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scaffold_service

Generates a standards-compliant Spring Boot microservice scaffold from your specs, including REST, Kafka, Consul, JPA, logging, Dockerfile, and tests.

Instructions

Scaffold a new Comviva Spring Boot microservice following company standards (packaging, logging, Kafka, Consul config, error handling, OpenAPI). Returns a structured prompt that yields a complete file tree with full source for pom.xml, the application class, config beans, controllers, producers/consumers, entities, application.yml/bootstrap.yml, logback-spring.xml, Dockerfile, and tests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
portNoHTTP port.
notesNoAnything else the generator should know (special integrations, naming, etc.).
authorNoAuthor tag for class-level JavaDoc.
entitiesNo
descriptionYesWhat the service does, in 1-3 sentences.
kafkaTopicsNo
serviceNameYesService name, lowercase. Used for artifactId, root package, application class name.
capabilitiesYesWhich features the service needs. Each capability pulls in the appropriate dependencies, packages, and boilerplate.
restEndpointsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, but it does disclose an important trait: the tool 'returns a structured prompt' rather than writing files directly, which prevents a common misuse assumption. It stops short of stating side effects, permissions, or whether anything is persisted.

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?

Two sentences, front-loaded with the action and scope, followed by the return value. The enumerated output list earns its place by telling the agent exactly what a call produces.

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?

For a complex 9-parameter generator with no output schema, the description does a solid job explaining the deliverable (file tree with full source) and the standards applied. It omits usage routing and prerequisite context, but the core contract is 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 coverage is 67%, so the schema already documents most parameters. The description maps its standards list (packaging, logging, Kafka, Consul config, OpenAPI, error handling) to the capabilities concept, adding modest value, but it doesn't explain requirements like the serviceName pattern or how capabilities drive boilerplate.

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?

Specific verb+resource ('Scaffold a new Comviva Spring Boot microservice') with an explicit scope of company standards. It is clearly distinguishable from the generic generate_code sibling by naming the artifact and the standards it follows, though it never explicitly differentiates itself.

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?

No when-to-use, when-not-to-use, or alternatives are given. The scenario (creating a new service) is inferable, but there is no guidance on how this differs from generate_code, recommend_architecture, or generate_implementation_plan.

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