Skip to main content
Glama
deenesjakoruzh
soynog

dev-loop-mcp

by soynog
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

dev-loop-mcp

MCP-сервер (Model Context Protocol), который запускает цикл разработки TDD под управлением ИИ. Он обобщает конечный автомат цикла разработки для работы с любым проектом через простой файл конфигурации.

Что он делает

Доступны два типа циклов — оба используют один и тот же конвейер TDD; они различаются только тем, как создаются задачи:

flowchart LR
    subgraph start_loop["start_loop (feature)"]
        direction LR
        A("description<br/>or tasks") --> B["DECOMPOSE<br/>AI breaks into tasks"]
        B --> C[/"tasks"/]
    end

    subgraph start_debug_loop["start_debug_loop (bug)"]
        direction LR
        D("symptom<br/>+ context files") --> E["DIAGNOSE<br/>AI ranks hypotheses"]
        E --> F[/"tasks"/]
    end

    C --> Pipeline["TDD pipeline"]
    F --> Pipeline

    subgraph Pipeline["Shared TDD pipeline"]
        direction LR
        I[INIT] --> T[TDD_LOOP<br/>per task]
        T --> Bu[BUILD]
        Bu --> De[DEPLOY<br/>optional]
        De --> It[INTEG_TEST<br/>optional]
        It -->|pass| Qr[QUALITY_REVIEW]
        It -->|fail| If[INTEG_FIX<br/>up to 5×]
        If --> Qr
        Qr --> Ct[CLEAN_TREE<br/>CHECK]
        Ct --> Pr[PUSH_AND_PR]
        Pr --> Done(["✓ DONE<br/>PR opened"])
    end

Полный конечный автомат

flowchart TD
    start_loop --> INIT
    start_debug_loop -->|"DIAGNOSE:<br/>ranked hypotheses → tasks"| INIT

    INIT -->|"pre-loaded tasks"| TDD_LOOP
    INIT -->|"description only"| DECOMPOSE
    DECOMPOSE -->|"AI → Task[]"| TDD_LOOP

    TDD_LOOP -->|"task done, more remain"| TDD_LOOP
    TDD_LOOP -->|"all tasks done"| BUILD
    TDD_LOOP -->|"task failed"| FAILED

    BUILD -->|pass| DEPLOY
    BUILD -->|fail| FAILED

    DEPLOY -->|"pass / skipped"| INTEG_TEST
    DEPLOY -->|fail| FAILED

    INTEG_TEST -->|"pass / skipped"| QUALITY_REVIEW
    INTEG_TEST -->|fail| INTEG_FIX

    INTEG_FIX -->|fixed| QUALITY_REVIEW
    INTEG_FIX -->|"still failing<br/>(retry, max 5)"| INTEG_FIX
    INTEG_FIX -->|"5 attempts exhausted"| FAILED

    QUALITY_REVIEW --> CLEAN_TREE_CHECK
    CLEAN_TREE_CHECK --> PUSH_AND_PR
    PUSH_AND_PR --> DONE

    DONE(["✓ DONE"])
    FAILED(["✗ FAILED"])

    style DONE fill:#22c55e,color:#fff
    style FAILED fill:#ef4444,color:#fff
    style start_loop fill:#6366f1,color:#fff
    style start_debug_loop fill:#f59e0b,color:#fff

Цикл TDD для каждой задачи

Каждая задача в TDD_LOOP проходит этот внутренний цикл (до 5 итераций кодирования):

flowchart LR
    A["Write scenarios<br/>scenarios/scenarios-*.md"] --> B["Write failing tests<br/>*.test.ts"]
    B --> C{"Tests<br/>fail?"}
    C -->|"no — tester error"| Z["✗ task failed"]
    C -->|yes| D["Implement"]
    D --> E{"Tests<br/>pass?"}
    E -->|yes| F["✓ commit & next task"]
    E -->|"no (retry)"| D

Справочник фаз:

  • INIT: Создает ветку git

  • DECOMPOSE: ИИ преобразует описание в Task[]

  • DIAGNOSE: (только для цикла отладки) ИИ считывает симптомы + файлы контекста и создает ранжированные гипотезы о первопричинах в виде Task[]

