Skip to main content
Glama
deenesjakoruzh
avansaber

tailtest-cline

by avansaber
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

tailtest-cline

Du baust. Wir decken ab. Über 8+ Editoren gleichzeitig.

tailtest-cline ist ein Cline-Plugin, das automatisch Tests generiert und ausführt, jedes Mal wenn der Agent eine Quelldatei bearbeitet. Dieselbe R1-R15-Regelebene wie bei den anderen tailtest-Varianten. Adversarial-Testmodus (V13) vom ersten Tag an dabei.

Cline-Reichweite: VS Code, Cursor, JetBrains IDEs, Antigravity, Zed, Neovim, VSCodium, Windsurf sowie die Cline CLI. Ein Plugin, acht plus Editoren.

Vollständige Dokumentation unter tailtest.com/docs/cline

Was ist anders als bei den anderen Varianten

Cline hat keine Hooks (Claude Codes PostToolUse, Cursors afterFileEdit, Codexs Stop). Stattdessen verwendet tailtest-cline drei Cline-native Primitive:

  • .clinerules/ enthält die Regelebene (R1-R14 + R15 Adversarial-Modus)

  • tailtest-mcp Server (dieses Repo) stellt strukturierte Tools für den Agenten bereit: tailtest_setup, tailtest_scenario_plan, tailtest_classify_failures, tailtest_pick_template, tailtest_ping

  • Memory Bank (tailtestContext.md) speichert das projektbezogene Profil über Cline-Sitzungen hinweg

Die deterministische Richtlinie (Tiefenstufen, R15 Adversarial-Anzahlen, Framework-Vorlagen, Dateipfade) liegt im MCP-Server-Code und nicht im Regeltext. Der Agent ruft die Tools auf, wenn er Gerüste benötigt, anstatt sich über lange Sitzungen hinweg auf das Abrufen von Regeln zu verlassen.

Related MCP server: OpenTester

Installation

Drei Installationswege:

1. Cline MCP Marketplace (empfohlen; ein Klick)

Öffne das Extensions-Panel von Cline in deiner IDE. Suche nach tailtest. Klicke auf Installieren. Cline klont, installiert Abhängigkeiten und registriert den Server automatisch in cline_mcp_settings.json.

2. Direkte GitHub-Installation

Füge https://github.com/avansaber/tailtest-cline in den MCP-Server-Installationsdialog von Cline ein. Cline führt denselben Klon-, Installations- und Registrierungsprozess durch.

3. Manuelle Bearbeitung von 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"
      ]
    }
  }
}

Erster Durchlauf

Gib nach der Installation im Cline-Chat ein: "set up tailtest in this project".

Cline ruft tailtest_setup auf. Das Tool erkennt Sprache / Framework / Runner, schreibt .clinerules/, schreibt .clinerules/workflows/, initialisiert memory-bank/tailtestContext.md und initialisiert .tailtest/config.json + .tailtest/session.json. Idempotent: Bestehende Dateien bleiben erhalten.

Wichtig: Cline lädt .clinerules nicht automatisch mitten im Gespräch neu. Starte eine neue Konversation (oder lade das Fenster neu), um sie zu aktivieren.

Zwei Betriebsmodi

Manueller Modus (Standard)

Du rufst den Testzyklus explizit auf:

  • /tailtest-test <file> -- führt den Zyklus für eine bestimmte Datei aus

  • /tailtest hunt <file> -- einmaliger Adversarial-Durchlauf für eine bestimmte Datei

  • Oder natürliche Sprache ("test the file I just edited")

Jeder Schritt (Dateischreiben, Terminalbefehl, MCP-Aufruf) erfordert eine Genehmigung. Sicherere Standardeinstellung für Erstbenutzer.

Auto-Modus (Opt-in)

Aktiviere die automatische Genehmigung von Cline für: Dateien bearbeiten (Arbeitsbereich), sichere Befehle ausführen, MCP-Server verwenden. Führe dann /tailtest-mode auto aus und lade neu. Nach jeder Bearbeitung startet tailtest automatisch den Testzyklus.

Plan / Act-Modus (Cline-native UX-Bonus)

Clines Plan / Act-Umschalter ist der SCENARIO PLAN-Konvention von tailtest zugeordnet: Der Plan-Modus erstellt den SCENARIO PLAN (schreibgeschützt); überprüfen und ändern; der Act-Modus schreibt die Testdatei, führt sie aus und wendet die R12-Klassifizierung an. Empfohlen für kritische Dateien.

Konfiguration

.tailtest/config.json:

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

Tiefenoptionen:

  • simple -- 2-3 Happy-Path-Szenarien

  • standard -- 5-8 Szenarien einschließlich 2+ Adversarial-Probes (Standard)

  • thorough -- 10-15 Szenarien einschließlich 4+ Adversarial-Probes

  • adversarial -- 8-12 Szenarien mit Fokus auf Fehleranfälligkeit

Siehe tailtest.com/docs/config für alle Optionen und tailtest.com/docs/adversarial für Details zum Adversarial-Modus.

Andere tailtest-Varianten

Dieselbe R1-R15-Regelebene, derselbe Adversarial-Testmodus, andere Host-Integration. Dieses Repo ist die Cline-Variante.

Siehe tailtest.com/demo/cline für eine Live-Demo dieser Variante oder tailtest.com/comparison für eine Funktionsmatrix aller vier.

Lizenz

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

Resources

Related MCP Connectors

Related MCP Servers

  • OpenTester

    kznr02
    A
    license
    Not graded
    quality
    C
    maintenance
    OpenTester is a testing execution engine designed for AI coding tools (Claude Code, Cursor, OpenCode, etc.). It provides a unified DSL format and MCP interface, enabling Agents to generate, execute, and manage test cases, achieving an automated "code-test-fix" workflow.
    28
    MIT

  • rush

    jamesdsizemore
    A
    license
    C
    quality
    A
    maintenance
    Agentic code-quality CLI and stdio MCP server that runs real Python and JS/TS quality engines (Ruff, pytest, ESLint, Prettier, Vitest, etc.) for linting, testing, and reviewing code.
    79
    MIT