TextRazor MCP. Extract entities, topics & relations from text or URLs.
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TextRazor delivers advanced NLP right through your AI client. It analyzes raw text or public URLs to pull out entities, topics, and complex relationships.
You can build custom dictionaries for industry jargon or classify entire documents automatically. This server lets your agent perform deep semantic analysis on anything you feed it.
What your AI agents can do
Add dictionary entries
Adds specific terms to a custom dictionary you created.
Analyze text
Analyzes provided text or URL content to pull out entities, topics, and relationships.
Create category
Creates or updates a custom topic classifier for organizing content.
Pass text or a URL to analyze it for key entities, underlying topics, and relationship structures.
Create and update specialized dictionaries so your agent recognizes domain-specific terminology.
Define and use custom classifiers to automatically tag content into predefined organizational categories.
Get current usage details, including API limits and billing information.
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TextRazor MCP Server: 11 Tools for NLP Analysis
These eleven tools allow you to manage custom vocabularies, categorize content, and perform deep semantic analysis on text and URLs directly from your AI client.
019e5d5dadd dictionary entries
Adds specific terms to a custom dictionary you created.
019e5d5danalyze text
Analyzes provided text or URL content to pull out entities, topics, and relationships.
019e5d5dcreate category
Creates or updates a custom topic classifier for organizing content.
019e5d5dcreate dictionary
Builds a new custom dictionary to recognize proprietary terms.
019e5d5ddelete category
Removes an existing custom classifier category.
019e5d5ddelete dictionary
Deletes an entire custom dictionary and all its entries permanently.
019e5d5dget account
Retrieves your current account details, usage metrics, and service limits.
019e5d5dget dictionary
Pulls the specific details of a custom dictionary by name or ID.
019e5d5dlist categories
Shows all current categories within a specified custom classifier.
019e5d5dlist dictionaries
Retrieves a list of all custom dictionaries you have created.
019e5d5dlist dictionary entries
Lists every entry contained within a specific custom dictionary.
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What you can do with this MCP connector
You connect TextRazor right into your AI client. Your agent gains serious text analysis power, letting it process raw copy or even live web URLs to pull out deep semantic insights. You don't just get keywords; you get the structure of what people are talking about.
Deep Text Analysis
When you run analyze_text, your agent analyzes provided content—whether it’s a block of text you paste or an entire public URL. It pulls out key entities, underlying topics, and complex relationships between those elements. This tool handles the heavy lifting, giving you structured data on everything from named people to industry-specific concepts.
Custom Vocabulary Management
You can train your agent to understand niche language by building custom dictionaries. If you deal with proprietary jargon or highly specialized field terms, this is how you handle it. You start by running create_dictionary to build a new vocabulary set. Once that dictionary exists, you use add_dictionary_entries to feed it specific terms and their definitions.
To manage what’s there, your agent can list all the dictionaries you've created using list_dictionaries. If you need to see what exactly is inside one of those custom sets, you run get_dictionary by name or ID, which then allows you to check every entry in that specific dictionary via list_dictionary_entries.
You can also permanently wipe a dictionary and all its contents with delete_dictionary.
Content Classification (Topics)
When organization is the game, you use the topic classifiers. To start, your agent can create or update a custom classification system using create_category. This lets you define buckets for your content—say, 'Q3 Reports' or 'Client Feedback.' If a category needs to go, you delete it with delete_category. You always know what categories are live by calling list_categories on the classifier you’ve set up.
These tools let you automatically tag and sort entire documents into predefined buckets.
Account Metrics & Status
If you need to check your usage limits or service status, your agent calls get_account. This single tool pulls all your current account details, including how much capacity you've used up against your assigned API limits. It’s quick and gives you the numbers you need.
This server lets your agent do three main things: it analyzes content for deep structure; it builds custom dictionaries to recognize domain-specific terms; and it sets up classifiers to automatically tag content into specific buckets.
How TextRazor MCP Works
- 1 First, subscribe to the server and provide your TextRazor API Key.
