Genuine question: What's driving all the excitement around Context7?

From what I can tell, it's an MCP server that fetches documentation and dumps it into your LLM's context. The pitch is that it solves "outdated training data" problems.

But here's what I don't get:

For 90% of use cases, Claude Sonnet already knows the docs cold. React? TypeScript? Next.js? Tailwind? The model was trained on these. It doesn't need the entire React docs re-explained to it. That's just burning tokens.

For the 10% where you actually need current docs (brand new releases, niche packages, internal tools), wouldn't a targeted web_fetch or curl be better? You get exactly the page you need, not a massive documentation dump. It's more precise, uses fewer tokens, and you control what goes into context.

I see people installing Context7 and then asking it about React hooks or Express middleware. Things that are absolutely baked into the model's training. It feels like installing a GPS to explain directions to a cab driver.

Am I completely off base here? What am I missing about why this is everywhere suddenly?

Edit: Did some digging into how Context7 actually works.

It's more sophisticated than I initially thought, but it still doesn't solve the core problem:

How it works:

• Context7 doesn't do live web fetches. It queries their proprietary backend API that serves pre crawled documentation

• They crawl 33k+ libraries on a 10-15 day rolling schedule, pre-process everything, and cache it

• When you query, you get 5,000-10,000 tokens of ranked documentation snippets

• Ranking system prioritizes: code examples > prose, API signatures > descriptions

• You can filter by topic (e.g., "routing", "authentication")

You're getting documentation that Context7 crawled up to 15 days ago from their database. You could just web_fetch the actual docs yourself and get current information directly from the source, without:

• Depending on Context7's infrastructure and update schedule

• Burning 5-10k tokens on pre-selected chunks when the model already knows the library

• Rate limits from their API

For mature, well documented frameworks like React, Next.js, or TypeScript that are baked into the training data, this is still redundant. For the 10% of cases where you need current docs (new releases, niche packages), web_fetch on the specific page you need is more precise, more current, and uses fewer tokens.

TL;DR: Context7 is a documentation caching layer with smart ranking. But for libraries Claude already knows, it's overkill. For the cases where you actually need current docs, web_fetch is more direct.

I am looking for an MCP server my copilot can reference to get up to date code documentation.

I am tired of having to tell my agent to fetch a certain website to ensure up to date best practices for a given dependency (because its knowledge cut off is ~6 months old.

I have never used or heard of Context7 until I tried looking for a tool like this, so I am a bit skeptical. I wanted to get your opinions on it. Have you used it? Is it helpful or not?

From their Github Repo:

❌ Without Context7

LLMs rely on outdated or generic information about the libraries you use. You get:

• ❌ Code examples are outdated and based on year-old training data

• ❌ Hallucinated APIs don't even exist

• ❌ Generic answers for old package versions

✅ With Context7

Context7 MCP pulls up-to-date, version-specific documentation and code examples straight from the source — and places them directly into your prompt.

Context7 fetches up-to-date code examples and documentation right into your LLM's context.

• 1️⃣ Write your prompt naturally

• 2️⃣ Tell the LLM to use context7

• 3️⃣ Get working code answers

No tab-switching, no hallucinated APIs that don't exist, no outdated code generations.

I have tried it with VS Code + Cline as well as Windsurf, using GPT-4.1-mini as a base model and it works like a charm.

• Context7 website

• Github Repo

YT Tutorials on how to use with Cline or Windsurf:

• Context7: The New MCP Server That Will CHANGE AI Coding (FREE)

• This is Hands Down the BEST MCP Server for AI Coding Assistants

Checked their website, and it looks like a user submitted unmoderated mess of junk. Tried their MCP server and it keeps erroring out with:

⎿ Documentation not found or not finalized for this library. This might have happened because you used an invalid Context7-compatible library ID. To get a valid Context7-compatible library ID, use the 'resolve-library-id' with the package name you wish to retrieve documentation for.

Does this after calling resolve and resolving a proper ID, and happens on everything.

But I guess the bigger concern is, if you want high quality docs for specific things like eg. the OpenAI image API in a single markdown doc for CC to reference, how do you do it? Thanks.

Hey devs! 👋

I've been working on a RAG-based solution that functions similarly to Context7 but at a significantly lower cost. After some rigorous testing, I thought I'd share my findings with the community.

TL;DR: This implementation costs roughly half as much as Context7 while producing code of comparable quality.

