> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ref.tools/llms.txt
> Use this file to discover all available pages before exploring further.

# Ref vs Context7

> Key differences between the popular documentation search MCP servers.

## Snapshot

|                             | Context7                           | Ref                                        |
| --------------------------- | ---------------------------------- | ------------------------------------------ |
| Approach                    | Batch retrieval                    | Iterative search + read                    |
| Content                     | Code snippets                      | Any content type (source of truth)         |
| Token optimization          | Consistent \~3k tokens/query       | Adaptive 500-5k tokens/query               |
| Tools                       | `resolve-library-id`, `query-docs` | `ref_search_documentation`, `ref_read_url` |
| Scrape any URL on the fly   | No                                 | Yes                                        |
| Private repos               | Paid add-on                        | Included                                   |
| PDF & file upload           | No                                 | Yes                                        |
| Repo indexing               | Paid                               | Free                                       |
| Prompt injection protection | In-house                           | [Centure.ai](https://centure.ai/)          |
| Paid plan                   | \$10/mo for 500 queries            | \$9/mo for 1,000 queries                   |

## Search Philosophy

Both Context7 and Ref now use stateful sessions to optimize token usage and avoid duplicate results. Where they differ is in search patterns and feature depth.

### Context7's Approach

Context7 asks your agent to pick a library and then query that library's docs.

**Strengths:**

* Pre-processed code snippets
* One of the most popular MCP servers showing why MCP is valuable

**Limitations:**

* Batch retrieval doesn't match iterative agent/human search patterns
* Limited to snippets from public documentation—no private repos, PDFs, or file uploads without paid upgrades

### Ref's Approach

Ref uses **agentic search with MCP sessions**: provides search() and read() tools, allowing agents to:

1. Issue queries and get result overviews
2. Selectively read only relevant documents
3. Iterate efficiently with session state

Ref emphasizes **source of truth** access so agents can read any content from docs (explanations, warnings, prose, code), not just pre-extracted code snippets. This prevents information loss from pre-processing while still returning only the relevant chunks needed.

**Session-powered improvements:**

* **Adaptive token usage** - agents choose which pages to read, so simple queries return only what's needed while complex queries can dig deeper
* **Never return same link twice** - agents can access prior results from context
* **On-the-fly extraction** - automatically filter large pages (e.g., 90K token Figma docs → 5K relevant tokens)
* **Pre-fetching** - results are cached for faster reads

## Why Token Efficiency Matters

Both servers optimize for **token usage** because:

* Tokens cost money
* [Context rot](https://research.trychroma.com/context-rot): irrelevant tokens degrade output quality
* Agents build context over multiple searches, so session-level metrics matter more than single-query precision

## Why Ref Wins

**2x the value:** \$9/month gets you 1,000 queries on Ref vs. 500 queries for \$10/month on Context7. That's more than double the queries per dollar.

**Agent-controlled retrieval:** Ref lets your agent decide which pages to read, adapting token usage to the task at hand. Context7 returns fixed batches—the agent only picks a library, then gets whatever the system decides to send back.

**Source of truth access:** Ref can retrieve any content type from documentation—explanatory text, warnings, prose, and code—returning only the relevant chunks needed. Context7 limits results to pre-extracted code snippets, which can miss important context and explanations.

**More sources, no extra cost:** Ref includes private GitHub repos, PDF indexing, and file uploads in the base plan. Context7 charges extra for private repos and doesn't support PDFs or file uploads at all.

**Free repo indexing:** Index your own repositories at no additional cost. Context7 charges for this feature.

**Matches how agents work:** Ref's search + read tools align with how frontier models are trained. OpenAI explicitly requires this pattern for Deep Research integration, signaling this is the future of agentic search.

**Prompt injection protection:** Ref uses [Centure.ai](https://centure.ai/) to detect and block prompt injection attacks in real-time. When your agent scrapes external websites or processes user-uploaded content, Centure's multi-modal analysis protects against malicious instructions embedded in text, images, or other data sources.

**Enterprise-ready:** Built-in GitHub, PDF, and Markdown indexing with team RBAC, no custom pipelines required.

## Learn more

Learn more about [how Ref evaluates agentic search](https://ref.tools/blog/how-make-search-good) and [how Ref leverages advanced MCP features](https://ref.tools/blog/how-does-ref-mcp) from the blog.