Parallel.ai Task Management
Connects to Parallel.ai's APIs to run deep research tasks and batch operations directly from your LLM client.
Highly accurate deep search and batch tasks
What it does
- Initiate deep research tasks
- Execute batch task groups
- Access Parallel.ai APIs
- Run experimental workflows
Best for
About Parallel.ai Task Management
Parallel.ai Task Management is a community-built MCP server published by parallel-web that provides AI assistants with tools and capabilities via the Model Context Protocol. Parallel.ai Task Management offers top AI tools for deep search and batch tasks, making AI software development easy wit It is categorized under productivity, developer tools.
How to install
You can install Parallel.ai Task Management in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
License
Parallel.ai Task Management is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Parallel Task MCP
The Parallel Task MCP allows initiating deep research or task groups directly from your favorite LLM client. It can be a great way to get to know Parallel’s different APIs by exploring their capabilities, but can also be used as a way to easily do small experiments while developing production systems using Parallel APIs. Please read our MCP docs here for more details.
Installation
The official installation instructions can be found here.
{
"mcpServers": {
"Parallel Task MCP": {
"url": "https://task-mcp.parallel.ai/mcp"
}
}
}
Running locally
Running locally
This repo contains a proxy to the mcp which is hosted at: https://task-mcp.parallel.ai/mcp
How to run and test locally:
wrangler devnpx @modelcontextprotocol/inspector- Connect to server: http://localhost:8787/mcp
Alternatives
Related Skills
Browse all skillsAutomate Wrike project management via Rube MCP (Composio): create tasks/folders, manage projects, assign work, and track progress. Always search tools first for current schemas.
Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
Linear issue management guide. Use when working with Linear issues, creating issues, updating status, or adding comments. Triggers on Linear issue references (LOBE-xxx), issue tracking, or project management tasks. Requires Linear MCP tools to be available.
Create, update, and manage GitHub issues using MCP tools. Use this skill when users want to create bug reports, feature requests, or task issues, update existing issues, add labels/assignees/milestones, or manage issue workflows. Triggers on requests like "create an issue", "file a bug", "request a feature", "update issue X", or any GitHub issue management task.
Automate Todoist task management, projects, sections, filtering, and bulk operations via Rube MCP (Composio). Always search tools first for current schemas.
This skill should be used whenever users request personal assistance tasks such as schedule management, task tracking, reminder setting, habit monitoring, productivity advice, time management, or any query requiring personalized responses based on user preferences and context. On first use, collects comprehensive user information including schedule, working habits, preferences, goals, and routines. Maintains an intelligent database that automatically organizes and prioritizes information, keeping relevant data and discarding outdated context.