The Tests

I ran three coding challenges using Gemini-2.5-pro (set to temp=0) with both Context7 and Custom MCP:

1. Creating a Next.js page with API data fetching

2. Building a FastAPI endpoint for streaming large files

3. Developing a FastAPI WebSockets app with Redis pub/sub

I implemented a simple prompt suffix system:

• For Context7: "use context7. Max tokens: 500"

• For MCP: "use documentation"

The Results

Cost comparison: https://imgur.com/a/lGFgMHz

• Average cost savings: ~40%

• Next.js Test: Context7 ($0.056) vs Custom MCP ($0.023)

• FastAPI Streaming Test: Context7 ($0.044) vs Custom MCP ($0.031)

• WebSockets/Redis Test: Context7 ($0.052) vs Custom MCP ($0.040)

Both tools generated fully functional code that implemented all requirements, but the Custom MCP server did it at consistently lower costs.

Why This Matters

If you're building AI-powered coding tools or using them extensively in your workflow, these cost savings add up fast.

For teams making thousands of API calls daily, you could be saving hundreds or thousands of dollars monthly.

What's Next

I encourage you to try the MCP server yourself and share your feedback. Currently it supports the latest versions of Expo, FastAPI, and NextJS:

{
  "documentation": {
    "url": "https://doc-mcp.fly.dev/mcp/"
  }
}

If there's enough interest, I'll add more libraries.

Would love to hear your thoughts and questions about the approach!

In one of my previous posts here, somebody asked how Context7 really works. It made me realize a lot of us use it as a black box, not knowing what happens under the hood.

I was curious too so I dug to put the pieces together.

Here's a summary of how the Context7 MCP works:

1. Understand that MCPs just expose tool descriptions (function calling)

2. Those tool descriptions influence how Claude Code calls Context7

3. Claude Code sends a best-guess keyword of the library name to the Context7 MCP's resolve-library-id tool

4. Context7 returns a list of possible library matches

5. Claude Code makes a best guess selection of the library based on some criteria and sends a keyword of what topic you're trying to get docs on to Context7 MCP's get-library-docs tool

6. Context7 returns a list of possible code snippets/docs regarding about said topic

7. Claude Code calls Context7 MCP's two tools as many times as necessary to achieve the intended goal

8. Claude Code synthesizes the output from get-library-docs tool picking out what it needs

It's easy to see this all playing out if you start Claude Code with claude --debug --verbose.

Based on a prompt such as Show me how I could use "Cloudflare Durable Objects" with "Cloudflare D1 (llmstxt)" together. Use context7. Here's what a call to resolve-library-id looks like. Notice how the output comes with additional instructions.

context7 - resolve-library-id (MCP)(libraryName: "Cloudflare Durable Objects")
  ⎿ Available Libraries (top matches):

    Each result includes:
    - Library ID: Context7-compatible identifier (format: /org/project)
    - Name: Library or package name
    - Description: Short summary
    - Code Snippets: Number of available code examples
    - Trust Score: Authority indicator
    - Versions: List of versions if available. Use one of those versions if and only if the user explicitly provides a version in their query.

    For best results, select libraries based on name match, trust score, snippet coverage, and relevance to your use case.

    ----------

    - Title: Cloudflare Durable Objects
    - Context7-compatible library ID: /llmstxt/developers_cloudflare-durable-objects-llms-full.txt
    - Description: Durable Objects provide a building block for stateful applications and distributed systems, enabling AI agents, collaborative applications, and real-time
    interactions without managing infrastructure.
    - Code Snippets: 3906
    - Trust Score: 8
    ----------
    - Title: y-durableobjects
    - Context7-compatible library ID: /napolab/y-durableobjects
    - Description: Facilitates real-time collaboration in Cloudflare Workers using Yjs and Durable Objects for scalable, decentralized editing features.
    - Code Snippets: 27
    - Trust Score: 8.4
    ----------
    - Title: Sandbox SDK
    - Context7-compatible library ID: /cloudflare/sandbox-sdk
    - Description: Run isolated code environments on Cloudflare's edge network using Durable Objects and Cloudflare Containers, enabling command execution, file management, and
    service exposure via public URLs.
    - Code Snippets: 12
    - Trust Score: 9.3
    ----------
...

This is how the get-library-docs tool call looks like:

context7 - get-library-docs (MCP)(context7CompatibleLibraryID: "/llmstxt/developers_cloudflare-durable-objects-llms-full.txt", topic: "database integration patterns", tokens:
                                   5000)
  ⎿ ========================
    CODE SNIPPETS
    ========================
    TITLE: D1 Compatible PRAGMA Statements
    DESCRIPTION: Documentation for PRAGMA statements compatible with Cloudflare D1, which is based on SQLite. This covers various aspects of database introspection, integrity
    checking, and configuration.

