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Parallel Search MCP

parallel-web/search-mcp
HTTPregistry active
Summary

Parallel's search MCP connects Claude to their web search API over streamable HTTP, giving your agent access to what they position as production-grade web retrieval with cross-referenced facts and source provenance. The server exposes search operations that return structured results rather than raw HTML, designed to reduce hallucination in agent workflows. Parallel claims significant accuracy advantages on benchmarks like HLE-Search and BrowseComp, outperforming alternatives from Exa, Tavily, and Perplexity at lower cost per query. Reach for this when you need your agent to ground responses in verifiable web data, especially for research tasks requiring multi-hop reasoning. The service is SOC-II Type 2 certified and uses a pay-per-query model with flexible compute budgets based on task complexity.

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Parallel

Parallel Search MCP

Free web search for your coding agent.

Find current documentation, check facts, and read web pages without leaving your agent. No account or API key needed to get started.

Ask your agent to install

Copy this into your coding agent:

Install Parallel's free Search MCP in the coding agent I'm using now.
Identify your client and use its native MCP setup instructions:
https://docs.parallel.ai/integrations/mcp/search-mcp

Server name: parallel-search
Server URL: https://search.parallel.ai/mcp
Transport: Streamable HTTP
Authentication: none. No account, API key, or Parallel CLI is needed.

Preserve existing settings and reuse any connection to this endpoint.
Tell me if a restart or approval is required.

Once connected, check that web_search and web_fetch are available.
Use Parallel to find the official FastAPI response-model documentation
and return a source link. Report anything you couldn't verify.

Install in Cursor · Install in VS Code · Manual setup · Documentation

Manual setup

Choose your client. You only need one of these options.

Codex

codex mcp add parallel-search --url https://search.parallel.ai/mcp

Start a new Codex session and run /mcp to check the connection. See the Codex MCP guide for configuration options.

Gemini CLI

gemini extensions install https://github.com/parallel-web/search-mcp

Restart Gemini CLI and run /mcp to check that parallel-search is connected. The extension uses Streamable HTTP and requires no Parallel account or API key. If you already configured parallel-search manually, that configuration takes precedence over the extension. See the extension guide for installation and update options.

Hermes Agent

Hermes already includes Parallel in its native web providers. Choose Parallel Free (keyless) in hermes tools to use that integration.

This optional package adds a direct connection to Parallel's MCP tools and the bundled setup skill. It leaves Hermes's native web-provider selection unchanged. It's available in the Hermes plugin catalog. Install and enable it with:

hermes plugins install parallel-search
hermes plugins enable parallel-search

Start a new session so Hermes loads the enabled package. Ask Hermes to use the Parallel MCP web_search tool to find official documentation, then web_fetch to read a page. Use skills_list to discover the namespaced parallel-search-setup skill. No Parallel account, API key, or CLI is required.

Use either this package or a manually configured connection to https://search.parallel.ai/mcp to avoid duplicate MCP tools. Disable or remove the package with hermes plugins disable parallel-search or hermes plugins remove parallel-search. Update through hermes plugins update parallel-search; catalog updates use reviewed commit pins.

OpenCode

Merge this into your project's opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "parallel-search": {
      "type": "remote",
      "url": "https://search.parallel.ai/mcp",
      "oauth": false
    }
  }
}

Restart OpenCode and run opencode mcp list to check the connection. See the OpenCode MCP guide for details.

OpenClaw

Follow the OpenClaw setup guide to install the Parallel plugin, then set tools.web.search.provider to parallel-free. No Parallel account or API key is needed.

Cursor, VS Code, and other clients

Use this remote MCP server URL:

https://search.parallel.ai/mcp

Select HTTP or Streamable HTTP if your client asks for a transport. Follow the client-specific instructions for the right settings file and format.

Try it

Ask your agent:

Use Parallel to find the official FastAPI docs for response models. Show me a small example and link to the source.

Or try:

Use Parallel to check the latest stable Node.js release and its release notes. Cite the official sources.

Use Parallel to read https://modelcontextprotocol.io/introduction and explain how MCP connects an agent to tools.

Your agent can use two tools:

ToolWhat it does
web_searchSearches the web and returns relevant excerpts with source links.
web_fetchReads specific URLs and returns page content for your agent to use.

Search results often contain enough information to answer. Your agent can fetch a page when it needs more detail.

Free access and higher limits

This plugin is anonymous by design: both tools work with no account, API key, or login, subject to free-tier rate limits. The free tier is intended for personal agents, hobby projects, and exploration.

For higher limits, usage analytics, and production workloads, create an account at platform.parallel.ai and connect the Parallel Search connector, which signs in with that account. Authenticated usage is billed under your account's pricing and limits — see parallel.ai for pricing. Don't add an API key or point this plugin at an authenticated endpoint; the connector handles sign-in natively.

Both tiers include the same two tools. Authenticated connections also let you configure search behavior, including search modes, result counts, and domain filters. Anonymous requests ignore these settings.

Privacy and terms

Parallel hosts the service. Search queries and requested URLs are sent to Parallel and handled under the Customer Terms and Privacy Policy. The server only receives what the tools send it — search queries and the URLs you ask it to fetch — and does not read your conversation, files, or other context.

Troubleshooting

  • Tools aren't showing up: start a new agent session and check its MCP connection list. Confirm the URL is https://search.parallel.ai/mcp.
  • Asked to log in to Parallel: check the URL. /mcp supports free access; /mcp-oauth requires authentication. Approving an MCP connection in your client is separate from logging in to Parallel.
  • Hit a rate limit: wait before retrying, or move to an authenticated Parallel account via the Parallel Search connector for higher limits.
  • Still stuck: check the setup guide or open an issue. Include your client, version, and error message. Leave out API keys and other credentials.

Support and security

  • Questions and bugs: open an issue with your client, version, and error message (leave out API keys and other credentials), or see the documentation.
  • Product support: contact Parallel at support@parallel.ai.
  • Security concerns: report privately to support@parallel.ai — see SECURITY.md. Please don't file security reports as public issues.

About this repo

This is the official GitHub home for Parallel's hosted Search MCP. It contains this guide and Codex, Cursor, Gemini CLI, and Hermes packages. Codex and Cursor share .mcp.json; Hermes uses the portable Agent Plugins v1 plugin.json and mcp.json files and loads the setup skill (skills/parallel-search-setup/SKILL.md) on demand for connection checks, usage guidance, troubleshooting, and higher-limit options. You can suggest documentation fixes and examples here. The search service runs on Parallel's infrastructure; cloning this repo gives you the documentation and configuration files.

For CLI-based search, research, and enrichment, see the separate Parallel CLI skills plugin.

The files in this repo are MIT licensed. Hosted service usage is covered by the Customer Terms linked above.

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Categories
AI & LLM ToolsSearch & Web Crawling
Registryactive
TransportHTTP
UpdatedJan 9, 2026
View on GitHub

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