- 2 Next, instruct your AI client (agent) to use a specific tool, like
analyze_text, providing it with the content or URL you want analyzed. - 3 The system sends the data to TextRazor, which returns structured JSON containing the extracted entities, topics, and relationships.
The bottom line is that your agent executes complex NLP tasks without needing manual setup or specialized coding beyond calling the right tool.
Who Is TextRazor MCP For?
Data Scientists who need to process petabytes of unstructured text; Content Managers struggling to tag thousands of articles consistently; Market Researchers tracking real-time news flow. If your job involves turning messy text into structured, actionable data points, this is for you.
Uses analyze_text to automate the extraction of structured data from massive document sets and raw text feeds.
Employs create_dictionary and list_categories to enforce consistent tagging across a large volume of blog posts or articles for SEO purposes.
Runs real-time analysis on social media feeds using the server to extract key entities and relationships from trending news headlines.
What Changes When You Connect
- Structured data comes out of unstructured noise. Instead of manually reading thousands of articles to find every mention of 'Project Phoenix,' the
analyze_texttool extracts all associated entities and relationships instantly. - Stop guessing about jargon. You can use
create_dictionaryto build a custom glossary for your company's internal acronyms, ensuring that even niche terms are correctly identified during analysis. - Organize content automatically. Instead of having someone manually read an article and tagging it 'Finance/Q3 Reports,' the agent runs
analyze_textand uses the classification results to assign precise topics and categories. - Scale your data ingestion without hiccups. The ability to strip HTML tags when calling
analyze_textmeans you can analyze content pulled directly from messy web scrapes or poorly formatted reports. - Manage your knowledge base programmatically. Use tools like
list_dictionariesandget_dictionaryto check what vocabulary is already active before attempting a complex analysis run.
Real-World Use Cases
Monitoring Competitor News Flow
A Market Researcher needs to track how often competitors mention 'AI chip architecture' and which specific executives are cited. They feed 50 recent news articles into the agent, running analyze_text with an entity extractor focused on people and companies. The result is a structured table showing every instance of an executive name tied directly to a company name.
Onboarding New Industry Jargon
A Data Scientist receives reports full of highly specialized medical terms the current AI model doesn't recognize. They use create_dictionary to build a 'Cardiology Terms' dictionary, then run analyze_text. The agent now correctly identifies and labels complex diagnoses that were previously ignored.
Cleaning Up Web Scraped Data
A Content Manager scrapes 200 product pages from a competitor's site. If the raw HTML is messy, analysis fails. The agent first uses the server's cleanup capabilities (part of analyze_text) to strip all tags and metadata before extracting entities like 'model number' or 'release date,' guaranteeing clean data.
Building a Content Taxonomy
A team needs to ensure every new blog post falls into one of five predefined topics. They use create_category to set up the taxonomy, then run analyze_text. The agent automatically suggests the correct category for each piece of content based on semantic analysis, eliminating manual review.
The Tradeoffs
Analyzing text without custom terms
Just passing a chunk of internal document text to analyze_text and getting poor results because the model doesn't know what 'XYZ Protocol' or 'Q3 Budget Cycle' mean.
→
Before running analysis, use create_dictionary to build a custom dictionary. Then, call add_dictionary_entries to load your proprietary terms. Finally, run analyze_text. The model will now recognize and extract the specialized vocabulary.
Ignoring data cleanup
Copy-pasting text directly from a poorly formatted website into an analysis tool and getting junk output because of embedded <br> tags or excessive HTML.
→
Rely on the server's web content handling. When calling analyze_text, let it process URLs, as its internal logic strips out non-essential markup, giving you clean data ready for extraction.
Overcomplicating classification
Trying to build a giant, monolithic classifier that tries to categorize everything at once and fails.
→
Use create_category to define small, focused classifiers (e.g., 'Legal Issues,' 'Product Feedback'). Then use the agent to run multiple, targeted analyses via analyze_text, improving accuracy.