  • TDD_LOOP: Для каждой задачи: сценарии → провальные тесты → реализация (до 5 итераций кодирования на задачу)

  • BUILD: Запускает buildCommand

  • DEPLOY: Запускает deployCommand — пропускается, если не настроено

  • INTEG_TEST: Запускает integTestCommand — пропускается, если не настроено

  • INTEG_FIX: ИИ диагностирует и исправляет ошибки интеграционного тестирования (до 5 попыток)

  • QUALITY_REVIEW: ИИ проверяет полный diff ветки и применяет исправления качества

  • CLEAN_TREE_CHECK: Автоматически фиксирует (commit) любые незафиксированные файлы

  • PUSH_AND_PR: Отправляет ветку в репозиторий и открывает GitHub PR

Related MCP server: Maestro

Установка

npm install -g dev-loop-mcp

Или используйте через npx:

npx dev-loop-mcp

Конфигурация

Создайте dev-loop.config.json в корне вашего проекта:

{
  "buildCommand": "npm run build",
  "testCommand": "npm test",
  "deployCommand": "npm run deploy",
  "integTestCommand": "npm run test:integ",
  "branchPrefix": "claude/",
  "model": "claude-sonnet-4-6"
}

Все поля необязательны. Значения по умолчанию:

  • buildCommand: "npm run build"

  • testCommand: "npm test"

  • deployCommand: отсутствует (фаза DEPLOY пропускается)

  • integTestCommand: отсутствует (фаза INTEG_TEST пропускается)

  • branchPrefix: "claude/"

  • model: "claude-sonnet-4-6"

Переменные окружения

Переменная

Обязательно

Описание

ANTHROPIC_API_KEY

Да

Ваш API-ключ Anthropic

DEV_LOOP_ROOT

Нет

Корневой каталог проекта (по умолчанию cwd)

Настройка MCP

Добавьте в конфигурацию вашего MCP-клиента (например, claude_desktop_config.json для Claude Desktop):

{
  "mcpServers": {
    "dev-loop": {
      "command": "dev-loop-mcp",
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "DEV_LOOP_ROOT": "/path/to/your/project"
      }
    }
  }
}

Доступные инструменты

start_debug_loop

Запуск цикла отладки на основе описания симптома. ИИ диагностирует первопричины в виде ранжированных задач TDD, затем запускает стандартный конвейер TDD для каждой гипотезы и открывает PR с полным отчетом о диагностике.

{
  "symptom": "read_website returns failure on most real URLs",
  "context_files": ["src/tools/read-website.ts", "src/http/client.ts"]
}

Параметры:

  • symptom (обязательно) — описание наблюдаемой ошибки или сбоя на естественном языке

  • context_files (необязательно) — относительные пути к исходным файлам, которые ИИ должен прочитать во время диагностики

Шаг DIAGNOSE выполняется перед стандартным конвейером TDD (см. конечный автомат выше). Тело PR включает симптом, выявленные первопричины и то, что было исправлено.

Ветка называется <branchPrefix>debug/<symptom-slug>.

start_loop

Запуск нового цикла разработки.

{
  "description": "Add email validation to the user registration flow",
  "branch": "claude/email-validation"
}

Или с предварительно декомпозированными задачами:

{
  "tasks": [
    {
      "id": 1,
      "title": "Add email validator function",
      "scope": "src/utils/email.ts",
      "acceptance": "validateEmail returns true for valid emails and false for invalid ones"
    }
  ],
  "branch": "claude/email-validation"
}

resume_loop

Возобновление прерванного цикла:

{}

loop_status

Проверка текущего статуса цикла:

{}

Использование в качестве библиотеки

import { runLoop, loadConfig, RealShellAdapter, AnthropicDevWorker } from "dev-loop-mcp";
import Anthropic from "@anthropic-ai/sdk";

const config = await loadConfig("/path/to/project");
const client = new Anthropic();
const shell = new RealShellAdapter();
const aiWorker = new AnthropicDevWorker(client, config.model, shell);

const finalState = await runLoop(initialState, {
  shell,
  aiWorker,
  stateFilePath: "/path/to/project/.loop-state.json",
  repoRoot: "/path/to/project",
  config,
});

Available Tools

4 tools
advance_loopA

Report the outcome of the last instruction and get the next one. Call this after completing each phase step. The loop persists state between calls.

