MCP For Claude Marketplace
TF-IDF Vectorizer Engine

TF-IDF Vectorizer Engine MCP Connector for Claude

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Exact Term Frequency-Inverse Document Frequency scores. Stop LLMs from guessing keyword relevance across massive corpuses.

1 tools Official Updated Jun 28, 2026 Official Vinkius Partner
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Large Language Models often hallucinate when asked to perform statistical text analysis like TF-IDF (Term Frequency-Inverse Document Frequency). They simply guess which keywords seem 'important'. This engine calculates mathematically perfect TF-IDF scores across arrays of documents deterministically local, using the Node.js V8 engine. It allows agents to rank documents objectively by true term relevance.

nlptext-analysisstatistical-modelingkeyword-extractiondata-processingdeterministic-math

1 tools expose this connector's capabilities to your AI agent.

calculate_tf_idf

Calculates the exact TF-IDF scores for an array of terms across an array of documents

See how to talk to your AI agent using TF-IDF Vectorizer Engine.

Here are 5 article texts and the terms ['crypto', 'regulation']. Give me the exact TF-IDF scores to rank these articles.

The computation has been executed with mathematical precision. All results are exact and ready for review.

I have a dataset of customer reviews. Run TF-IDF on the words 'slow' and 'expensive' to see which reviews focus on them.

The computation has been executed with mathematical precision. All results are exact and ready for review.

Calculate the exact TF-IDF scores for these 10 support tickets using these 3 technical keywords.

The computation has been executed with mathematical precision. All results are exact and ready for review.

Word counting overvalues common words like 'the' or 'and'. TF-IDF lowers the weight of words that appear in many documents, highlighting terms that are uniquely relevant to a specific text.

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