Supercharge your AI with Deterministic Text Summarizer. Extract facts, not AI guesses.
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The Deterministic Text Summarizer & Extractor MCP provides pure, mathematical text analysis without needing external API calls or hallucination risk.
It extracts key information by pulling exact sentences and phrases directly from source material using Term Frequency (TF) algorithms. Use this to reliably find core concepts, analyze keyword density, and condense long documents into actionable data points.
What your AI can do
Extract top bigrams
Pulls the top N most common two-word phrases from a text, ideal for mapping out SEO topics or semantic links.
Extract top keywords
Calculates and returns the top N keywords based on term frequency, filtering out meaningless stop words.
Extractive summary
Runs a mathematical algorithm to select and combine the most important sentences from a document for condensation.
Selects the most mathematically important sentences from a document and compiles them into an extractive summary.
Counts the top recurring keywords in a text using Term Frequency analysis, ignoring common stop words like 'the' or 'a'.
Finds and counts the most frequently occurring two-word phrases (bigrams), useful for understanding semantic connections.
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Compatible AI Apps
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Deterministic Text Summarizer & Extractor: 3 Tools
These three tools allow you to process text by extracting keyword counts, identifying common two-word phrases, or creating mathematically supported summaries.
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Start using Deterministic Text Summarizer & Extractor on VinkiusExtract Top Bigrams
Pulls the top N most common two-word phrases from a text, ideal for mapping out SEO topics or semantic links.
Extract Top Keywords
Calculates and returns the top N keywords based on term frequency, filtering out...
Extractive Summary
Runs a mathematical algorithm to select and combine the most important sentences...
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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 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Analyzing large texts usually means copy-pasting into a chat window.
Right now, when you get a massive PDF or research paper, your workflow is manual. You open the file, skim the introduction, highlight key paragraphs, and then spend time copying those chunks of text into a separate document just to keep track of what was important. It's tedious, slow, and easy to lose context in the copy/paste process.
With this MCP, you simply feed the full document to your agent. You instruct it to analyze the structure mathematically, whether that’s using `extract_top_keywords` or running an extractive summary. What changes is that you get a clean, structured output—a list of facts and phrases—without having to manage any manual copy-pasting.
Extracting core concepts with the Deterministic Text Summarizer & Extractor MCP
Before this, identifying key themes meant relying on a general LLM prompt and hoping it didn't miss anything critical or hallucinate. You were guessing at importance. The manual step was always verifying that the generated summary actually contained verifiable proof in the original text.
Now you can run `extract_top_bigrams` to see exactly which two-word phrases dominate the discourse, or use `extractive_summary` to get a mathematically defined set of core sentences. You know the output is clean and directly traceable.
What your AI can actually do with this
When you need to extract facts, not interpretations, this MCP is the right tool. Most large language models generate 'abstractive' summaries; they write new text based on what they think the source means. That process is prone to hallucination and burns through tokens fast. Our approach flips that script entirely.
It uses pure math—Term Frequency analysis—to identify the most statistically important parts of a document, pulling out those exact, unmodified sentences. This MCP lets you analyze text structure directly. Need to find recurring themes or boost SEO content? You can use it to pinpoint the top two-word phrases (bigrams) or count core vocabulary with extract_top_keywords.
By connecting this through Vinkius, your agent gets a guaranteed way to process complex documents for strict data extraction.
019e38f9-f5e2-73c1-9811-38e4f31c3b72 Here's how it actually works
The bottom line is that you get deterministic, verifiable text features without relying on interpretive language generation.
You provide your AI client with the source text you want analyzed.
Your agent calls one of the dedicated tools, like extract_top_keywords, specifying what kind of analysis is needed (e.g., top 10 keywords).
The MCP returns a structured data array containing only the requested elements—the exact sentences, keyword counts, or bigrams.
Who is this actually for?
This MCP is for the data scientist, technical writer, and academic researcher. You're someone who doesn't trust an AI to 'just know' the answer; you need proof of concept based on frequency counts or direct textual evidence.
Needs to quickly analyze massive amounts of source material (e.g., old white papers) and extract the most critical, fact-based claims for a new product manual.
Processes competitor content or long articles to find high-density keyword pairings and topics that need integration into client blogs.
Manages a bibliography of journal articles, needing to summarize each paper using only the most mathematically weighted sentences for literature review.
What Changes When You Connect
Accuracy: Because the extractive_summary tool only pulls existing text, you eliminate the risk of hallucinations common with abstractive models. The output is guaranteed to be factually sourced from the input document.
