Dispersl MCP Server

A Model Context Protocol (MCP) server implementation that integrates with Dispersl; The AI Dev Team, to give you multi-agents that work together to build software.

This MCP server can use other MCP servers as well for distributed, tool-driven workflows.

Built for modern AI-driven development, with support for multiple LLM models, multi-agent planning, and full SDLC automation.

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Features

• Multi-agent orchestration (plan, code, test, git, docs, chat)
• Code generation, test generation, and documentation
• Git operations and repo management
• Conversational agentic chat
• API key and session management
• Connect to and manage other MCP servers (local or remote)
• Extensible with custom tools and external agents
• Works with Cursor, VS Code, and other MCP-compatible clients

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Installation

Running with npx

env DISPERSL_API_KEY=your-api-key npx -y dispersl-mcp

Manual Installation

npm install -g dispersl-mcp

Running on Cursor

For the most up-to-date configuration instructions, see the Cursor MCP Server Configuration Guide.

Example Cursor MCP config:

{
  "mcpServers": {
    "dispersl-mcp": {
      "command": "npx",
      "args": ["-y", "dispersl-mcp"],
      "env": {
        "DISPERSL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Running Locally

export DISPERSL_API_KEY=your-api-key
npm run dev

Running with Custom Models

You can specify default models for each agent type:

export DISPERSL_PLAN_MODEL=deepseek/deepseek-chat-v3-0324:free
export DISPERSL_CODE_MODEL=anthropic/claude-sonnet-4
export DISPERSL_TEST_MODEL=anthropic/claude-sonnet-4
export DISPERSL_GIT_MODEL=meta-llama/llama-4-maverick:free
export DISPERSL_DOCS_MODEL=openai/gpt-4o-mini
export DISPERSL_CHAT_MODEL=openai/gpt-4o-mini

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Configuration

MCP Config Example

To connect to local and external MCP servers, use .dispersl/mcp.json:

{
  "mcpServers": {
    "anthropic-main": {
      "command": "npx",
      "args": [
        "-y",
        "--package=@anthropic-ai/mcp-server",
        "anthropic-mcp-server"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "${ANTHROPIC_API_KEY}"
      }
    },
    "anthropic-backup": {
      "command": "npx",
      "args": [
        "-y",
        "--package=@anthropic-ai/mcp-server",
        "anthropic-mcp-server"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "${ANTHROPIC_API_KEY_BACKUP}"
      }
    }
  }
}

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Usage

Tool Reference

Tool Name	Description
list_models	List available models
dispersl_code_agent	Generate code files and codebases based on a prompt using agentic execution
dispersl_testing_agent	Generate end to end tests based on a prompt using agentic execution
dispersl_git_agent	Execute codebase versioning operations with Git based on a prompt using agentic execution
dispersl_new_docs_agent	Generate file by file technical documentation for a code repository using agentic execution
dispersl_chat_agent	Chat with the Dispersl agent to get knowledge or insights about codebases using agentic execution
dispersl_plan_agent	Multi-agent task dispersion using agentic execution (plan agent)
start_session	Start a new agentic session
end_session	End an active session
add_mcp_server	Connect to an external MCP server and save to config
remove_mcp_server	Disconnect from an MCP server and remove from config
get_models	List available AI models
get_keys	Get API keys for the authenticated user
new_key	Generate new API key
create_task	Create a new task
edit_task	Edit a task by ID
get_tasks	Get all tasks
get_task	Get a task by ID
cancel_task	Cancel a task by ID
edit_step	Edit a step by ID
get_steps	Get all steps
get_step	Get a step by ID
cancel_step	Cancel a step by ID
get_usage_stats	Get usage stats
get_language_stats	Get language usage stats
get_agent_stats	Get agent query stats
get_task_history	Get task history by ID
get_step_history	Get step history by ID
fetch_api_root	Fetch API root (utility endpoint)
health_check	Health check endpoint

Example: Chat

This agent is able to interact with the user to fetch insights, shared memories and task progress to the user.

await client.callTool({
  name: "start_session",
  arguments: { session_id: "my-session" }
});

const response = await client.callTool({
  name: "dispersl_chat_agent",
  arguments: {
    prompt: "Hello, how are you?",
    model: "meta-llama/llama-4-maverick:free"
  }
});

await client.callTool({
  name: "end_session",
  arguments: { session_id: "my-session" }
});

Example: Plan Agent

This agent is able to coordinate all the agents i.e code, test, git, documentation to execute complex tasks. Agents work in sync and handover tasks to each other once they complete their assigned role.

const response = await client.callTool({
  name: "dispersl_plan_agent",
  arguments: {
    prompt: "Plan a workflow for building and testing an ExpressJS web app using TypeScript",
    model: "meta-llama/llama-4-maverick:free",
    agents: ["code", "test", "git", "docs"]
  }
});

Example: Code Generation

This agent is able to run autonomously. It can also collaborate with the other agents i.e code, test, git, documentation to execute complex tasks. Agents work in sync and handover tasks to each other once they complete their assigned role.

const response = await client.callTool({
  name: "dispersl_code_agent",
  arguments: {
    prompt: "Create a simple hello world function",
    model: "meta-llama/llama-4-maverick:free"
  }
});

Example: Add External MCP Server

await client.callTool({
  name: "add_mcp_server",
  arguments: {
    name: "my-server",
    command: "node",
    args: ["dist/server.js"],
    env: { PORT: "8080" }
  }
});

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Development

# Install dependencies
npm install

# Run in development mode
npm run dev

# Build
npm run build

# Run tests (requires DISPERSL_API_KEY)
npm test

# Lint
npm run lint

# Format code
npm run format

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Contributing

Contributions are welcome! Please submit a Pull Request.

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License

MIT License - see LICENSE file for details