Kotlin Multi-Agent Orchestrator

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generate_kotlin_api_bridge

Generates full-stack API bridge contracts for Ktor backends and clients, producing Ktor routes, Kotlin/Dart DTOs, and OpenAPI specifications from service models and endpoints.

Instructions

Generates full-stack API bridge contracts between Ktor backend and clients, including Ktor routes, Kotlin/Dart DTOs, and OpenAPI specifications.

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": {
      "serviceName": "<serviceName>"
    },
    "name": "generate_kotlin_api_bridge"
  }
}

Input Schema

Name	Required	Description
`models`	No	List of data models.
`endpoints`	No	List of endpoints.
`serviceName`	Yes	Name of the API service (e.g. 'Order', 'Payment', 'Auth').

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 30, 2026

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It states that the tool produces API bridge artifacts, but it does not reveal side effects such as file system writes, project modifications, overwriting behavior, required project state, or reversibility. This is a meaningful gap for a code generation tool.

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 sentence with no filler words. It front-loads the tool's core action and resource, then lists concrete output types. Every phrase adds useful context.

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?

The schema covers all parameters and the description names key output artifacts, but the tool lacks an output schema and annotations. Given the complexity of generating full-stack contracts, the description would benefit from mention of expected output structure, generation location, or prerequisites, so it is only minimally complete.

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%, with 'serviceName', 'models', and 'endpoints' each documented in the input schema. The description adds no additional parameter-level semantics beyond the schema, so the baseline score of 3 is 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 uses a specific verb ('Generates'), names a clear resource ('full-stack API bridge contracts between Ktor backend and clients'), and enumerates concrete deliverables (Ktor routes, Kotlin/Dart DTOs, OpenAPI specifications). This makes the tool's purpose specific and distinguishes it from general backend scaffolding siblings like scaffold_kotlin_backend.

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 no explicit guidance on when to use this tool versus alternatives such as scaffold_kotlin_backend or generate_kotlin_platform_config. There are no conditions, exclusions, or sibling comparisons, so the agent must infer usage solely from the tool's name and output description.

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