    SOURCE: https://developers.cloudflare.com/d1/sql-api/sql-statements/

    LANGUAGE: APIDOC
    CODE:
    ```
    PRAGMA Statements:

    These PRAGMA statements are compatible with Cloudflare D1, providing insights and control over the database.

    Table Information:
      - PRAGMA table_list: Lists all tables in the database.
      - PRAGMA table_info("TABLE_NAME"): Describes the schema of a specific table.
      - PRAGMA table_xinfo("TABLE_NAME"): Provides extended information about table columns.

    Index Information:
      - PRAGMA index_list("TABLE_NAME"): Lists all indexes for a specific table.
      - PRAGMA index_info(INDEX_NAME): Describes a specific index.
      - PRAGMA index_xinfo("INDEX_NAME"): Provides extended information about index columns.

    Integrity and Constraints:
      - PRAGMA quick_check: Performs a quick integrity check on the database.
      - PRAGMA foreign_key_check: Checks for violations of foreign key constraints.
      - PRAGMA foreign_key_list("TABLE_NAME"): Lists foreign key constraints for a table.
      - PRAGMA case_sensitive_like = (on|off): Controls case sensitivity for LIKE operations.
      - PRAGMA ignore_check_constraints = (on|off): Ignores CHECK constraints during operations.
      - PRAGMA foreign_keys = (on|off): Enables or disables foreign key enforcement.
      - PRAGMA defer_foreign_keys = (on|off): Controls deferred foreign key constraint checking.

    Other:
      - PRAGMA legacy_alter_table = (on|off): Enables or disables legacy ALTER TABLE syntax.
      - PRAGMA recursive_triggers = (on|off): Controls recursive execution of triggers.
      - PRAGMA reverse_unordered_selects = (on|off): Affects the order of results for unordered SELECTs.
      - PRAGMA optimize: Optimizes the database schema (may not be fully supported or have different behavior).

    Querying sqlite_master:
      - SELECT name FROM sqlite_master WHERE type='table';: Example query to list all tables.

    Search with LIKE:
      - LIKE operator can be used for pattern matching in WHERE clauses.
    ```

    ----------------------------------------

    TITLE: Rust Worker D1 Database Integration Example
    DESCRIPTION: Example of a Rust worker handling a GET request to fetch data from a D1 database. It demonstrates preparing a SQL statement, binding parameters, executing the
    query, and returning the result as JSON.

    SOURCE: https://github.com/cloudflare/workers-rs#_snippet_75

    LANGUAGE: rust
    CODE:
    ```
    use worker::*;
    use serde::Deserialize;

    #[derive(Deserialize)]
    struct Thing {
    thing_id: String,
    desc: String,
    num: u32,
    }

    #[event(fetch, respond_with_errors)]
    pub async fn main(request: Request, env: Env, _ctx: Context) -> Result<Response> {
    Router::new()
    .get_async("/:id", |_, ctx| async move {
    let id = ctx.param("id").unwrap()?;
    let d1 = ctx.env.d1("things-db")?;
    let statement = d1.prepare("SELECT * FROM things WHERE thing_id = ?1");
    let query = statement.bind(&[id])?;
    let result = query.first::<Thing>(None).await?;
    match result {
    Some(thing) => Response::from_json(&thing),
    None => Response::error("Not found", 404),
    }
    })
    .run(request, env)
    .await
    }
    ```

    ----------------------------------------
...

How to see the Context7 MCP's tool description

Claude Code actually makes it really easy to see the tool descriptions for all of your enabled MCPs. Just issue the /mcp slash command, select Context7 and keep drilling down until you get to the tool you're interested in. Eventually you'll see the tool description.

Super important: to reiterate, the description and parameters in these tools are what influences when and how Claude Code calls them.

Saving on Tokens and Latency

Each call to resolve-library-id can return about 7000 tokens. And every call to get-library-docs can be between 4000 to 10000 tokens. If you already know exactly which Context7 library ID you want to use to query, you can already save a decent amount of tokens and a big plus there's less latency.

To do that go to context7.com, search for your library, make sure it's the one you need (sometimes there's similar ones), and copy the link to the detail page.

The URL looks like https://context7.com/llmstxt/developers_cloudflare_com-d1-llms-full.txt

If you remove the domain you get the library ID like so /llmstxt/developers_cloudflare_com-d1-llms-full.txt.

Now you can use that library ID in your prompt.

