DocBreach MCP. Read and structure any API documentation in real-time.
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…and any MCP-compatible client
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DocBreach gives AI agents a crowbar for API documentation. It lets your agent discover, read, and extract structured data from any technical docs—OpenAPI, Swagger, Postman, or complex SPAs—without needing browser rendering or API keys.
What your AI agents can do
Docs.discover
Finds and lists potential documentation source URLs for any service or API you name.
Docs.extract
Reads OpenAPI, Swagger, or Postman specs to pull out structured endpoint details, optionally filtered by an API group tag.
Docs.map
Creates a complete sitemap (table of contents) for any domain so you can see the full structure of its documentation.
Your agent uses docs.discover to find potential documentation sites or APIs for any service name.
The docs.read tool fetches content from any URL (HTML, PDF, JSON, etc.) and converts it into clean, LLM-ready Markdown. It also extracts related links for you to check.
Using docs.map, your agent gets a complete table of contents (sitemap) for an entire domain, showing how all the documentation pages connect.
The docs.extract tool processes OpenAPI or Swagger files to pull out structured endpoint details and organize them by API group.
With docs.search, your agent can look for specific topics inside a known domain, provided you give it the site parameter.
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Supported MCP Clients
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DocBreach MCP Server: 5 Tools for Documentation Retrieval
These five tools let your agent discover API sources, read specific documentation pages, map entire sites, search topics, and extract structured endpoint data from specs.
019ea5f3docs.discover
Finds and lists potential documentation source URLs for any service or API you name.
019ea5f3docs.extract
Reads OpenAPI, Swagger, or Postman specs to pull out structured endpoint details, optionally filtered by an API group tag.
019ea5f3docs.map
Creates a complete sitemap (table of contents) for any domain so you can see the full structure of its documentation.
019ea5f3docs.read
Reads content from any URL—HTML, PDF, JSON, etc.—and returns clean, LLM-ready Markdown while also finding related links.
019ea5f3docs.search
Searches for specific topics within a known domain, requiring you to provide the site parameter for accuracy.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with DocBreach, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
DocBreach gives your AI agent a crowbar for API documentation. You don't have to worry about messy web pages, missing API keys, or complex browser rendering; we just get you the clean data you need. It reads and structures technical docs in real time so your agent can actually use it.
Locating Documentation Sources:
You start by figuring out where the documentation lives. You run docs.discover to find potential documentation sites or APIs for any service name you throw at it. That tool tells you exactly which URLs might hold what you're looking for.
Mapping Site Structures and Reading Content:
Once you know the domain, you need a roadmap. You use docs.map, and your agent immediately gets a complete sitemap—a full table of contents showing every single documentation page connected on that site. If you only have one URL but need to see how everything connects, this is what you run.
For specific content retrieval, you've got two tools. The docs.read tool fetches and processes the actual content from any type of URL—whether it's raw HTML, a PDF, or JSON. It converts that mess into clean Markdown that your LLM can eat up immediately, and it’ll even pull out related links for you to check next.
If you know where the documentation is but need to search through it, docs.search lets your agent look for specific topics inside a known domain; just make sure you provide the site parameter so it doesn't get confused.
Extracting Structured Endpoints:
When the docs are clean and readable, you often need structured data—the endpoints themselves. You use docs.extract to process OpenAPI, Swagger, or Postman files. This tool pulls out every structured endpoint detail and keeps it organized for you, letting you filter those results by specific API group tags if you want.
DocBreach supports reading documentation from a huge range of formats—OpenAPI specs, Swagger files, Postman Collections, sitemaps, even llms.txt. If it's technical documentation, we can read it cleanly enough for your AI agent to use. You don't need SaaS providers or browser rendering; you just point us at the docs, and we give you structured data.
How DocBreach MCP Works
- 1 Subscribe to this server on Vinkius.
- 2 No authentication is required; just pass the URL or query directly to your agent client.
- 3 Your agent invokes one of the five tools (e.g.,
docs.readordocs.map) and receives clean, actionable data.
The bottom line is you get structured, isolated documentation context that your AI agent can use immediately, without needing to scrape a browser window.
Who Is DocBreach MCP For?
Backend engineers who spend half their day sifting through READMEs. Solutions architects building multi-service integrations. ML developers whose agents need reliable external knowledge sources. If your work involves connecting disparate APIs, this is for you.
Uses docs.read to quickly grab details from a specific API page while building code, then uses docs.extract when they need endpoint lists.
Relys on docs.map and docs.discover to build context for the agent—it needs the whole site structure before it can even attempt a single query.
Uses this server to validate connectivity requirements across multiple client APIs, ensuring all required documentation sources are accessible via docs.discover.
What Changes When You Connect
- You get clean, LLM-ready Markdown directly from
docs.read. This means no messy HTML tags or boilerplate text—just the content your agent needs to run code. - Instead of guessing where documentation is, use
docs.discoverfirst. It finds service and API docs for any name, giving you a starting point before you even read a page. - Stop reading single pages in isolation. Run
docs.mapon a domain like stripe.com to get a full table of contents, showing every section available. - When working with large specs, don't scroll forever. Use
docs.extractto pull structured endpoint information directly from OpenAPI or Swagger files into usable lists. - The server handles many formats (PDFs under 5MB, JSON, YAML). You just point the agent at it; the tool figures out how to parse it.
