MCP Server

The Prathya MCP server exposes the contract management surface via the Model Context Protocol, enabling AI coding agents to participate directly in the contract-driven development loop.

An agent can read the contract before generating code, check coverage after generating tests, and iterate until the contract is satisfied.

Prerequisites

1. Install JBang

JBang is used to run the MCP server without manual classpath setup.

Linux / macOSWindowsSDKMAN!

curl -Ls https://sh.jbang.dev | bash -s - app setup

iex "& { $(iwr -useb https://ps.jbang.dev) } app setup"

sdk install jbang

2. Install the MCP Server Artifact

Fetch the Prathya MCP server uber-jar into your local Maven repository:

mvn dependency:get -Dartifact=com.intrigsoft.prathya:prathya-mcp-server:0.6.1

Configuration

Add the Prathya MCP server to your AI coding tool's MCP configuration. The server uses stdio transport.

Generic MCP Configuration

{
  "mcpServers": {
    "prathya": {
      "command": "jbang",
      "args": [
        "--quiet",
        "--main", "com.intrigsoft.prathya.mcp.PrathyaMcpServer",
        "com.intrigsoft.prathya:prathya-mcp-server:0.6.1"
      ]
    }
  }
}

Configuration Options

Option	Default	Description
`--contract-file`	`CONTRACT.yaml`	Path to the contract file
`--test-classes`	`target/test-classes`	Path to compiled test classes

To pass options, append them after the artifact coordinate:

{
  "mcpServers": {
    "prathya": {
      "command": "jbang",
      "args": [
        "--quiet",
        "--main", "com.intrigsoft.prathya.mcp.PrathyaMcpServer",
        "com.intrigsoft.prathya:prathya-mcp-server:0.6.1",
        "--contract-file", "/path/to/CONTRACT.yaml",
        "--test-classes", "/path/to/target/test-classes"
      ]
    }
  }
}

Tool Reference

The server exposes 13 tools organized into read and write operations.

Read Tools

Tool	Description
`get_contract`	Returns the full parsed contract as JSON
`list_requirements`	Lists all requirements with ID, title, status, and version
`get_requirement`	Returns full details for a specific requirement by ID
`list_untested`	Lists approved requirements and corner cases with no mapped tests
`get_coverage_matrix`	Returns the full coverage matrix: requirements, mapped tests, and coverage percentages
`run_audit`	Runs the audit engine and returns all violations
`validate_contract`	Validates the CONTRACT.yaml against the schema

Write Tools

Tool	Description
`add_requirement`	Adds a new requirement to the contract
`update_requirement`	Updates an existing requirement's fields
`add_corner_case`	Adds a corner case to an existing requirement
`update_corner_case`	Updates an existing corner case
`deprecate_requirement`	Sets a requirement's status to `deprecated`
`supersede_requirement`	Marks a requirement as superseded and links to its replacement

Agent Workflow

A typical workflow for an AI agent using the MCP server:

Read the contract — get_contract or list_requirements to understand what the module must do
Check coverage — list_untested to find gaps
Generate tests — write tests annotated with @Requirement for uncovered items
Verify — get_coverage_matrix to confirm coverage improved
Iterate — repeat until the contract is fully satisfied

Technical Details

Transport: stdio (standard input/output)
Packaging: Maven Shade uber-jar
SDK: MCP SDK io.modelcontextprotocol.sdk:mcp:1.0.0