Hyperresearch Answers
Provides optional LangChain packages for Python (langchain-hyperresearch) and JavaScript (@hyperresearch/langchain) that expose the same 'ask' and 'search' tools to agent code via the REST API with an API key.
Provides an optional Vercel AI SDK package (@hyperresearch/ai-sdk) that gives agent code the same 'ask' and 'search' tools as the MCP server, backed by the REST API with an API key.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Hyperresearch AnswersWhat was NVIDIA's data center revenue in fiscal 2024? Cite the filing."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Hyperresearch Answers (MCP)
Every web search provider has gaps. Each one indexes different pages, misses different sources, and none of them reach the structured data behind most real questions.
Hyperresearch Answers layers four web search engines with about 45 structured sources, including company filings, government statistics, public registers, scholarly indexes and books. Your agent gets one search tool with the gaps filled in.
Every answer is checked word for word against quotes from the pages it cites. If the sources don't back it up, it says not_found instead of guessing. Your agent gets the answer and its proof, not whole web pages, so its context stays on your work.
Tools
ask: one question, one verified answer with its quotes and links. A hard question returns an id instead.
get_answer: collects an answer that is still being worked on, by its id. Free.
search: ranked sources with only the relevant passages, copied word for word, for when your agent should read the evidence itself.
Related MCP server: Web Research MCP
Install
Server: https://answers.hyperresearch.ai/mcp (streamable HTTP). You sign in with your Hyperresearch account the first time a tool is called.
Claude Code:
claude mcp add --transport http -s user hyperresearch-answers https://answers.hyperresearch.ai/mcpCline:
cline mcp install hyperresearch-answers --transport http https://answers.hyperresearch.ai/mcpVS Code (GitHub Copilot agent mode):
code --add-mcp '{"name":"hyperresearch-answers","type":"http","url":"https://answers.hyperresearch.ai/mcp"}'Any MCP client (mcp.json):
{
"mcpServers": {
"hyperresearch-answers": { "url": "https://answers.hyperresearch.ai/mcp" }
}
}No browser (CI)? Create an API key at https://console.hyperresearch.ai/#/keys and send it as Authorization: Bearer <YOUR_API_KEY>.
Pricing
Calls are paid from prepaid credit in your Hyperresearch account. Every result shows what it cost and the balance left. New accounts get $5 of free credit, no card. Prices: https://hyperresearch.ai/answers
SDKs
Optional packages that give your own agent code the same ask and search tools, over the REST API with an API key (not on npm or PyPI yet):
Vercel AI SDK:
@hyperresearch/ai-sdkLangChain (Python):
langchain-hyperresearchLangChain.js:
@hyperresearch/langchain
Support
support@hyperresearch.ai · https://hyperresearch.ai/answers
The server is hosted by Hyperresearch; nothing here needs to be installed or run to use it. packages/ holds the optional SDKs above.
This server cannot be deployed
Maintenance
Related MCP Connectors
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
Structured, cited, real-world data from 300+ sources and the open web for your AI agents.
Agent-native search engine with live web research optimized for AI agents.
Cited web evidence and fact checking for AI agents. $0.01 per call, no API key.
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