AnySearch provides AI search infrastructure for agents, with structured results delivered through MCP, Skill, or a REST API. It helps developers build search into automated workflows that need current, machine-readable information.
AnySearch is AI search infrastructure built for agents and other automated workflows. It is API-first and designed to return reliable, structured, machine-readable information instead of a conventional list of web links.
The product routes a query across multiple vertical data sources, merges and ranks the results, and returns output that agents can use directly through MCP, Skill, or the REST API. The company positions the service as useful where agents need information that is current, attributable, and suitable for the next execution step.
A single search request is routed to the most relevant data sources based on intent, then fused and re-ranked before results are returned. This is meant to reduce the need to manage separate search systems for different domains.
Results are scored using authority, diversity, and freshness, with ranking based on a cross-source fusion algorithm. The product also detects freshness intent and can route to real-time data when recent information is needed.
The system returns structured results instead of a plain list of links. Through MCP or Skill, outputs are formatted as compact Markdown for agents; through the REST API, responses are JSON with a consistent schema.
AnySearch supports multiple integration paths: MCP Server, Skill Plugin, and REST API. The docs note remote MCP support, a no-local-setup option, and Skill support for Python, Node.js, PowerShell, and Bash.
The docs expose a unified search endpoint at POST /v1/search with options such as max_results, tag, zone, languages, params, and output format. This allows callers to tune requests for different domains and locales.
The source lists coverage across finance, academia, code, security, business, medical and health, patents, energy, environment, agriculture, travel, gaming, and more. The FAQ also describes support for sub-domain tags such as academic.preprint and academic.search.
Build agents that need structured information to continue a workflow without manual parsing. The product is designed to return compact, machine-readable results that can be fed into the next step of execution.
Search across domain-specific sources such as finance, academia, code, or security when a general web search engine is too broad. The tag system and vertical routing are meant for targeted retrieval.
Retrieve current or time-sensitive information when freshness matters, such as recent papers, evolving code references, or real-time market-related queries. The docs say freshness intent can trigger routing to real-time data.
Integrate search into a product or internal tool through the REST API, MCP, or Skill instead of stitching together multiple source-specific systems. The API exposes a single endpoint and configurable request parameters.
Use a free tier or anonymous access for early testing, then move to authenticated usage as requirements grow. The pricing and docs both indicate a path from quick setup to paid usage.
AnySearch is search infrastructure built for AI agents. It routes a query across multiple vertical data sources and returns structured results that agents can use directly.
The product is designed for agent workflows rather than human link browsing. It returns structured outputs, supports MCP and Skill, and can also be accessed through a REST API.
The docs describe two agent-oriented options with the same capabilities: an MCP Server that can be configured remotely, and a Skill Plugin that runs inside the agent. The REST API at /v1/search is also available for direct integration.
Yes. The pricing page shows a Free tier at $0/month, a Professional tier marked coming soon, and an Enterprise plan with contact-sales pricing. The docs also mention a free anonymous tier and free quota out of the box.
The FAQ says search queries are not stored, searches are not used for training, and content is encrypted in transit and processed without retaining search data. The docs also note that anonymous requests are rate-limited per IP and count toward the daily free quota.
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