F
licenseNot graded
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maintenanceEnables AI assistants to analyze Java and Web projects, automatically generate unit tests (JUnit) and end-to-end tests (Selenium), and execute them directly through Maven or NPM commands.
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tailtest-cline
by avansaber
tailtest-cline
あなたがビルドし、私たちがカバーする。8つ以上のエディタで同時に。
tailtest-clineは、Clineがソースファイルを編集するたびに自動的にテストを生成・実行するClineプラグインです。他のtailtestバリアントと同じR1-R15ルールレイヤーを備えています。敵対的テストモード（V13）は初日から実装されています。
Clineの対応範囲: VS Code、Cursor、JetBrains IDE、Antigravity、Zed、Neovim、VSCodium、Windsurf、およびCline CLI。1つのプラグインで8つ以上のエディタに対応します。
完全なドキュメントはこちら: tailtest.com/docs/cline
他のバリアントとの違い
Clineにはフック（Claude CodeのPostToolUse、CursorのafterFileEdit、CodexのStopなど）がありません。その代わり、tailtest-clineは3つのClineネイティブなプリミティブを使用します。
.clinerules/: ルールレイヤー（R1-R14 + R15敵対的モード）を保持しますtailtest-mcpサーバー（本リポジトリ）: エージェント向けに構造化されたツールを提供します:tailtest_setup、tailtest_scenario_plan、tailtest_classify_failures、tailtest_pick_template、tailtest_pingMemory Bank (
tailtestContext.md): Clineセッション全体でプロジェクトごとのプロファイルを保持します
決定論的なポリシー（深度階層、R15敵対的カウント、フレームワークテンプレート、ファイルパス）は、ルールテキストではなくMCPサーバーのコード内に存在します。エージェントは、長いセッションを通じてルールを記憶に頼るのではなく、足場が必要なときにツールを呼び出します。
Related MCP server: OpenTester
インストール
3つのインストール方法があります：
1. Cline MCPマーケットプレイス（推奨：ワンクリック）
IDE内のClineの拡張機能パネルを開きます。「tailtest」を検索し、インストールをクリックします。Clineが自動的にクローン、依存関係のインストール、cline_mcp_settings.jsonへのサーバー登録を行います。
2. GitHubからの直接インストール
ClineのMCPサーバーインストールダイアログに https://github.com/avansaber/tailtest-cline を貼り付けます。Clineが同様のクローン・インストール・登録フローを実行します。
3. cline_mcp_settings.jsonの手動編集
{
"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"
]
}
}
}初回実行
インストール後、Clineのチャットで "set up tailtest in this project" と入力してください。
Clineが tailtest_setup を呼び出します。このツールは言語/フレームワーク/ランナーを検出し、.clinerules/、.clinerules/workflows/ を書き込み、memory-bank/tailtestContext.md をシードし、.tailtest/config.json と .tailtest/session.json を初期化します。冪等性があるため、既存のファイルは保持されます。
重要: Clineは会話の途中で .clinerules を自動再読み込みしません。有効にするには、新しい会話を開始するか、ウィンドウをリロードしてください。
2つの動作モード
手動モード（デフォルト）
テストサイクルを明示的に呼び出します：
/tailtest-test <file>-- 特定のファイルに対してサイクルを実行/tailtest hunt <file>-- 特定のファイルに対して1回限りの敵対的パスを実行または自然言語（「編集したばかりのファイルをテストして」）
各ステップ（ファイル書き込み、ターミナルコマンド、MCP呼び出し）で承認が求められます。初回ユーザーにとってより安全なデフォルト設定です。
自動モード（オプトイン）
Clineの自動承認設定で「ファイルの編集（ワークスペース）」「安全なコマンドの実行」「MCPサーバーの使用」を有効にします。その後、/tailtest-mode auto を実行してリロードします。編集のたびに、tailtestが自動的にテストサイクルを開始します。
Plan / Actモード（ClineネイティブUXの利点）
ClineのPlan / Act切り替えは、tailtestのSCENARIO PLAN規約にマッピングされます。PlanモードはSCENARIO PLAN（読み取り専用）を作成し、確認と修正を行います。Actモードはテストファイルを書き込み、実行し、R12分類を適用します。重要なファイルに対して推奨されます。
設定
.tailtest/config.json:
{
"depth": "standard",
"mode": "manual"
}深度オプション:
simple-- 2〜3のハッピーパスシナリオstandard-- 2つ以上の敵対的プローブを含む5〜8のシナリオ（デフォルト）thorough-- 4つ以上の敵対的プローブを含む10〜15のシナリオadversarial-- 破壊を目的とした8〜12のシナリオ
すべてのオプションについては tailtest.com/docs/config を、敵対的モードの詳細については tailtest.com/docs/adversarial を参照してください。
その他のtailtestバリアント
同じR1-R15ルールレイヤー、同じ敵対的テストモードを備え、ホスト統合が異なります。本リポジトリはClineバリアントです。
tailtest -- Claude Codeプラグイン（フック駆動）
tailtest-cursor -- Cursorプラグイン（フック駆動）
tailtest-codex -- Codex CLIプラグイン（フック駆動）
tailtest-cline -- Clineプラグイン（MCP駆動; 本リポジトリ）
このバリアントのライブウォークスルーについては tailtest.com/demo/cline を、4つすべての機能マトリックスについては tailtest.com/comparison を参照してください。
ライセンス
MIT。
Available Tools
5 toolstailtest_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
| Name | Required | Description | Default |
|---|---|---|---|
| runner_output | Yes | Stdout (and optionally stderr) from the test runner. | |
| runner | No | Runner 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
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Relative or absolute path to the source file under test. | |
| project_root | No | Project 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
| Name | Required | Description | Default |
|---|---|---|---|
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
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Relative or absolute path to the source file under test. | |
| project_root | No | Project 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
| Name | Required | Description | Default |
|---|---|---|---|
| project_root | No | Project directory. Defaults to the current working directory. | |
| mode | No | manual (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.
5 tool updates
v1.0.1- First observed
tailtest_classify_failures - First observed
tailtest_pick_template - First observed
tailtest_ping - First observed
tailtest_scenario_plan - First observed
tailtest_setup
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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