Here's how that could look like:

Show me how I could use "Cloudflare Durable Objects" (use library id /llmstxt/developers_cloudflare-durable-objects-llms-full.txt)
  with "Cloudflare D1 (llmstxt)" (use library id /llmstxt/developers_cloudflare_com-d1-llms-full.txt) together. Use context7.

Now it completely skips 2 calls to resolve-library-id.

----

Hopefully this deep dive helps you to see how all of the pieces fit together.

——

UPDATE: I really enjoy writing these posts and appreciate every upvote and comment. Thank you!

Trying very hard to grow my very small YouTube channel. If you'd like to support me, please subscribe here https://www.youtube.com/@jorgecolonconsulting.

Got several Claude Code ideas to talk about for future videos inspired by the comments here.

Just this week I came across the Context7 mcp and have since been using it together with Copilot with great results. Context7 is a collection of documentation that is (it seems) synced directly with GitHub. And if your framework is not in the collection, you can add it by simply pasting in the repo url (provided that it has documentation in there).

I added this mcp to my user settings, along with a custom user prompt that tells Copilot to use it. This way you don't have to set it up for every project (you could though if you wanted).

settings.json

{
  "mcp": {
    "servers": {
      "Context7": {
        "type": "stdio",
        "command": "npx",
        "args": [
          "-y",
          "@upstash/context7-mcp@latest"
        ]
      }
    },
  },
  "github.copilot.chat.codeGeneration.instructions": [      
    {
      "text": "When answering questions about frameworks, libraries, or APIs, use Context7 to retrieve current documentation rather than relying on training data.",      
    }
  ],
}

So far mcp's seem to only work in agent mode though, but I found you can also ask agent mode questions as long as you tell it to not edit your code. That said I rarely feel the need to switch from agent mode to ask mode these days.

Thought I'd share!

Dear community,

Yesterday I made a test for next.js with Gemini CLI using context7 for latest updates, but some s*it happened and the 1000 free daily request limit has suddenly reached the end quickly. Actually because of the token size of any documentation and possible multiple requests by the Agent, I'm a bit worried about using it efficiently.

Therefore my question shall be how to make sure that the agent will check the documentation through context7 only for the needed parts?

I found this MCP tool recently: https://smithery.ai/server/@upstash/context7-mcp
Context 7, a software document retrieval tool and combined it with chain of thought reasoning using https://smithery.ai/server/@smithery-ai/server-sequential-thinking

Here's the prompt I used, it was rather helpful in improving accuracy and the overall experience:

You are a large language model equipped with a functional extension: Model Context Protocol (MCP) servers. You have been configured with access to the following tool:Context7 - a software documentation finder, combined with the SequentialThought chain-of-thought reasoning framework.

Tool Descriptions:

• resolve-library-idRequired first step: Resolves a general package name into a Context7-compatible library ID. This must be called before using get-library-docs to retrieve valid documentation.

• get-library-docsFetches up-to-date documentation for a library. You must first call resolve-library-id to obtain the exact Context7-compatible library ID.

• sequentialthinkingEnables chain-of-thought reasoning to analyze and respond to user queries.

Your task:

You will extensively use these tools when users ask questions about how a software package works. Your responses should follow this structured approach:

1. Analyze the user’s request to identify the type of query. Queries may be:

   • Creative: e.g., proposing an idea using a package and how it would work.

   • Technical: e.g., asking about a specific part of the documentation.

   • Error debugging: e.g., encountering an error and searching for a fix in the documentation.

2. Use SequentialThought to determine the query type.

3. For each query type, follow these steps:

   1. Generate your own idea or response based on the request.

   2. Find relevant documentation using Context7 to support your response and reference it.

   3. Reflect on the documentation and your response to ensure quality and correctness.

RESULTS:
I asked for a LangChain prompt chain system using MCP servers, and it gave me a very accurate response with examples straight from the docs!

Hello all,

I am working with Context 7 using the desktop app, and I must say it helps a lot — the context of the answers is much more to the point.
Now, I would like to expand to more MCPs that can assist me with coding and performing deep research while coding, particularly in related open-source projects, documentation, and code examples.

I do not want them to change my files, only provide output — I will handle the implementation myself. So, experts, please:

1. Suggest more coding-related MCPs that help you.

2. Provide good prompt suggestions for combining MCP pipelines.

Has anyone managed to use Context7 MCP with AI Assistant? I've not really used MCP servers before, but Context7 looks really useful. I know MCP is still in Beta, but I cant' get it to use the Context7 server. I've copied in the JSON, and when I select Apply, there is a spinner, then a stop icon. When hover over, it tells me it has 'not started'. I can run the command `npx -y u/upstash/context7-mcp` in my terminal and it tells me it has connected.

