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dispersl-mcp

by Code-Fundi
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TypeScript
Remote

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.

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

Related MCP server: Agentic MCP Server

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

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}"
      }
    }
  }
}

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" }
  }
});

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

Contributing

Contributions are welcome! Please submit a Pull Request.

License

MIT License - see LICENSE file for details

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