification. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tools follow a consistent snake_case pattern with the prefix 'tailtest_' and a verb_noun structure (e.g., tailtest_classify_failures, tailtest_pick_template). No deviations.

Tool Count5/5

With 5 tools, the server is tightly scoped to test analysis and scaffolding. Each tool earns its place without being too few or too many for the domain.

Completeness4/5

The tools cover setup, health, template selection, scenario planning, and failure classification. While not a full test framework, it appears complete for its intended planning/analysis purpose. Minor gap: no tool for running tests or generating code.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

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