Writer (AI Enterprise LLM) MCP for AI. Query proprietary data and generate content from your documents.
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
Writer (AI Enterprise LLM) connects your AI agent to full-stack enterprise models and Knowledge Graphs. Use it to process internal documents, answer complex questions based on proprietary data, or generate high-quality content using custom trained models.
What AI agents can do with Writer (AI Enterprise LLM) Automation
Add file to graph
Adds an uploaded file to a Knowledge Graph for context and analysis.
Analyze vision
Uses prompts to analyze the contents of images or documents you provide.
Ask question
Queries one or more Knowledge Graphs directly with a question, retrieving answers from the contained data.
Create dedicated knowledge graphs and add files to them so the LLM can answer questions using only that specific set of internal documents.
Upload various file types, like PDFs or images, and have the system analyze their content using prompts before generating a response or writing new material.
Use high-performance models to generate long-form text completions or handle conversational chat tasks based on specific inputs.
List, upload, download, and permanently delete files needed for training graphs or providing context to the AI agent.
Execute complex, multi-step content generation tasks asynchronously, retrieving job status when finished.
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What AI agents can do with Writer (AI Enterprise LLM) with 24 Tools
These tools let your agent manage files, create knowledge graphs, run applications, and generate highly customized content using enterprise-grade models.
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 Writer (AI Enterprise LLM) on VinkiusAdd File To Graph
Adds an uploaded file to a Knowledge Graph for context and analysis.
Analyze Vision
Uses prompts to analyze the contents of images or documents you provide.
Ask Question
Queries one or more Knowledge Graphs directly with a question, retrieving answers...
Chat Completion
Generates conversational chat text completions using Writer's enterprise models.
Create Graph
Builds a brand new, empty Knowledge Graph structure for your specific project or...
Delete File
Removes a specified file permanently from the system and context.
Download File
Downloads the raw binary content of any listed file.
Generate Application Content Async
Starts a complex content generation job that runs in the background, providing a job...
Generate Application Content
Creates content synchronously by running a no-code application workflow.
Get Application Job
Checks the current status and result of an asynchronous content generation job.
Get Application
Retrieves configuration details and required inputs for a specific pre-built...
Get File
Fetches metadata (like size or owner) for a specific uploaded file ID.
List Application Jobs
Retrieves a list of all job executions for a given application workflow.
List Applications
Shows a directory of all deployed, no-code agent applications available in the...
List Files
Retrieves a paginated list of every file that has been uploaded to the platform.
List Graphs
Lists all existing Knowledge Graphs, allowing you to see what data sources are...
List Models
Provides a list of the specific Writer models currently accessible for generation...
Parse Pdf
Converts PDF documents into plain text or Markdown format (Note: This tool is deprecated).
Remove File From Graph
Deletes a specific file's connection to a Knowledge Graph without deleting the original file.
Retry Application Job
Attempts to re-run an application job that previously failed.
Text Completion
Generates simple text completions for a single, straightforward prompt.
Translate Text
Converts written text from one supported language into another.
Upload File
Uploads a file to the platform, making it available for graph building or vision...
Web Search
Performs a search query against the live web for current information.
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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 24 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Problem: Finding a single source of truth in corporate documents is manual, messy, and slow., Solved with Vinkius AI Gateway
Today, answering a complex question means clicking through three different departmental SharePoint sites. You download the policy guide PDF, copy charts from an Excel sheet, find the relevant Jira ticket, and then paste all that disparate information into a single email draft for review. It's a massive amount of copying, pasting, and cross-referencing.
With this MCP, you eliminate the manual gathering process. You upload your entire document corpus once, creating structured Knowledge Graphs. Now, when an agent answers a question using `ask_question`, it synthesizes facts from those disparate sources instantly, giving you a citation trail right in the answer.
Graphing Your Data with `add_file_to_graph`
The manual step of mapping relationships—figuring out that 'Project Alpha' mentioned on Page 3 of the PDF actually relates to 'Budget Q4' in the spreadsheet—is lost. You have to do it manually, drawing diagrams and linking concepts.
By using `add_file_to_graph`, the system automatically builds those connections for you. It sees the link between your files and structures that knowledge so your AI agent can query relationships, not just keywords.
What your AI can actually do with this
Need to generate content from private company data? This MCP lets your agent access Writer’s powerful LLMs, enabling you to build sophisticated applications directly on your enterprise knowledge. Instead of just generating text, the system manages complex Knowledge Graphs and processes internal documents, allowing it to answer questions using facts pulled only from your own source material.
You can upload files, analyze images inside those files, or even run scheduled tasks through no-code application generators. When you connect this MCP via Vinkius, credentials pass through a zero-trust proxy; that means your sensitive keys are used only in transit and never stored on disk, keeping your data secure while letting your AI agent work across private enterprise resources.
019e5d67-b871-72ee-ba31-ca103df680b4 Here's how it actually works
The bottom line is that it lets your AI client treat private enterprise documents and complex relationships as first-class data sources for generation.
Subscribe to this MCP and enter your Writer API key.
The AI agent can then use tools like create_graph or upload_file to ingest your data into the system.
Finally, you instruct the agent to use a tool like ask_question to query the newly built knowledge graph for an answer.
Who is this actually for?
This connector targets technical teams dealing with huge amounts of proprietary content. Think Data Engineers who need to build RAG pipelines, or Solution Architects building internal knowledge tools. If your company's intelligence lives in PDFs and confluence pages, this is for you.
