ContextFetch use cases

Coding agents

Search docs, Stack Overflow, GitHub and the open web, and hand your agent only the passages that answer its question.

Stack Overflow and GitHub are queried for technical questions
Pruned context instead of whole pages in the context window
One-line keyless MCP install for Claude Code, Cursor and Windsurf

Docs search
Code search
MCP

ContextFetch Search/v1/search$1/1k requests

runebook.dev

The asyncio Timeout Trap: Why wait_for() Doesn't Stop Tasks Instantly and How to Fix It

I'll break down the common issues and offer sample code for alternatives in a friendly way! In Python's asyncio, timeouts are handled primarily using…

↓ 57% tokens

Real response, recorded with no key

AI assistants

Ground replies in pages fetched at question time, with a quoted answer and the URLs it came from.

Tavily-compatible: tavily-python works after a base-URL change
Extractive answers quoted from the sources, no second model
Prepaid credits that never expire, no per-seat plan

Web answers
Citations
Tavily-compatible

ContextFetch Tavily-compatible search/tavily/search$1/1k requests

Explore ai assistants

webexhibits.org

answer · quoted from the sources

In the European Union, Summer Time begins and ends at 1:00 a.m. Universal Time (Greenwich Mean Time). It begins the last Sunday in March and ends the…

5 sources

Real response, recorded with no key

Research agents

Fan a question out across web, arXiv, Hacker News and Wikipedia, then read ranked highlights with their URLs and dates.

arXiv is added for academic questions
contextfetch_deep_research runs up to 3 sub-queries in parallel
Every highlight carries its URL, score and published date

Multi-source
Deep research
Citations

ContextFetch Search/v1/search$1/1k requests

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research.google

Deeper insights into retrieval augmented generation: The role of sufficient context

May 14, 2025 Cyrus Rashtchian, Research Lead, and Da-Cheng Juan, Software Engineering Manager, Google Research We introduce a new notion of…

2025-05-14 · ↓ 72% tokens

Real response, recorded with no key

RAG pipelines

Turn search results or your own URL list into clean Markdown, ranked against the question, ready to embed or prompt.

Up to 10 URLs per Contents call, 1 credit per page that loads
Highlights and pruned context scored against your query
URLs you send are never added to the shared index

Contents
Markdown
Chunk ranking

ContextFetch Contents/v1/contents$1/1k pages

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en.wikipedia.org

Retrieval-augmented generation

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating an information-retrieval mechanism that allows models to…

score 0.96

Real response, recorded with no key

	Search/v1/search	Contents/v1/contents	Reader/r/{url}	Find similar/v1/findSimilar	Tavily-compatible search/tavily/search
Base price(per 1k requests, Instant pack)	$1.00	$1.00(per 1k pages)	$1.00(per 1k pages)	$1.00	$1.00
Results or pages per request	up to 25	up to 10	1	up to 25	up to 25
Cost per additional result	$0	-	-	$0	$0
Page Markdown	included	included	included	included	included (raw_content)
Highlights and pruned context	included	included	included (?q=)	included	quoted answer, included
On the Scale pack(per 1k requests)	$0.75	$0.75(per 1k pages)	$0.75(per 1k pages)	$0.75	$0.75

Use cases

Coding agents

AI assistants

Research agents

RAG pipelines

Endpoint pricing

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