ParametersJSON Schema
NameRequiredDescriptionDefault
eventYesThe outcome event. One of: BranchCreated, TasksDecomposed, TaskDone, TaskFailed, BuildPassed, BuildFailed, DeployPassed, DeployFailed, IntegPassed, IntegFailed, IntegFixPassed, IntegFixFailed, QualityDone, TreeClean, PrCreated.
tasksNoFor TasksDecomposed: the decomposed or diagnosed task list.
failureReasonNoFor TaskFailed: why the task could not be completed.
stderrNoFor BuildFailed or DeployFailed: the error output.
failuresNoFor IntegFailed: array of {testFile, testName, description}.
prUrlNoFor PrCreated: the URL of the opened pull request.

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the loop persists state between calls, which is useful context. However, it doesn't describe error handling, rate limits, authentication needs, or what happens if called out of sequence. For a stateful tool with no annotations, more behavioral details would be helpful.

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 extremely concise (two sentences) and front-loaded with the core purpose. Every word earns its place, with no redundant information. The structure moves from primary function to usage guidance efficiently.

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 tool's complexity (stateful loop advancement with multiple event types) and 100% schema coverage but no output schema or annotations, the description is mostly complete. It explains the core workflow and when to use it, though it could benefit from mentioning what the tool returns (the 'next instruction') more explicitly since there's no output schema.

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 6 parameters thoroughly with their purposes and event-specific usage. The description doesn't add any parameter-specific information beyond what's in the schema, making the baseline score of 3 appropriate.

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 with specific verbs ('Report the outcome', 'get the next one') and distinguishes it from siblings by specifying it's for after completing each phase step in a loop. It explicitly mentions the loop persists state, which differentiates it from start_loop or loop_status.

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 provides explicit usage guidance: 'Call this after completing each phase step.' This tells the agent precisely when to use this tool versus alternatives like start_loop (for initiation) or loop_status (for checking status without advancing).

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

loop_statusB

Get the current status of the loop: phase, branch, task list, failure reason, or PR URL.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates this is a read operation ('Get'), but doesn't specify whether it requires authentication, has rate limits, returns real-time or cached data, or handles errors. The description adds minimal behavioral context beyond the basic action, missing key details for safe and effective use.

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 a single, efficient sentence that front-loads the core action ('Get the current status of the loop') and lists the retrieved data points without unnecessary words. Every element serves a purpose, making it highly concise and well-structured for quick understanding.

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

Completeness3/5

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It covers what data is retrieved, but without annotations or an output schema, it doesn't explain the return format (e.g., structure of the status object) or behavioral aspects like error handling. For a status-checking tool, this leaves some contextual needs unmet.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter information, focusing instead on the tool's purpose and output semantics. This aligns with the baseline expectation for tools without parameters, as the schema fully covers the input structure.

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 the tool's purpose with the verb 'Get' and specifies the resource 'current status of the loop', including what information is retrieved (phase, branch, task list, failure reason, PR URL). It distinguishes itself from sibling tools like 'advance_loop', 'start_debug_loop', and 'start_loop' by focusing on status retrieval rather than initiation or progression, though it doesn't explicitly name these alternatives.

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 by listing the specific data points retrieved (e.g., phase, failure reason), suggesting it's for monitoring or checking loop progress. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., when to check status vs. start or advance a loop) or any prerequisites, leaving usage context inferred rather than stated.

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

start_debug_loopC

Start a debug loop from a symptom description. Returns an instruction telling you to diagnose root causes as ranked TDD tasks, then proceeds through the standard TDD pipeline. The PR body will include a diagnosis writeup.

ParametersJSON Schema
NameRequiredDescriptionDefault
symptomYesNatural-language description of the observed bug or failure.

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions the tool returns an instruction for TDD tasks and that the PR body includes a diagnosis writeup, giving some insight into outputs and workflow. However, it lacks critical details like whether this is a read-only or mutating operation, error handling, or any side effects, which are essential for a tool that starts processes.

