The Guardian MCP for AI. Query decades of news archives directly with your AI agent.
Works with every AI agent you already use
…and any MCP-compatible client








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The Guardian MCP Server gives your AI agent structured access to The Guardian's entire content archive. It lets you run full-text queries across decades of reporting, filter by section or tag, and retrieve the full metadata for any article—all via natural conversation.
Stop browsing; start querying.
What your AI can do
Get item
Fetches the complete body, byline, and metadata for one specific Guardian article.
Get latest content
Retrieves a list of articles ordered from most recent to oldest.
Get section details
Gets editorial highlights and the most-viewed content for any given section.
The server pulls the full text and metadata for any specific article by providing its identifier.
You can run advanced searches across the archive, filtering results simultaneously by section name, keyword tag, or precise date range.
The system lists all available editorial sections and tags. This lets your agent map out the full scope of content coverage (e.g., listing every contributor or topic area).
You can query articles across multiple regional editions, including UK, US, Australia, and International feeds.
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The Guardian MCP Server: 10 Tools for Content Archives
These tools let your agent access specific functions: from listing all available sections to running deep searches across regional content.
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Start using The Guardian on VinkiusGet Item
Fetches the complete body, byline, and metadata for one specific Guardian article.
Get Latest Content
Retrieves a list of articles ordered from most recent to oldest.
Get Section Details
Gets editorial highlights and the most-viewed content for any given section.
List Editions
Lists all available regional versions of The Guardian (e.g., US, UK, Australia).
List Sections
Lists every major editorial section available on the platform, like 'Technology' or...
List Tags
Provides a list of all content tags used by The Guardian (e.g., keyword, contributor, tone).
Search By Date Range
Searches the entire archive for articles published within user-specified start and end dates.
Search By Section
Browses content filtered specifically to a chosen editorial section, supporting...
Search By Tag
Filters and searches the archive using specific keywords or contributor tags.
Search Content
Runs a powerful search across all criteria: section, tag, date range, ordering, and...
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by The Guardian. 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 connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Sifting through news archives manually wastes hours of your time.
Today, if you need to know what The Guardian covered on a topic three years ago, you usually fall into the trap of searching Google News. You get hundreds of links—snippets, headlines, and dates—but accessing the full context means clicking through 20 tabs, copy-pasting URLs, and manually verifying the byline for each piece.
With this MCP server, your agent handles that entire process in one step. You give it a date range and a topic; it returns structured data: article title, full body text, editor metadata—all ready to be processed.
The Guardian MCP Server lets you find content by its structure.
You don't have to guess where the right information lives. Instead of just searching a keyword, you can first run `list_sections` to see if 'Opinion' or 'Business' is the right bucket. Then you use that structural knowledge with `search_by_section`, narrowing your focus immediately.
The key difference is control. You move from guessing what content exists to explicitly asking for data based on known categories, dates, and tags. It’s surgical precision.
What your AI can actually do with this
The Guardian MCP Server gives your AI agent deep access to decades of reporting across The Guardian’s entire content archive. You're not just browsing; you're running full-text queries against a massive, structured database of journalism. This server lets your agent pull everything—from the raw body text and byline details for any specific article to mapping out every contributor and topic area they’ve ever covered.
You can start by figuring out the scope. If you want to know what content exists, you'll first use list_sections to grab a list of all major editorial categories—things like 'Technology,' 'Sport,' or 'Politics.' You'd then run list_tags to pull every single keyword or contributor tag they’ve used over the years.
To map out the entire scope, you can also call list_editions, which lists regional versions of The Guardian, so you know if you need US data, UK data, or Australian feeds.
When you're ready to search, you get powerful filtering options. If you only care about one area, calling search_by_section lets your agent browse content strictly filtered by a chosen category, and it handles pagination for you. You can narrow the focus even more using search_by_tag, which filters results based on specific keywords or named contributors.
For ultimate control, you've got search_content. This tool runs across every possible criteria at once: section name, keyword tag, start and end date range, ordering preference, and pagination. You don't have to run three separate searches; this one query does it all.
If your search is time-bound, you can use search_by_date_range to pull articles published between specific dates you provide. Or, if you know the exact section and the date range, you've got multiple ways to combine those filters for super precise results.
Once a query returns a list of promising article IDs, getting the full details is easy. You use get_item to fetch the complete body text, the byline, and all the necessary metadata for one single article ID. If you just want a snapshot of what’s hot right now, calling get_latest_content returns a list ordered from most recent straight back.
To find out about featured content within any given category, you can check get_section_details, which pulls the editorial highlights and most-viewed pieces for that specific section. It’s all structured so your AI agent gets clean data it can work with immediately.
019d75ac-4498-72b9-9608-48946f62c31a Here's how it actually works
The bottom line is: your agent handles complex journalistic queries using specific APIs instead of just reading a web page.
Subscribe to the server. Then, grab a free API key from The Guardian Open Platform.
Your AI client sends a query—for example, asking for all articles about 'AI' published in Technology between two dates.
The server executes the necessary search tools and returns structured JSON data containing article summaries, full texts, or metadata.
Who is this actually for?
This is for the researcher who needs to prove a trend existed across five years of coverage, or the data scientist building knowledge graphs from media sources. If your job involves tracking evolving narratives or compiling large-scale content indexes, you need this.