Hey everyone,

I'm currently utilizing Roo with the RooFlow memory bank and I am interested in integrating the Context7 MCP to enhance my workflow.

Has anyone here successfully combined Context7 MCP with a memory bank solution like RooFlow? I'm particularly curious about:

• Whether any adjustments to system prompts are necessary to guide Roo in effectively utilizing the MCP.

• Best practices for ensuring seamless interaction between Roo and Context7.

Any insights, experiences, or resources you could share would be greatly appreciated!

Went with full-on dev mode this week for the list of MCPs added to the platform.

This week’s focus was to allow developers to install these MCPs to their IDEs and making them more useful.

My favourite of all is Context7, It is a really special MCP built just for finding and fetching documentations for framework. I was using it personally even before this, and I think every dev will love it in their IDEs.

im having trouble when trying to acceess to context7 mcp tools, i've already created the json structure and also tried the manual installation with npx -y, it downloaded and turned on with expected output proving it is "running" on stdio. anyone else experiencing he same problems or know any solution? it simply doesnt appear on roo and i cant know exactly why.

thanks

I started using Context7 for my NextJS App development a week back and I love it.

Here's how it helps you code more accurately and with fewer errors. Context7 pulls up-to-date version specific documentation for any library or framework you are working on, and passes that to your coding agent / co-pilot so they have the exact information needed to make your code perfect.

You can install it manually from here - https://github.com/upstash/context7

Or use it for free via toolrouter along side MCPs like linear, github, trello & more,

(I am using toolrouter because I developed it)

1. Go to toolrouter.ai

2. Create a stack with whatever name you like

3. Add Context7 to the server list (no credentials required), and add any other MCPs you like.

4. Go to connect tab and create an SSE Credentials

5. Copy config for the IDE you are using & paste it.

TL;DR
Constantly seeing Context7 praised as an effective MCP, but it seems to have some severe limitations if the required documentation is not precisely structured as a "small paragraph + code snippet" (see example below)

It seems like every two days I see Context7 being mentioned on this reddit and others as one of the most comonly used MCP servers, functioning well for coding agents. It seems like for certain documentation it can behave extremely bad though.

As an example, I was recently looking into using a CLI code agent to upgrade a Laravel project and wanted to see if Context7 could be useful. Unfortunately, it seems that the documentation returned by Context7 is completely useless, at least for this specific case.

The official Laravel documentation has a neat, separate page covering the steps for upgrading a project from one version to the next. They even provide a button to copy the entire page in markdown format.

https://laravel.com/docs/10.x/upgrade

In fact, the exact page can be found back in the github repo for that Laravel documentation:
https://github.com/laravel/docs/blob/10.x/upgrade.md
(I'm fairly sure that this is also the exact repository scraped by Context7 for the Laravel 10 docs)

However, we can check exactly what Context7 returns when searching for this with reasonable key words (simply "upgrade guide"):
https://context7.com/websites/laravel_10_x?topic=upgrade+guide
But rather than returning some clean, relevant context, it very clearly provides essentially the same bit of markdown docs and useless code snippet over and over and over again.
I suspect that, despite Context7 scraping that exact github repo and literally having the full upgrade guide available in markdown, we get these results due to the way the documentation is stored and how Context7 focuses on code snippets (rather than more complete docs).

Am I just misunderstanding something, or is this a coincidence where Context7 fails to be useful in this specific case?
It feels like the way the documentation is set up and stored, a very specific structure is needed for Context7 to be useful.

Edit:
I noticed that the Laravel 10 docs that Context7 has seems to be based on the Laravel docs website itself rather than the github repo. Wanted to test if there is more success when adding the docs through the repo but the 10.x docs are only available through a branch, which Context7 sees as an invalid github link. So maybe a feature to add to Context7 (allowing to add docs through a github repo branch-specific link)?

Can someone(s) help me understand the best way to utilize the Context7 MCP? I have it setup in Claude Code and can call each tool individually (get library id, get docs from library id). However, where do these docs get stored? Do they remain JUST in memory and for how long? Is there any persistence across sessions?

I've seen some people talk about putting reference to Context7 in their CLAUDE.md file. If I do that, will it leverage Context7 for all prompts? Only those with a specific language mentioned? Only those where I say "use Context7"?

Basically, its great when I take it step by step, but how do I make this more seamless and "integrated" into a workflow. Thoughts?