Building data ingestion pipelines by using upload_file and add_file_to_graph to prepare structured context for model consumption.
Automating the drafting of technical manuals or internal whitepapers that must cite specific facts found in company documents.
Designing and testing complex, multi-step automated workflows that combine data retrieval with text generation using generate_application_content.
What Changes When You Connect
Build reliable, fact-checked answers. By using add_file_to_graph and then calling ask_question, you guarantee the AI agent bases its response on your specific internal files, not general web knowledge.
Handle complex document pipelines efficiently. Uploading documents via upload_file lets you gather context for both Knowledge Graph building and immediate analysis using analyze_vision.
Automate multi-stage content creation. If a task requires multiple steps—like fetching data, processing it, then writing a report—you can queue the process with generate_application_content_async, getting notified when it’s done via get_application_job.
Maintain security while scaling up. Since Vinkius handles all MCP execution inside its own sandbox and uses a zero-trust proxy, you know your API keys are secure in transit, no matter how many tools you chain together.
Improve cost management across the board. Every tool call benefits from Vinkius's native token optimization, which cuts down on unnecessary costs compared to running these same operations without it.
See it in action
Retrieving Policy Details
A compliance officer needs to know the current PTO policy for remote workers. Instead of searching through fifty HR PDFs, they ask their agent a question. The agent uses ask_question against the 'HR Manual' Knowledge Graph and provides a direct quote from the correct document.
Analyzing Competitor Reports
A market analyst uploads three competitor annual reports using upload_file. They then use analyze_vision on the charts within those PDFs, followed by asking the agent to summarize key risks across all documents.
Building a Training Module
A technical writer needs content based on product specs. They first list available models using list_models, then use text_completion for drafts, and finally run the entire sequence through an application job started by generate_application_content_async.
Cleaning Up Data Context
A data engineer realizes a retired project document is polluting a graph. They use list_graphs to identify the wrong knowledge base, then call remove_file_from_graph on the specific file ID to clean up the context.
The honest tradeoffs
Treating LLMs like search engines
Asking the agent a broad question without first building or pointing it toward relevant internal documents. The AI will hallucinate or give general knowledge.
Before asking, always use upload_file and then build context by running add_file_to_graph. Then, execute the query using the dedicated tool: ask_question.
Forgetting job status
Kicking off a large content generation task with generate_application_content_async and assuming the result is immediately available. The agent will fail because it's running in the background.
After starting the job, you must poll for results using get_application_job until the status returns 'complete'. You can even use list_application_jobs to check historical runs.
Confusing text generation methods
Using basic text_completion when you actually need a multi-step, structured process involving file analysis and graph querying. The simple tool lacks the necessary context management.
For complex tasks, use the application tools: start with get_application to define the workflow, then trigger it using generate_application_content_async.
When It Fits, When It Doesn't
Use this MCP if your core problem involves generating content or answering questions based on private, complex, and varied data sources (e.g., PDFs, charts, internal databases). If the information you need is spread across multiple file types and requires relationship mapping, this connector is necessary. Don't use it if all you need is a quick fact-check against current public events; for that, stick to web_search. Also, if your process involves complex, repeatable steps (like 'download file X, run analysis Y, then write report Z'), the application tools are superior to simple text generation. This MCP handles data ingestion and workflow orchestration—it's built for deep enterprise integration.
Questions you might have
How do I make sure my LLM only uses my internal documents? (Tool: ask_question) +
You must first build a Knowledge Graph by uploading files and using add_file_to_graph. The ask_question tool is designed to query only that specific graph, ignoring external web knowledge.
Can I run a multi-step process automatically? (Tool: generate_application_content_async) +
Yes. Use generate_application_content_async to kick off workflows. This is necessary for tasks that require multiple steps, like first analyzing an image with analyze_vision, then writing text based on the result.
I uploaded a PDF; how do I make sure it's available? (Tool: list_files) +
Use list_files to get a paginated inventory of every file ID and name. This confirms the upload succeeded before you try to add it to a graph or analyze it.
What's the difference between chat_completion and text_completion? (Tool: chat_completion) +
Use chat_completion for conversational interactions, where the history of the conversation matters. Use text_completion when you just need a single block of generated text based on one prompt.
How do I delete old files from my knowledge base? (Tool: remove_file_from_graph) +
Don't just delete the file; use remove_file_from_graph. This tool cleanly severs the connection to the graph, keeping your data clean while preserving the original document.
If I need to permanently remove a file, what are the steps involved when using `delete_file`? +
The file is immediately and irrevocably deleted from your system. This action removes both the binary content and all associated metadata records for that file within your connected knowledge graph.
How do I check which LLM models are available before running a chat completion? Should I use `list_models`? +
Yes, you must call list_models to see all writer-supported versions. This ensures your agent uses the optimal model—like Palmyra-X5 or a specific size—for the job at hand.
If I have an image or document that isn't text, how do I process it for context using `analyze_vision`? +
You simply pass the visual file directly to analyze_vision. The service interprets the images and documents, generating a structured, descriptive text output your agent can then use for content generation.
How do I see which Palmyra models are available in my account? +
Use the list_models tool. It will return a list of all active models in your Writer account, such as palmyra-x5 or palmyra-med.
Can I use my own documents for AI responses? +
Yes. First, use upload_file to add your documents, then create_graph and add_file_to_graph to build a Knowledge Graph for RAG-based querying.
Does this support conversational history? +
Yes, the chat_completion tool accepts a messages array, allowing you to maintain full context for multi-turn dialogues with the model.
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