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PoetryDB MCP. Query literary data from the world's poetry archives.

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PoetryDB MCP on Cursor AI Code Editor MCP Client PoetryDB MCP on Claude Desktop App MCP Integration PoetryDB MCP on OpenAI Agents SDK MCP Compatible PoetryDB MCP on Visual Studio Code MCP Extension Client PoetryDB MCP on GitHub Copilot AI Agent MCP Integration PoetryDB MCP on Google Gemini AI MCP Integration PoetryDB MCP on Lovable AI Development MCP Client PoetryDB MCP on Mistral AI Agents MCP Compatible PoetryDB MCP on Amazon AWS Bedrock MCP Support

Just plug in your AI agents and start using Vinkius.

PoetryDB connects your AI agent to a massive public domain poetry database. It lets you search thousands of classic poems by author name, title, line count, or specific keywords within the lines.

Need inspiration? Run `get_random_poems`. Building an NLP model that needs rich text data? Use `advanced_search` to combine multiple criteria like John Keats's sonnets with a line count filter.

What your AI agents can do

Advanced search

Runs a complex query combining multiple fields like author, title, or line count simultaneously.

Get random poems

Retrieves an unpredictable set of random poems for quick reading and inspiration.

List authors

Provides a complete list of every author available in the PoetryDB collection.

+ 5 more capabilities included
List all available authors

Retrieves a complete manifest of every poet in the database.

List all poem titles

Provides an exhaustive list of every poetic work stored in PoetryDB.

Find poems by author name

Searches for works, supporting both partial and exact matches against a specific poet's name.

Search by poem title

Locates poems using either the full or a partial match of the work’s official title.

Filter poems by line count

Narrows results to only include poems that have an exact number of lines you specify (e.g., sonnets with 14 lines).

Search for specific phrases

Finds poems containing a given block of text or keyword within their actual lines.

Combine multiple search criteria

Runs complex queries by simultaneously filtering results across author, title, line count, and more fields.

Supported MCP Clients

Claude Claude
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Gemini Gemini
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+ other MCP clients
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AI Agent

PoetryDB MCP Server: 8 Tools for Literary Data Access

These tools allow your AI client to query the poetry database. They let you list authors, filter by line count, or run advanced searches across multiple criteria.

advanced019e5d48

advanced search

Runs a complex query combining multiple fields like author, title, or line count simultaneously.

get019e5d48

get random poems

Retrieves an unpredictable set of random poems for quick reading and inspiration.

list019e5d48

list authors

Provides a complete list of every author available in the PoetryDB collection.

list019e5d48

list titles

Retrieves an exhaustive list of poem titles stored in the database.

search019e5d48

search by author

Searches for poems by a specified author, supporting both partial and exact name matches.

search019e5d48

search by linecount

Filters the database to find only those poems that contain an exact number of lines.

search019e5d48

search by lines

Searches for poems where specific text or keywords appear within their actual poem lines.

search019e5d48

search by title

Finds poems using a specified title, supporting both partial and exact matches.

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What you can do with this MCP connector

PoetryDB MCP Server connects your AI client directly to a massive collection of public domain poetry. You're talking about thousands of classic works here, and this server lets your agent act like a digital literary researcher—it pulls precise data when you tell it what to look for.


list_authors gives you a complete manifest of every single poet in the PoetryDB collection; you'll get an exhaustive list so you never have to guess who’s represented. list_titles handles the same thing for poems, retrieving a comprehensive list of every poetic work stored in the database.

If you know who wrote it or what the poem's title is, you can target your search immediately. You run search_by_author to find works by a specific poet; this function supports both partial and exact name matches, so even if you only remember part of their name, you’ll get results.

Similarly, you use search_by_title when you need poems based on the title—it finds matching works whether you give it the full name or just a snippet.

For narrowing down your search, PoetryDB gives you several filters. If you're looking for sonnets, for instance, you run search_by_linecount, and it filters the entire database to show only those poems that contain an exact number of lines, like 14. You can also pinpoint works using search_by_lines when all you remember is a few key phrases or blocks of text from inside the poem's actual lines—it searches the content itself.

