TextRazor MCP for AI. Extract entities, topics & relations from text or URLs.

Q: Can I make TextRazor recognize my company's internal acronyms?

Yes. You must first use createdictionary to build a custom dictionary, then call adddictionaryentries to populate it with your proprietary terms before running analysis.

Q: Is there a way to check my API usage limits with TextRazor?

You can check your account standing by calling the getaccount tool. This returns all current metrics, including remaining credits and rate limits for your service.

Q: Before running an analysis, how do I review my custom dictionary contents with TextRazor?

You use the listdictionaryentries tool to see every term currently defined. This lets you verify your specialized vocabulary before adding new entries or running a deep analysis.

Q: How do I check which custom classifiers are available for my account using TextRazor?

You call listcategories 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.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

How this MCP server connects to your AI agent

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 AI agents can do with TextRazor Automation

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.

+ 8 more capabilities included

Analyze Content

Pass text or a URL to analyze it for key entities, underlying topics, and relationship structures.

Manage Custom Vocabulary

Create and update specialized dictionaries so your agent recognizes domain-specific terminology.

Classify Documents

Define and use custom classifiers to automatically tag content into predefined organizational categories.

Retrieve Account Metrics

Get current usage details, including API limits and billing information.

Ask an AI about this

Included with Plan

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AI Agent

What AI agents can do with 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.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using TextRazor on Vinkius

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...

Create Category

Creates or updates a custom topic classifier for organizing content.

Create Dictionary

Builds a new custom dictionary to recognize proprietary terms.

Delete Category

Removes an existing custom classifier category.

Delete Dictionary

Deletes an entire custom dictionary and all its entries permanently.

Get Account

Retrieves your current account details, usage metrics, and service limits.

Get Dictionary

Pulls the specific details of a custom dictionary by name or ID.

List Categories

Shows all current categories within a specified custom classifier.

List Dictionary Entries

Lists every entry contained within a specific custom dictionary.

List Dictionaries

Retrieves a list of all custom dictionaries you have created.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The TextRazor integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "textrazor": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the TextRazor tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"textrazor": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with TextRazor, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

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.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Built on the Model Context Protocol (MCP) for 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 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Sifting through endless articles for key data points shouldn't feel like a full-time job., Solved with Vinkius AI Gateway

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.

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nlp

entity-extraction

text-analysis

semantic-web

topic-tagging

What your AI can actually do with this

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.

Built · Hosted · Managed by Vinkius TextRazor MCP Server - Extract Entities & Topics

Server ID 019e5d5d-f592-7141-9057-c6fe727af2cb

Vinkius Inspector

Compliance Grade A+

Score 100/100

Report View Report ↗

Here's how it actually works

The bottom line is that your agent executes complex NLP tasks without needing manual setup or specialized coding beyond calling the right tool.

First, subscribe to the server and provide your TextRazor API Key.

Next, instruct your AI client (agent) to use a specific tool, like analyze_text, providing it with the content or URL you want analyzed.

The system sends the data to TextRazor, which returns structured JSON containing the extracted entities, topics, and relationships.

Who is this actually 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.

Data Scientist

Uses analyze_text to automate the extraction of structured data from massive document sets and raw text feeds.

Content Manager

Employs create_dictionary and list_categories to enforce consistent tagging across a large volume of blog posts or articles for SEO purposes.

Market Researcher

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_text tool extracts all associated entities and relationships instantly.

Stop guessing about jargon. You can use create_dictionary to 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_text and 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_text means you can analyze content pulled directly from messy web scrapes or poorly formatted reports.

Manage your knowledge base programmatically. Use tools like list_dictionaries and get_dictionary to check what vocabulary is already active before attempting a complex analysis run.

See it in action

01 01

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.

02 02

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.

03 03

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.

04 04

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 honest tradeoffs

Analyzing text without custom terms

Anti-pattern

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.

The Fix

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

Anti-pattern

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.

The Fix

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

Anti-pattern

Trying to build a giant, monolithic classifier that tries to categorize everything at once and fails.

The Fix

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.

Questions you might have

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.

How this MCP server connects to your AI agent

What AI agents can do with TextRazor Automation

Add dictionary entries

Analyze text

Create category

What AI agents can do with TextRazor MCP Server: 11 Tools for NLP Analysis

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...

Create Category

Creates or updates a custom topic classifier for organizing content.

Create Dictionary

Builds a new custom dictionary to recognize proprietary terms.

Delete Category

Removes an existing custom classifier category.

Delete Dictionary

Deletes an entire custom dictionary and all its entries permanently.

Get Account

Retrieves your current account details, usage metrics, and service limits.

Get Dictionary

Pulls the specific details of a custom dictionary by name or ID.

List Categories

Shows all current categories within a specified custom classifier.

List Dictionary Entries

Lists every entry contained within a specific custom dictionary.

List Dictionaries

Retrieves a list of all custom dictionaries you have created.

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more

Sifting through endless articles for key data points shouldn't feel like a full-time job., Solved with Vinkius AI Gateway

TextRazor MCP Server: Structured insights from messy content.

nlp

entity-extraction

text-analysis

semantic-web

topic-tagging

What your AI can actually do with this

Deep Text Analysis

Custom Vocabulary Management

Content Classification (Topics)

Account Metrics & Status

Here's how it actually works

Who is this actually for?

What Changes When You Connect

Monitoring Competitor News Flow