Real-World Use Cases
Debugging a Webhook Endpoint
A developer needs to know about Stripe webhooks. First, they use docs.discover to find the correct URL. Then, they pass that URL to docs.read. The agent gets clean Markdown and also finds related links—like authentication guides—to solve the problem fast.
Mapping a New Service Integration
An architect needs to integrate with an unknown API. They run docs.map on the vendor's domain. This gives them a complete sitemap, allowing them to systematically check every section (like authentication or billing) before writing any code.
Quickly Listing Available API Calls
A backend engineer has an OpenAPI spec file. Instead of manually reading hundreds of pages, they run docs.extract and get a structured summary of all available endpoints (e.g., 'User: GET /users') organized by tag.
Finding Specific Context in Large Docs
A team member needs to know about rate limiting for AWS services. They use docs.search, making sure they specify the correct site domain, and the agent returns only pages mentioning 'rate limits' without giving them unrelated docs.
The Tradeoffs
Treating all URLs equally
Just pointing your agent at a random documentation URL expecting it to give code examples or structured data. It just gives you raw, messy HTML.
→
First, run docs.read on the specific page to get clean Markdown context. If you need structure, use docs.extract on the OpenAPI spec instead.
Overlooking the site boundary
Running a search query without specifying the domain (e.g., just 'authentication headers'). The results will be too broad or incorrect.
→
Always use docs.search and make sure you include the { site: "docs.yourcompany.com" } parameter in your call.
Skipping the overview
Jumping straight to reading a single page (docs.read) when you don't know if that page is related to authentication or setup.
→
Start with docs.map on the root domain. This gives you the full picture first, helping you navigate to the right section.
When It Fits, When It Doesn't
Use DocBreach when your task requires understanding a complex external API structure or technical manual. If you need to find out 'what's available,' use docs.discover. If you need the full scope of what exists, run docs.map. If you have a specific file format (OpenAPI/Swagger) and just want endpoint lists, use docs.extract. You should only skip using this server if your task is simple web content retrieval that doesn't involve structured API documentation.
Don't rely on it for general news articles or basic blog posts; those are better suited for standard search engines. If you need to check multiple endpoints, don't call docs.extract repeatedly—use the summary provided by your initial run to guide the next step.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DocBreach. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 5 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through docs shouldn't require 20 tabs and three copy/paste sessions.
Right now, getting documentation context for a new service means opening the main site, finding the right section in the sidebar, clicking into it, then maybe needing to check a related page—all while copying snippets of code or YAML definitions just to feed them into your agent. It's slow, and you lose track of where you were.
With DocBreach, you stop clicking through tabs. You call `docs.read` with the URL, and bam—you get clean Markdown containing all the necessary context in one shot. The server even pulls out 'Related Documentation Links,' so you know exactly what to check next.
DocBreach: Get structured API data using `docs.extract`.
Manually reviewing an OpenAPI spec is painful. You have to scroll through massive JSON blocks, manually identifying the verb (GET/POST), the path (`/users`), and the required parameters for every single endpoint just to build a usage guide.
Now, you point your agent at the spec URL and call `docs.extract`. It returns clean, structured data—a list of endpoints categorized by API group—ready to use in your code or documentation.
Common Questions About DocBreach MCP
How do I find documentation for a service using docs.discover? +
You just give docs.discover the name and type of the API (e.g., 'payment gateway webhooks'). It returns potential URLs for you to read later.
Does docs.read work with PDFs? +
Yes, it handles various formats including PDFs (up to 5MB). The output is always cleaned up and ready for an LLM context window.
What's the difference between docs.map and docs.search? +
docs.map gives you the complete organizational structure of a domain (the sitemap). docs.search lets you search for specific keywords within that known site.
Do I need an API key to use docs.extract? +
Nope. You don't need any authentication keys or access tokens; the server reads the specification file directly and extracts the data for you.
Can I use docs.read on documentation that isn't public, like internal company sites? +
Yes, it handles private sources as long as your AI client can access them. DocBreach reads the content directly via URL, meaning it doesn't rely on public web crawling or API keys for basic retrieval.
When running docs.extract, how do I narrow down the endpoints by specific functionality? +
You provide a descriptive tag to the docs.extract tool. If you don't give a filter tag, it returns a summary of all available tags and counts for every group.
Is there any limit or rule regarding how often I can use docs.discover? +
The system advises against calling docs.discover in rapid loops. It's better to refine your search query iteratively rather than hitting the tool repeatedly with minor changes.
What exactly does docs.map provide that a standard sitemap or simple crawl doesn't? +
The docs.map function generates a complete, organized table of contents for an entire domain. This goes beyond links; it structures the documentation into navigable sections.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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