When It Fits, When It Doesn't
Use this server if your primary bottleneck is turning massive amounts of unstructured text—from articles, reports, or scraped web pages—into structured data points. If you need to find out what was said (entities), where it came from (URLs/text), and how the pieces relate (relations), TextRazor is ideal.
Don't use this if you only need simple keyword searching or basic sentiment analysis. For pure counting, a standard database query works better. If your data needs to be filtered by user roles or simple metadata tags that don't require deep semantic understanding, consider using a dedicated search index tool instead. However, if the meaning is what matters—linking 'Elon Musk' (entity) to 'Tesla' (entity) via 'CEO of' (relation)—this server is necessary.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TextRazor. 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 11 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through endless articles for key data points shouldn't feel like a full-time job.
Today, if you need to understand the core themes and people mentioned across 50 research reports, you copy and paste text into multiple tabs. You use keyword searches for 'acquisition' but then have to manually cross-reference every mention of a company name against an internal list of known competitors. It’s slow, error-prone, and requires hours of tedious manual review.
With TextRazor, your agent runs the `analyze_text` tool on the entire batch of reports. Instead of highlighting keywords, it returns structured JSON—a single file listing every entity (person, company), every topic, and how they relate to one another. You skip the reading; you go straight to the actionable data.
TextRazor MCP Server: Structured insights from messy content.
Before, if your industry used proprietary terms—like 'Alpha-Beta Integration' or 'Type III Compliance Protocol'—the analysis would fail because the model wouldn't recognize them. You'd waste time creating spreadsheets just to define basic vocabulary for the system.
Now, you simply use `create_dictionary` and `add_dictionary_entries`. The server learns your specific jargon instantly. When you run `analyze_text`, it treats 'Alpha-Beta Integration' like a known concept, delivering accurate results every time.
Common Questions About TextRazor MCP
How does the TextRazor MCP Server handle text that comes from a website URL? +
The server processes both raw text and public URLs. When you pass a URL to analyze_text, it automatically scrapes the content, cleans out HTML tags, and performs the full analysis on the clean body text.
Can I make TextRazor recognize my company's internal acronyms? +
Yes. You must first use create_dictionary to build a custom dictionary, then call add_dictionary_entries to populate it with your proprietary terms before running analysis.
What if I want to tag documents into multiple groups? +
Use the categorization tools. First, define the groups using create_category, then run the document through analyze_text. The tool classifies the content based on its semantic fit.
Is there a way to check my API usage limits with TextRazor? +
You can check your account standing by calling the get_account tool. This returns all current metrics, including remaining credits and rate limits for your service.
When I use the `analyze_text` tool with a URL, what does TextRazor return regarding relationships? +
It returns structured data detailing dependencies between entities. The response maps out grammatical connections and key relationships found on the page, giving you more than just keywords.
Before running an analysis, how do I review my custom dictionary contents with TextRazor? +
You use the list_dictionary_entries tool to see every term currently defined. This lets you verify your specialized vocabulary before adding new entries or running a deep analysis.
If my text input is too large for the API, how does TextRazor report that error when using `analyze_text`? +
The API response includes a specific status code and an error message detailing the size limit violation. This tells you exactly how much shorter your input needs to be.
How do I check which custom classifiers are available for my account using TextRazor? +
You call list_categories to pull a list of all existing classification schemas. This lets you see what categories you can use or delete before creating a new one.
How can I analyze a web page directly without copying the text? +
Use the analyze_text tool and provide the target link in the url parameter. TextRazor will fetch the content and process it based on your requested extractors.
Can I filter entities by specific types like DBPedia? +
Yes, the analyze_text tool includes an entities_filterDbpediaTypes parameter. You can provide a list of types to narrow down the results to exactly what you need.
How do I manage custom terminology for my business? +
You can use create_dictionary to initialize a new knowledge base and then use add_dictionary_entries to populate it with your specific terms and IDs.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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