mcpField
R
Role-Specific Context MCP Server

Role-Specific Context MCP Server

Local
github.com/Chris-JuneAI & Agents1Updated 84w ago

About Role-Specific Context MCP Server

Role-Specific Context MCP Server manages role-scoped instructions, tone, and memory for AI agents. It provides predefined and custom roles, keeping relevant context separated by role.

Key features

  1. 01Create, update, and delete roles
  2. 02Role-specific instructions and tone
  3. 03Role-scoped memory with relevance retrieval
  4. 04Memory TTL and per-role limits
  5. 05Resources, tools, and prompts for roles

Use cases

  1. 01Process queries with a chosen role
  2. 02Keep memories separate across agent roles
  3. 03Adjust an agent's tone for a role
  4. 04Create custom roles for specialist tasks

MCP support

The server exposes role details and tone profiles as resources, role and memory operations as tools, and role-based interaction prompts to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Role-Specific Context MCP Server

A Model Context Protocol (MCP) server that defines and governs contextual boundaries based on agent roles in your system.

Overview

This MCP server enables role-based context management for AI agents, allowing you to:

  • Establish clear instructions, objectives, and domain knowledge for each AI agent (Marketing Expert, Songwriter, Executive Assistant, etc.)
  • Keep role-relevant memory partitioned and scoped, preventing cross-contamination between different agent roles
  • Adapt tone and style dynamically (serious, witty, sarcastic) per role, with tone profiles baked into the prompt

Features

Role Management
  • Create, update, and delete custom roles
  • Pre-defined roles with specific expertise domains
  • Role-specific system prompts and instructions
  • Customizable tone profiles
Memory Management
  • Role-specific memory storage
  • Memory retrieval based on relevance to current query
  • Time-to-live (TTL) for memories
  • Memory limits per role
MCP Integration
  • Exposes roles as MCP resources
  • Provides tools for role management and query processing
  • Offers prompts for role-based interactions

Getting Started

Prerequisites
  • Node.js 18+
  • OpenAI API key
Installation
# Clone the repository
git clone https://github.com/yourusername/role-context-mcp.git
cd role-context-mcp

# Install dependencies
npm install

# Set up environment variables
echo "OPENAI_API_KEY=your_api_key_here" > .env

# Build the project
npm run build
Running the Server
# Run the MCP server
npm start

# Run the HTTP server for testing
npm run start:http
Configuration

The server can be configured by modifying src/config.ts. Key configuration options include:

  • Default roles and their properties
  • Available tone profiles
  • Memory management settings
  • OpenAI model selection

MCP Integration

Resources

The server exposes the following resources:

  • role://{roleId} - Information about a specific role
  • role://tones - Available tone profiles
Tools

The server provides the following tools:

  • process-with-role - Process a query using a specific role
  • create-role - Create a new role
  • update-role - Update an existing role
  • delete-role - Delete a custom role
  • change-role-tone - Change the tone of a role
  • store-memory - Store a memory for a specific role
  • clear-role-memories - Clear all memories for a role
Prompts

The server provides the following prompts:

  • role-{roleId} - Use a specific role to process a request
  • create-custom-role - Create a new custom role

Example Usage

Processing a Query with a Role (MCP)
// Example of using the process-with-role tool
const result = await client.executeToolRequest({
  name: 'process-with-role',
  parameters: {
    roleId: 'marketing-expert',
    query: 'How can I improve my social media engagement?',
    customInstructions: 'Focus on B2B strategies'
  }
});
Processing a Query with a Role (HTTP API)
// Example of using the HTTP API
const response = await axios.post('http://localhost:3000/process', {
  roleId: 'marketing-expert',
  query: 'How can I improve my social media engagement?',
  customInstructions: 'Focus on B2B strategies'
});

console.log(response.data.response);
Creating a Custom Role
// Example of using the create-role tool
const result = await client.executeToolRequest({
  name: 'create-role',
  parameters: {
    id: 'tech-writer',
    name: 'Technical Writer',
    description: 'Specializes in clear, concise technical documentation',
    instructions: 'Create documentation that is accessible to both technical and non-technical audiences',
    domains: ['technical-writing', 'documentation', 'tutorials'],
    tone: 'technical',
    systemPrompt: 'You are an experienced technical writer with expertise in creating clear, concise documentation for complex systems.'
  }
});

License

MIT

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.