Octen delivers real-time web search and research retrieval for AI agents, with concurrent multi-query searches, webpage extraction to Markdown, and text or vision embeddings. Discover fresh information, retrieve relevant source content, and generate vectors for semantic search and knowledge-base indexing.
Codex is OpenAI's coding agent for the terminal. It reads MCP servers from its config and signs in to remote ones with OAuth.
This guide connects Octen MCP to Codex through MewCP, so Codex can use Octen MCP's 6 tools, like search, news_search and broad_search, without your Octen MCP credentials ever reaching the app.
Your Octen MCP API key is stored encrypted and injected only at call time. Your agent never holds it, so it can never leak it, log it or paste it into a prompt.
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
Octen MCP works the same way in every client below. Pick yours for its own guide.
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Scope access to the exact Octen 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.
Run this and approve access in the browser window.
Start a new chat and ask: "List the tools you can use from MewCP." You should see your connected apps.
Search the live web and return ranked results (title, url, snippet) — fast, fresh, real-time web search for one focused lookup. Set `topic` to `news` for news-focused results. Pass `highlight` to get a ranked snippet per result, or `full_content` to pull the cleaned page body inline (heavier — costs more context). Narrow with domain / text include-exclude filters, a `language` filter (ISO 639-1 codes), and a time window (published/crawled `start_time`/`end_time`, or a relative `time_range`). Set `include_images` to return image URLs per result. USE FOR a single focused lookup: one fact, one entity, one document. If the question spans several independent subtopics, load and use `broad_search` instead — a sequence of search calls is slower and gives worse coverage than one fan-out. To read a page you already have the URL for, use `extract`. keywords: web search, search the web, look up, find, check, fact, current information, latest, news, source, url, real-time
Search recent news and return ranked articles (title, url, snippet) — current events, headlines, timely reporting. This is `search` locked to `topic: news`; same options as `search` (domain / text filters, `language` filter, time window, highlight / full_content, media) except `topic`, which is fixed to news. For a single news lookup this is the right tool. For a multi-angle news question ("what shipped across the industry this month", "how are different outlets covering X"), use `broad_search` with topic=news instead of looping news_search. keywords: news search, latest news, headlines, current events, breaking news, recent, today, this week, press coverage
Search the web across many angles in one call — for comparisons, research, surveys, and questions with several distinct parts. Expands your question into multiple sub-queries and runs them concurrently. USE WHEN the question has multiple distinct parts or entities that one search cannot cover: - comparing vendors / products / pricing across many sources - literature reviews, market or landscape surveys - open-ended "what are the options for X" / "how do people solve Y" - a question that clearly decomposes into 3+ independent sub-questions - multi-angle questions about recent events ("what shipped across the industry this month") — set topic=news, do NOT loop news_search DO NOT USE for: - a single fact, entity, or document → use `search` - re-running a disappointing search → do NOT call broad_search twice; follow up with a targeted `search` or `extract` on the specific gaps - reading a page you already have the URL for → use `extract` - a straight A-vs-B comparison of two known entities → two targeted `search` calls are cheaper and more controllable COST: fans out into `max_queries` concurrent searches — roughly Nx the cost and notably higher latency than a single `search`. When in doubt, prefer `search`. QUERY: pass one natural-language question (max 500 chars). Resolve pronouns and references from the conversation first — "how does it compare to the other one" is a useless query. Do NOT pre-split into sub-queries; that is this tool's job. For broader coverage raise `max_queries` rather than calling repeatedly. Per-sub-query options (count, topic, `language` filter, domain / text filters, time window, highlight / full_content, media) match `search` and apply to every sub-query. RESULTS are grouped per sub-query and NOT deduplicated — the same URL may appear under several sub-queries. max_queries: 3-5 focused comparison (2-3 entities) | 5-10 multi-facet research | 10-20 landscape scan | 20-30 exhaustive survey For a single focused lookup use `search`; to read a specific page use `extract`. keywords: web search, search the web, look up, find information, research, compare, comparison, versus, alternatives, options, landscape, survey, market research, pricing, latest, current information, multi-part question
Read one or more web pages by URL and return clean, LLM-ready content (markdown or text). By default (no `query`) it returns each page's full content — this is what you want in almost all cases. Only pass `query` when the user explicitly asks to fetch relevance-ranked snippets for a specific topic; doing so returns highlights INSTEAD of the full body, so the content will be partial. Every result also includes a `category` (topical) and `page_structure` (typology) classification. Bare hosts like 'octen.ai' are auto-normalized to https. Cached when fresh. Pick `mode` by how hard the URLs are: ordinary sites → standard (or omit), known hard sites → advanced, mixed/unsure → auto. If a standard result failed or came back empty/skeletal, retry just those URLs with mode "advanced". Use this when you already have the URL(s). To find pages first, use `search` or `broad_search`. keywords: read page, fetch url, scrape, page content, article text, parse webpage, extract, read article, url content, open link
Find images on the web by text query OR by a reference image — returns ranked results (title, source page, dimensions, thumbnail, description, summary). In Beta; contact us to request beta access. Pass exactly one of: a text `query`, an `image_url` (a picture already on the web), or `image_data` (base64, for a picture you hold). Never more than one. Set `topic` to `design` for UI design references — each result then carries a structured style `summary` and an `html_snippet` for building/restyling frontends. Use this when the user wants pictures, photos, diagrams, screenshots, or visual references — not for general text web search. keywords: find images, image search, photos, pictures, screenshots, visual reference, diagram, icon, illustration, UI design, reference image
Find videos on the web by text query — returns ranked results (title, source page, cover image, duration, matching segment, authors, description). In Beta; contact us to request beta access. Pass a text `query`. Use this when the user wants to find videos, clips, footage, tutorials, or a specific moment within a video — not for general text web search. keywords: find videos, video search, clips, footage, youtube, tutorial video, watch, movie, video clip
