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.

Related MCP server: olostep-mcp

Available tools

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 createDeepResearch to 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 createTaskGroup to 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:

1. Start the job once and keep its run or group ID.

2. Use getStatus to check progress while continuing other work.

3. Once complete, use getResultMarkdown to 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_KEY must 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:

1. Run npm install.

2. Run npx wrangler dev and leave it running.

3. In another terminal, run npx @modelcontextprotocol/inspector.

4. Connect the Inspector to http://localhost:8787/mcp using Streamable HTTP.

The local proxy still calls the hosted service. Authentication and task charges still apply; this is not an offline task runner.