Granularity: Instead of just knowing a topic, the extract_top_bigrams tool lets you see exactly which two words appear together most often. This is critical for deep SEO topic modeling or identifying specialized technical phrases.
Efficiency: The extract_top_keywords function efficiently counts core vocabulary using TF analysis, saving you from sifting through massive documents manually just to find the main themes.
Speed: The architecture runs on a pure Javascript runtime. This means fast processing of text data without loading bloated NLP packages into your agent's environment.
Control: You control the output depth. By defining 'top N' for keywords or sentences, you maintain precise control over how much detail is included in the final summary.
See it in action
Summarizing a large legal filing
A paralegal needs to synthesize key points from a 50-page deposition transcript. Instead of asking an agent for a general overview, they use extractive_summary with the request: 'Extract the top 5 most mathematically relevant sentences.' The resulting output provides only verifiable claims, citing the exact text needed for cross-referencing.
Analyzing competitive blog content
An SEO manager collects ten competitor articles. To understand their core focus areas, they use extract_top_bigrams on all texts. This reveals patterns like 'cloud computing' or 'data security,' allowing them to target topic gaps that the competition overlooked.
Mining academic literature for a review
A student has fifty research papers and needs to write an introduction section. They process each paper individually using extract_top_keywords to pull out the most frequent, non-stop words. This builds a robust foundation of technical vocabulary before writing the draft.
The honest tradeoffs
Relying on general summarization
Asking your agent, 'Summarize this paper.' If the LLM writes new text, you lose the ability to verify which sentence was taken from the source, making it unusable for legal or academic review.
Instead, use extractive_summary and specify a count (e.g., 4 sentences). This forces the agent to return only the most statistically weighted phrases pulled directly from the original document.
Only looking for single keywords
Just counting single words like 'data' or 'AI'. This misses the nuance and relationship between concepts, leading to a weak understanding of the actual subject matter.
Always run extract_top_bigrams alongside keyword analysis. Finding pairs like 'machine learning' shows semantic depth that single word counts miss.
Processing unstructured data
Giving the tool a mixed file containing images, tables, and text without preprocessing it first.
Ensure your input is clean, pure text. The MCP relies on consistent tokenization of written characters for its mathematical models to work correctly.
When It Fits, When It Doesn't
Use this MCP when the absolute priority is verifiability and structure. If you need to analyze a document purely by frequency counts—like finding the most repeated technical terms or common two-word phrases—this toolset is ideal. It provides mechanical evidence, not interpretive fluff.
However, don't use it if your goal is creative writing, adjusting tone, or generating dialogue. If you need to change how something sounds (e.g., making a technical report sound more conversational), this MCP won't help; its job stops at extraction. For those tasks, an abstractive model is necessary. When in doubt, if the output needs to be used as evidence, run it through extractive_summary first.
Questions you might have
What is the difference between Extractive and Abstractive summarization? +
Abstractive summarization (what ChatGPT does) writes a completely new text based on its understanding. Extractive summarization (what this tool does) selects the most mathematically important sentences directly from the original text without changing a single word. It guarantees 100% factual accuracy.
Does the keyword extraction ignore simple connection words? +
Yes. It has a built-in cross-language 'Stop Words' dictionary (supporting English, Portuguese, and Spanish) to ensure words like 'the', 'and', 'for', 'uma' are completely ignored during Term Frequency calculations.
Why use this tool instead of just asking an AI to summarize? +
If you have a massive 50-page document, passing the entire text into an AI context window is extremely expensive and slow. Running an algorithmic extraction first condenses the text dramatically while retaining all key facts.
Do I need to connect any external API keys for `extractive_summary` to work? +
No, you don't. This MCP uses a purely mathematical algorithm that runs in the Javascript runtime. It never requires connecting to paid APIs or external services.
What determines the performance of the Deterministic Text Summarizer & Extractor? +
It’s fast because it's built on a pure JS runtime. The system analyzes text frequency directly, avoiding resource-intensive calls that bog down traditional NLP packages.
How does using `extract_top_bigrams` differ from running `extract_top_keywords`? +
Keywords find single words based on their individual frequency. Bigrams, however, look for pairs of adjacent words that appear together often, giving you a deeper semantic view.
Does the Deterministic Text Summarizer & Extractor have limits when running `extract_top_keywords`? +
The tool is designed to handle large volumes of text efficiently. It processes data by calculating term frequency, which scales better than model-based approaches.
Is the Deterministic Text Summarizer & Extractor suitable for non-English content using `extractive_summary`? +
While optimized for English stop word handling, it uses a foundational Term Frequency (TF) algorithm. You can run it on other languages to extract important phrases.
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