Uses search_by_date_range and list_tags to track how a specific political topic's coverage changed over time.
Runs get_section_details or search_by_section to quickly surface related stories when writing an investigative piece, without leaving their editor environment.
Integrates the data using all tools—from list_sections for schema definition to get_item for full text extraction—into NLP pipelines.
What Changes When You Connect
Deep Trend Analysis: Use search_by_date_range and search_content to track how the coverage of a topic changes over years, which is impossible by just browsing the homepage.
Contextual Depth: Instead of getting surface links, use get_item to pull the full article text, metadata, and byline for immediate context within your workflow.
Pinpoint Precision: Need to know if a story was about AI and featured a specific author? Use search_by_tag combined with list_tags to narrow results down immediately. This is better than a general search.
Schema Mapping: Start by running list_sections and list_editions. This gives your agent the full vocabulary of content, ensuring it doesn't miss relevant areas or regional perspectives.
Multi-Layer Querying: The combination of tools—starting with search_by_section, then filtering that result using search_by_tag—allows for highly precise, multi-step data retrieval.
See it in action
Tracking Policy Shifts
A policy expert needs to see how 'carbon market' coverage evolved between 2018 and 2024. They use search_by_date_range with the keyword tag, retrieving a chronological list of articles that proves shifts in editorial focus.
Verifying Source Material
A journalist is writing about an old event and needs confirmation on what was reported. They use list_sections to find the relevant section (e.g., 'World') and then run get_latest_content or a targeted search to pull historical source material.
Building Knowledge Graphs
A data science team wants to index all articles mentioning 'artificial intelligence' from the Technology section. They use list_sections, then run search_by_section and filter results with search_by_tag to build a complete, structured knowledge base.
Competitive Media Analysis
A marketing firm wants to know what content is currently gaining attention. They use get_section_details to find the most-viewed or featured content across major sections and pull those titles for a competitive report.
The honest tradeoffs
Trying to search everything with one query
Asking your agent, 'Find all articles about AI in politics from Australia last year.' While search_content is powerful, it's hard for the model to know which specific filters you need.
Don't rely solely on general search. First, use list_editions to confirm the regional scope, then run search_by_section (for 'Politics'), and finally refine that result using a tag-specific query like search_by_tag('artificial-intelligence'). This guarantees accuracy.
Assuming general keywords are enough
A user asks for articles on 'climate change' but doesn't specify if they mean the topic, a contributor, or an event. The search results will be noisy.
Always use list_tags first. This shows the exact taxonomy (e.g., is it keyword/climate-change or tone/alarming) and allows you to run a precise search_by_tag, yielding much cleaner data.
Skipping metadata checks
The agent only pulls the article title and snippet. The user can't verify if the piece was written by an internal staffer or syndicated from a wire service.
Always use get_item. This tool is mandatory when you need the full editorial metadata, including the byline and publication details, to validate the source.
When It Fits, When It Doesn't
Use this MCP Server if your workflow requires structured data extraction from specific news publications. You need measurable results—a list of 10 articles with dates and full text, for example. If you're building a knowledge base or running longitudinal studies (tracking X topic over time), this is essential.
Don't use it if all you need is general web context or recent opinions that aren't tied to the archive structure. For basic, non-archival information gathering, your agent might just scrape the front page. But when you need verifiable source material and deep filtering (e.g., 'Show me every article tagged 'policy' AND written in 2021'), this server is necessary because its specialized tools (search_by_tag, search_by_date_range) provide depth that simple browsing can't match.
Questions you might have
Can I retrieve the full text of a Guardian article, not just the headline? +
Yes. Use the get_item tool with the article's path ID. The response includes the full body text, byline, standfirst, publication date, section, tags, and thumbnail image when available.
How far back does the Guardian Content API archive go? +
The Guardian Content API provides access to articles dating back to 1999. You can use search_by_date_range with specific start and end dates to query historical content from any period covered by the archive.
Is a paid subscription required to use this integration? +
No. The Guardian Open Platform offers a free developer API key that supports up to 12 calls per second and 5,000 calls per day. This is sufficient for most research and automation workflows.
Can I filter articles by topic, section, or contributor? +
Yes. The search_content tool accepts section, tag, and date filters. Use list_sections to discover available sections and list_tags to find keywords, contributors, and series to refine your queries.
When I use the `get_item` tool, what metadata fields are returned with the full article text? +
You get the complete body text along with critical context: the byline, publication date, and specific editorial metadata. This structured data lets your agent build knowledge graphs or index content without needing extra calls.
What happens if I run too many searches using `search_content` in a short time? +
The system adheres to The Guardian Open Platform's rate limits. If you hit the quota, you will receive an HTTP 429 error code. Your agent must implement an exponential backoff retry mechanism to continue running.
How do I authenticate my client when using `search_by_section`? +
Authentication is handled via your registered API key, which you provide during the initial setup phase. Your AI client routes this credential through Vinkius so the tool can execute the query on your behalf.
If I use `list_tags` and get no results for a specific keyword, does that mean there is no content? +
No. An empty tag list means that The Guardian's archive currently has no articles matching that exact filter. You should try broadening the query or using the general search_content tool instead.
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