When simple searches aren't enough, advanced_search lets your agent run complex queries by combining multiple criteria simultaneously. You don't have to do three separate calls; you can filter results across author, title, line count, and keywords all in one go. This is how you combine those specialized filters for the deepest research.

Need inspiration fast? Don’t waste time scrolling through indexes. Just run get_random_poems, and it pulls an unpredictable set of random poems instantly, giving you a quick read or a starting point when you're stuck.

This server gives your agent granular control over the entire database, letting you check for authors and titles, narrow by length, search content, and combine everything into one powerful call.

How PoetryDB MCP Works

  1. 1 First, subscribe to the PoetryDB server on Vinkius. Then, your AI client uses an access identifier (use 'PUBLIC' for standard read access) to connect.
  2. 2 Next, you ask your agent a specific question—for example: 'Find me poems by Emily Dickinson that have 14 lines and mention 'chance'.' The agent maps this request to the right tool.
  3. 3 Finally, the PoetryDB server executes the query against the database and sends back the structured list of matching poem data.

The bottom line is: your AI client acts as a digital scholar, running complex queries across thousands of poems without you having to write SQL or use an external website.

Who Is PoetryDB MCP For?

Writers and academic researchers need this. If you're constantly copy-pasting poem snippets into Notion because you forgot the original author, this is for you. It gives you immediate access to structured literary data when you’re stuck on a deadline.

Creative Writer

Needs quick inspiration or reference. Uses get_random_poems to break writer's block, or uses search_by_author to mimic a specific poet's style.

Academic Researcher

Studies literary patterns across time periods. Uses advanced_search combined with line counts and keywords for precise comparative analysis of multiple texts.

Developer (NLP/ML)

Builds models that need rich, validated text data. Queries the database via list_authors and then uses specific tools to pull structured text segments for testing.

What Changes When You Connect

  • Stop manually checking external websites for poem existence. list_titles and list_authors give you a single, definitive manifest of every piece in the collection.
  • Get immediate creative hits without effort. Just run get_random_poems to pull three completely different poems from authors you might never have heard of.
  • Don't just search by author; combine criteria. The advanced_search tool lets you filter for 'sonnets by Keats with 14 lines' in one go, which is impossible otherwise.
  • Pinpoint text fragments fast. If you only remember a specific line—like 'the stars shine bright tonight'—use search_by_lines. You don’t need the author or title to start your research.
  • Perfect for academic work: Use search_by_linecount when analyzing formal poetry, guaranteeing that every result meets strict structural criteria (e.g., filtering only 12-line poems).
  • It's a single source of truth. Instead of juggling multiple databases or archives, all the data is exposed through simple API calls.

Real-World Use Cases

01

Analyzing Shakespearean influence.

A student needs to compare sonnets that share themes but aren't by the same author. They run list_authors to find all known poets, then use advanced_search combining 'sonnet' (title filter) and a specific line count (14 lines). The agent returns a curated list for immediate comparison.

02

Finding an obscure poem.

A writer remembers only a few phrases from a poem but can't recall the title or author. They run search_by_lines with those key phrases. The agent returns matches, allowing them to retrieve the full text and finally identify the correct source.

03

Building a poetry API endpoint.

A developer needs thousands of clean, structured poems for an app test. Instead of manual data scraping, they call list_authors first, then loop through them using search_by_author to pull specific datasets into their application.

04

Just needing a break from research.

A researcher is deep in source material and needs a palate cleanser. They run get_random_poems. The agent pulls three unrelated poems, offering quick context shifts and fresh reading without any search parameters.

The Tradeoffs

Treating the database like a generic search engine

Trying to ask your AI client: 'Find all poems about love.' This is too vague and doesn't use structured tools, so it fails or gives irrelevant text.

Don’t rely on general language queries. Instead, run advanced_search by combining a known author (e.g., 'Dickinson') with specific keywords using search_by_lines. This forces the AI to use the tool's structure and get actual data.

Overcomplicating simple lookups

Trying to run a full, complex query (advanced_search) when all you really needed was to see what titles are available. This wastes tokens and adds unnecessary latency.

