Parallel Search provides AI agents with real-time web search and research capabilities, returning relevant, source-backed results from across the internet. Search multiple queries, retrieve and extract webpage content, find specific information across sources, and gather structured evidence for deeper research and analysis.
The fastmcp library is a Python client for MCP servers. Use it to call MewCP tools from your own scripts and services.
This guide connects Parallel Search MCP to Python through MewCP, so Python can use Parallel Search MCP's 2 tools, like web_search and web_fetch.
Parallel Search MCP needs no credentials, but every call still runs through MewCP: scoped, checked and logged like any other.
More apps
Crawl websites and extract clean structured data optimized for AI applications and automation. Collect content from the web at scale while maintaining high-quality outputs.
Extract structured data from websites and public web pages. Fetch content, parse information and transform online data into formats that can be consumed by applications, agents and workflows.
TinyFish MCP for running AI-driven browser automation tasks, deep web research, persistent browser login profiles, remote CDP browser sessions, and agentic wallet payments.
Scrape, extract, and understand the web, plus identify any company from a domain, email, name, or transaction string. This MCP server exposes Context.dev's API for web scraping in multiple formats (raw HTML, Markdown, screenshots, images, sitemaps, and web search), schema-driven structured data extraction, design-system extraction (styleguides and fonts), and brand intelligence lookups. It's built for AI agents that read, summarize, or monitor web content, for enriching CRM, KYC, or fintech pipelines with company and brand data, and for extracting a competitor's design system or structured product and pricing data at scale.
Other clients
Parallel Search MCP works the same way in every client below. Pick yours for its own guide.
Free to start.
Scope access to the exact Parallel Search MCP tools and accounts you allow, work and personal side by side. Anything outside that scope is refused at call time, even when the model asks for it.
Each call is logged: what ran, on which account, and whether it worked. Failures come back structured, so the agent knows whether to retry instead of guessing.
Every app sits behind one secure MCP URL, and there is no server for you to deploy or keep running. Add an integration in the dashboard and it is live on the next call, with no config to change and no new credentials to wire up. Tools load only when a task needs them, so your agent stays fast and focused however many apps you connect.
SDK clients sign in with an API key. Create one in your dashboard and replace API_KEY in the code below.
SDK clients run without a browser, so they sign in with your API key.
Purpose: Perform web searches and return LLM-friendly results, including excerpts that are usually sufficient to answer directly without a follow-up fetch. Ideal Use Cases: - Answering questions that require fresh or current information - Research, comparison, documentation, and troubleshooting questions - Broad tasks where multiple `search_queries` can be issued in a single call For multiple independent objectives, make one web_search call per objective concurrently. Put related query variations for the same objective in a single call's search_queries list.
Purpose: Fetch and extract relevant content from specific web URLs. Use only when web_search excerpts are insufficient for the task at hand. Ideal Use Cases: - The user asked about a specific URL or page - You need exact wording or quotes that excerpts may have truncated - You need full-page analysis (long article, document, or page structure) - web_search excerpts are conflicting or clearly insufficient to answer For multiple independent extraction objectives, make separate web_fetch calls concurrently. Put related URLs that share one objective in a single call.
