Parallel Task MCP MCP Server
ai.parallel/task-mcp
Deep research and data enrichment for AI agents via Parallel's hosted service.
What is the Parallel Task MCP MCP server?
Parallel Task MCP is an MCP server that enables AI assistants to conduct deep research on questions and enrich datasets through Parallel's hosted service. It provides asynchronous research and enrichment tasks with sourced results, requiring a Parallel account and API key for access.
Task MCP lets you offload research and data enrichment work to Parallel's service without leaving your agent. Start a research job to answer detailed questions or enrich a dataset with a common schema, check progress asynchronously, and retrieve results as Markdown. This is useful for gathering sourced information, verifying facts, and bulk data enrichment tasks.
How to install Parallel Task MCP
Copy-paste configuration for popular MCP clients.
Tools & capabilities
Tools this server exposes to the agent.
createDeepResearch— Starts a research job for a question that needs a detailed, sourced answer.createTaskGroup— Starts enrichment jobs for a list of inputs using a common output schema.getStatus— Checks whether a run or group is still active.getResultMarkdown— Retrieves research or enrichment output as Markdown.
Use cases
- Research a company or topic with sourced citations and verification flags
- Enrich a list of companies with standardized fields like founding year, headquarters, and product descriptions
- Verify facts and gather detailed information for reports or analysis
- Bulk data enrichment using a consistent output schema across multiple inputs
- Asynchronous research workflows that continue while you work on other tasks
Parallel Task MCP MCP server FAQ
It's an MCP server that connects your AI agent to Parallel's deep research and data enrichment service. You submit research questions or enrichment jobs, and retrieve sourced results asynchronously.
No. Task MCP requires a Parallel account and API key. Creating research or enrichment tasks is billable; check Task API pricing in Parallel's documentation before starting work.
Add the server URL `https://task-mcp.parallel.ai/mcp` to your Cursor MCP configuration under `mcpServers` with HTTP/Streamable HTTP transport. Complete browser sign-in when prompted.
Run `claude mcp add --transport http --scope project parallel-task https://task-mcp.parallel.ai/mcp`, then start a new Claude Code session and complete Parallel sign-in in your browser.
You need a Parallel account and API key from platform.parallel.ai. Clients support OAuth sign-in or Bearer token authentication via the API key in secure credential settings.
Tasks run asynchronously: create a job once with `createDeepResearch` or `createTaskGroup`, use `getStatus` to check progress, and retrieve results with `getResultMarkdown` when complete. Do not create duplicate jobs to check status.
README (reference)
Source of truth, from the repository.
Parallel Task MCP
Deep research and data enrichment from your AI assistant.
Research a question or enrich a small dataset, then retrieve the results without leaving your agent. Parallel hosts the service; you do not need to run this repository to use it.
Task MCP requires a Parallel account and authentication. Unlike the free Search MCP, creating research or enrichment tasks is billable. Review Task API pricing before starting work.
Setup documentation · Get an API key · Task MCP documentation
Manual setup
Choose one connection method for your client. Reuse an existing connection to this endpoint rather than adding it twice.
Claude Code
Run this in the project where you want to use Parallel:
claude mcp add --transport http --scope project parallel-task https://task-mcp.parallel.ai/mcp
Start a new Claude Code session, run /mcp, and complete the Parallel sign-in in your browser when prompted.
Codex
Make your API key available as PARALLEL_API_KEY in the environment that launches Codex, then run:
codex mcp add parallel-task --url https://task-mcp.parallel.ai/mcp \
--bearer-token-env-var PARALLEL_API_KEY
Start a new Codex session and run /mcp to check the connection. The configuration references the environment variable, not the key itself. Do not commit API keys to configuration files or paste them into a conversation. For OAuth instead, see the Codex setup instructions.
Cursor, VS Code, and other clients
Use this server URL with HTTP / Streamable HTTP transport:
https://task-mcp.parallel.ai/mcp
For Cursor, merge this into your MCP configuration:
{
"mcpServers": {
"parallel-task": {
"url": "https://task-mcp.parallel.ai/mcp"
}
}
}
Complete the Parallel sign-in when prompted. Clients without compatible OAuth support can use a Parallel API key as a Bearer token in the Authorization header; use the client's secure credential settings rather than hardcoding the key in a shared file.
VS Code uses a top-level servers object, not mcpServers. Follow the client-specific setup instructions for its configuration format and authentication options.
Available tools
| Tool | What it does |
|---|---|
createDeepResearch | Starts a research job for a question that needs a detailed, sourced answer. |
createTaskGroup | Starts enrichment jobs for a list of inputs using a common output schema. |
getStatus | Checks whether a run or group is still active. |
getResultMarkdown | Retrieves research or enrichment output as Markdown. |
Verify the connection by checking that these four tools are available. You do not need to start a paid job just to check installation.
Try it
Research a company:
Use
createDeepResearchto research Parallel Web Systems (https://parallel.ai). Write a company brief covering its products, target customers, and publicly announced funding. Cite your sources and flag anything you cannot verify.
Enrich a list of companies:
Use
createTaskGroupto enrich these three companies: Parallel Web Systems (https://parallel.ai), Google (https://google.com), and Apple (https://apple.com). Return one row per company with its official company name, website, headquarters, founding year, and a one-sentence product description. Include source URLs and leave any fields you cannot verify blank.
Research and enrichment run asynchronously:
- Start the job once and keep its run or group ID.
- Use
getStatusto check progress while continuing other work. - Once complete, use
getResultMarkdownto retrieve and analyze the output.
If your client does not continue automatically, ask it to check the existing job in a follow-up message. Do not create another job just to check progress. See the workflow guide for more examples.
Troubleshooting
- Tools are missing: start a new agent session, check the MCP connection list, and confirm the URL is
https://task-mcp.parallel.ai/mcp. - Authentication fails: complete browser sign-in, or check that your client can access a valid API key. For the Codex command above,
PARALLEL_API_KEYmust be present in the environment that launches Codex. - Insufficient credits / HTTP 402: check your balance at platform.parallel.ai before starting another task.
- No results yet: check the existing ID with
getStatus; starting a job does not wait for completion. - Still stuck: see the troubleshooting guide or open an issue. Include your client, version, and error message, but leave out credentials and private task inputs or outputs.
Privacy, support, and security
Task inputs are sent to Parallel to run the requested research or enrichment. Hosted service usage is governed by the Customer Terms and Privacy Policy.
For product support, contact support@parallel.ai.
Running the proxy locally
From a checkout of this repository, with Node.js and npm available:
- Run
npm install. - Run
npx wrangler devand leave it running. - In another terminal, run
npx @modelcontextprotocol/inspector. - Connect the Inspector to
http://localhost:8787/mcpusing Streamable HTTP.
The local proxy still calls the hosted service. Authentication and task charges still apply; this is not an offline task runner.
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