If you just want the names, use list_titles or list_authors. These tools are designed for quick, single-purpose data retrieval.

Searching by a keyword that isn't in the lines

Running search_by_lines with a common word like 'the' or 'and.' The tool will return thousands of results because every poem contains those words.

Be specific. Use proper nouns, unique phrases, or quotes you remember exactly. The more specific the text in your search query, the better search_by_lines performs.

When It Fits, When It Doesn't

Use this server if your research requires structured literary data from a known body of work (public domain poetry). You need precise control over filters like author, title, or line count. If you're building a tool that must validate poetic structure—like ensuring all results are sonnets—you MUST use search_by_linecount and combine it with other tools.

Don't use this if you only need general literary advice (e.g., 'Write a poem about rain'). For that, your agent should just write the text itself. If you are looking for data on historical poetry movements or analysis outside of the poems themselves, you need a different kind of database, not PoetryDB.

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by PoetryDB. 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 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.

Available Capabilities

advanced_search get_random_poems list_authors list_titles search_by_author search_by_linecount search_by_lines search_by_title

Finding a specific poem shouldn't feel like detective work.

Today, finding an obscure piece of literature is a headache. You remember the author and maybe one line, but the original archive might be fragmented or locked in university databases that don't talk to your agent. You end up cross-referencing Google Books with JSTOR just to confirm if 'Ozymandias' was written by Shelley.

With PoetryDB MCP Server, you skip all the searching. Your AI client uses `search_by_author` and then `search_by_lines` simultaneously. The agent finds the poem—with its full text and metadata—in seconds. It just works.

PoetryDB MCP Server: Access structured poetry data.

Before this, getting structural data meant multiple manual steps: 1) Find the poet's name, 2) Check if they wrote a specific type of poem (like a sonnet), and 3) Then count the lines manually. It was tedious copy-pasting across three different sheets.

Now, you combine all that into one query using `advanced_search`. You tell your agent: 'Find poems by this author with exactly 14 lines.' Done. The system handles the filtering and retrieval automatically.

Common Questions About PoetryDB MCP

How do I find a poem if I only know a few words? (Using search_by_lines) +

Use search_by_lines. You feed your agent the specific text snippet you remember. The server searches all lines in the database and returns any poems that contain those keywords, regardless of author or title.

What if I want to check how many authors are available? (Using list_authors) +

Just run list_authors. This tool fetches a complete manifest of every single poet stored in PoetryDB. It's the fastest way to see who you can search for.

Can I combine multiple filters like author and line count? (Using advanced_search) +

Yes, that’s exactly what advanced_search is for. You pass all criteria—like 'Jane Austen' AND '12 lines'—and it returns only the perfect matches.

Is there a way to just get something random? (Using get_random_poems) +

Use get_random_poems. This tool bypasses all searching and simply pulls three random selections from the database. It’s great for quick inspiration.

How do I see the full catalog of available poem titles using list_titles? +

The list_titles tool returns every title currently indexed in PoetryDB. This lets you quickly map out your research scope before running specific content searches.

What happens if I run search_by_linecount with an impossible number? +

The server rejects the query and returns a clear error message indicating invalid input parameters. You must use positive integers for line counts to successfully retrieve poems.

Does search_by_author support partial name matches or does the author need to be exact? +

The search_by_author tool handles both partial and exact matches. This flexibility means you can use fragments of a poet's name if you aren't sure of the correct spelling.

What access identifier do I input when setting up my AI agent connection? +

You should enter 'PUBLIC'. Using this standard identifier allows your AI client to connect and explore the literature collection without needing a custom API key or complex authentication setup.

Can I search for poems that contain specific words or phrases in their text? +

Yes! Use the search_by_lines tool. Provide a string, and the agent will return poems that include that specific text within their lines.

Is it possible to find a poem if I only know the author and the approximate length? +

Absolutely. You can use the advanced_search tool to combine fields like 'author' and 'linecount' to narrow down the results to exactly what you're looking for.

How can I discover new poets I haven't read before? +

You can use list_authors to see the full directory of available poets, or use get_random_poems to have the agent surprise you with a random selection from the database.

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Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
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Vercel Vercel
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