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 appropriately concise with two sentences that front-load the core action and follow with output details. Each sentence adds value: the first defines the tool's purpose, and the second explains the return behavior and PR inclusion. There's no wasted text, though it could be slightly more structured for clarity.

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

Completeness2/5

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

Given the tool's complexity (starting a debug process with no annotations or output schema), the description is incomplete. It covers the basic action and some output hints but misses key behavioral aspects like mutation status, error cases, or interaction with sibling tools. Without annotations or an output schema, more detail is needed to fully inform the agent about this process-initiating tool.

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?

The input schema has 100% description coverage, with the 'symptom' parameter fully documented as a natural-language bug description. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. This meets the baseline score of 3, as the schema does the heavy lifting without extra value from the description.

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 the tool's purpose: 'Start a debug loop from a symptom description.' It specifies the verb ('Start'), resource ('debug loop'), and input trigger ('symptom description'). However, it doesn't explicitly differentiate from sibling tools like 'start_loop' or 'advance_loop', which reduces clarity about when to choose this specific debug variant.

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

Usage Guidelines2/5

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

The description provides minimal guidance on when to use this tool. It mentions the tool initiates a debug loop from a symptom, but offers no explicit when/when-not criteria or alternatives. While it implies usage for bug diagnosis, it doesn't compare to sibling tools like 'start_loop' or specify prerequisites, leaving the agent with little contextual direction.

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

start_loopA

Start a new TDD development loop. Returns an instruction telling you what to do first. Keep calling advance_loop after each step until the loop reaches DONE or FAILED. Phases: INIT → DECOMPOSE → TDD_LOOP → BUILD → DEPLOY → INTEG_TEST → INTEG_FIX → QUALITY_REVIEW → CLEAN_TREE_CHECK → PUSH_AND_PR.

ParametersJSON Schema
NameRequiredDescriptionDefault
descriptionNoNatural language description of the work to be done.
tasksNoPre-decomposed task list. If provided, skips DECOMPOSE.
branchNoGit branch name. Generated from description if omitted.

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the tool's purpose and procedural flow (phases, interaction with advance_loop), but lacks details about potential side effects, error conditions, authentication needs, or what 'DONE or FAILED' states entail. It provides basic behavioral context but misses important operational details.

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 efficiently structured in two sentences: the first states the purpose and return value, the second provides crucial procedural guidance and lists all phases. Every element serves a clear purpose with zero wasted words.

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

Completeness3/5

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

Given the tool's complexity (managing a multi-phase development loop) and the absence of both annotations and output schema, the description provides adequate procedural context but lacks details about return values, error handling, and operational constraints. It's complete enough to understand the basic workflow but leaves important implementation questions unanswered.

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 three parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 the action ('Start a new TDD development loop') and the resource (the loop itself), with a specific verb. However, it doesn't explicitly distinguish this from its sibling 'start_debug_loop' beyond the name difference, leaving some ambiguity about when to choose one over the other.

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 provides explicit usage guidance: it tells the agent to 'Keep calling advance_loop after each step until the loop reaches DONE or FAILED' and lists all the phases of the loop. This gives clear procedural context for when and how to use this tool in relation to its sibling 'advance_loop'.

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. Dates show when Glama detected each change.

  1. 4 tool updatesv0.1.0
    • First observedadvance_loop
    • First observedloop_status
    • First observedstart_debug_loop
    • First observedstart_loop
Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.8/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: advance_loop progresses the loop, loop_status checks current state, start_debug_loop initiates debugging, and start_loop initiates standard development. The descriptions make it impossible to confuse their functions.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case throughout: advance_loop, loop_status, start_debug_loop, and start_loop. The naming is predictable and readable across the set.

Tool Count5/5

Four tools is well-scoped for a development loop server, covering initiation (start_loop, start_debug_loop), progression (advance_loop), and status checking (loop_status). Each tool earns its place without bloat or gaps.

Completeness5/5

The tool set provides complete lifecycle coverage for TDD development loops: starting loops (standard and debug), advancing through phases, and checking status. No obvious gaps exist for the stated purpose, enabling agents to manage loops end-to-end.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/soynog/dev-loop-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server