flutter-orchestrator

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orchestrate_flutter_project

Turn a prompt into a complete Flutter project: verifies target platforms, decomposes requirements, then generates pubspec, UI screens, backend, and tests.

Instructions

End-to-end multi-agent orchestration for Flutter apps. Checks target platforms first (asks if not specified), then executes prompt decomposition, pubspec creation, UI screens, backend/state layer, and tests.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "orchestrate_flutter_project"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	The complete project prompt or feature specification.
`includeUi`	No	Whether to generate responsive Flutter UI screens.
`projectName`	No	Flutter project name (e.g. 'e_commerce_app').	flutter_app
`includeTests`	No	Whether to generate Unit & Widget test suites.
`includeBackend`	No	Whether to scaffold Clean Architecture data sources and repositories.
`stateManagement`	No	State management system.	riverpod
`targetPlatforms`	No	Target platforms. If omitted and not mentioned in the prompt, execution will pause and request platform clarification.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 23, 2026

TDQS

A3.8/5.0

Behavior4/5

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, and it does so well. It reveals a key behavior: 'Checks target platforms first (asks if not specified),' warning the agent that the tool may pause for clarification. It also sets expectations that the tool will execute multiple steps (decomposition, file creation). This is valuable context about side effects that the schema cannot convey.

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?

Two sentences with no filler; the first sentence states the purpose, and the second lists the workflow. Information is effectively front-loaded. The list of steps is a bit long, but for an orchestration tool of this complexity, it's an appropriate and dense summary. Nearly every word carries meaning.

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?

For a tool with 7 params, no output schema, and no annotations, the description competently maps the high-level flow. However, given the tool's complexity, an agent might need more context about return values, expected execution time/long-running operations, or failure states. The description clearly explains the 'what' and 'in what order' but is silent on outcomes or errors, which is a noticeable gap for a high-stakes orchestrator.

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%, so the schema already documents all parameters thoroughly, hitting the baseline of 3. The description adds value by explaining the targetPlatforms interaction (asks if omitted), which is not fully captured in the parameter description alone. However, it doesn't provide additional detail on other parameters like prompt or stateManagement, which are already adequately described in 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 a specific verb and resource: 'End-to-end multi-agent orchestration for Fltter apps.' It enumerates a concrete pipeline (platform checks, prompt decomposition, pubspec creation, UI, backend/state, tests), which distinguishes it from the many specialized sibling tools like design_flutter_ui or scaffold_flutter_backend. An agent can immediately tell this is the do-everything orchestrator. This goes beyond a mere restatement of the name.

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 full-project generation by listing the full workflow (UI screens, backend/state layer, tests), which strongly contrasts with the single-purpose siblings. However, it never explicitly states 'use this for full projects' or 'for just one layer use design_flutter_ui instead.' The usage context is implied through the listed pipeline steps rather than stated as guidance.

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