# 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](https://github.com/parallel-web/search-mcp), creating research or enrichment tasks is billable. Review [Task API pricing](https://docs.parallel.ai/getting-started/pricing#task-api) before starting work. [Setup documentation](https://docs.parallel.ai/integrations/mcp/task-mcp#installation) · [Get an API key](https://platform.parallel.ai) · [Task MCP documentation](https://docs.parallel.ai/integrations/mcp/task-mcp) ## 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: ```bash 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: ```bash 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](https://docs.parallel.ai/integrations/mcp/task-mcp#codex-cli). ### Cursor, VS Code, and other clients Use this server URL with HTTP / Streamable HTTP transport: ```text https://task-mcp.parallel.ai/mcp ``` For Cursor, merge this into your MCP configuration: ```json { "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](https://docs.parallel.ai/integrations/mcp/task-mcp#installation) 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 `createDeepResearch` to research Parallel Web Systems (). 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 (), Google (), and Apple (). 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](https://docs.parallel.ai/integrations/mcp/task-mcp) 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](https://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](https://docs.parallel.ai/integrations/mcp/task-mcp#troubleshooting) or [open an issue](https://github.com/parallel-web/task-mcp/issues). 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](https://parallel.ai/customer-terms) and [Privacy Policy](https://parallel.ai/privacy-policy). For product support, contact [support@parallel.ai](mailto: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.