Supercharge your AI with Cognee. Turn documents into relationship-aware knowledge.
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Cognee builds structured knowledge graphs from messy, unstructured data. Ingest documents and text, automatically extract relationships between concepts, and query your entire dataset using graph-aware AI reasoning, going far beyond simple keyword search.
What your AI can do
Cognee add data
Loads raw text or documents into the Cognee knowledge base, preparing them for graph construction.
Cognee cognify
Processes ingested data by extracting entities and relationships to build a structured, searchable knowledge graph.
Cognee get insights
Retrieves specific, structured entity relationship sets to map out how concepts connect across the entire knowledge base.
Loads documents, articles, or data streams into the MCP's knowledge storage.
Analyzes stored data to automatically extract entities and map the connections between them.
Retrieves answers by traversing the graph structure, showing how multiple facts relate in a single query response.
Pulls out structured relationship sets to visualize connections across your entire knowledge pool.
Answers natural language questions using a hybrid approach that combines graph traversal with semantic vector matching.
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Compatible AI Apps
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Cognee: 4 Tools for Structured Knowledge Graphs
These four tools let your agent handle the entire knowledge pipeline: from adding raw text to extracting relationships and finally querying complex insights.
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 Cognee on VinkiusCognee Add Data
Loads raw text or documents into the Cognee knowledge base, preparing them for graph construction.
Cognee Cognify
Processes ingested data by extracting entities and relationships to build a...
Cognee Get Insights
Retrieves specific, structured entity relationship sets to map out how concepts...
Cognee Search
Answers natural language questions by querying the graph structure and combining...
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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
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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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 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Sifting through mountains of documents takes forever.
Right now, if you want to know the relationship between three different concepts mentioned across ten years of reports, you spend hours downloading PDFs, opening tabs, and copy-pasting key names into a spreadsheet just to start drawing lines. The whole process is manual, tedious, and prone to missing subtle connections.
With this MCP, you feed all those documents in one go. Your agent processes them, building an internal map of how every concept links up. You stop guessing and start asking—you get the answer, complete with evidence showing exactly which facts are connected.
cognee_get_insights: See the whole picture.
Before this, if you found one key concept in a report, finding all related concepts required starting a new search query every single time. You couldn't see the broader web of influence or dependency; it was always isolated information.
Now, you use `cognee_get_insights` and the MCP maps out the entire structure for you. It’s not just telling you facts; it's showing you the architecture of knowledge itself.
What your AI can actually do with this
Your agent can connect to Cognee to turn raw documents and research papers into a truly organized knowledge base. Instead of just spitting back chunks of text, this MCP figures out how ideas relate to each other—who worked with whom, what concepts are foundational to others, or which events influenced subsequent findings.
You first feed the system your unstructured data; then, it processes that raw material into a structured graph, mapping out every entity and relationship automatically. Once built, you can ask questions and get answers that trace connections across disparate sources. This capability is critical for deep research work, making Vinkius the central point where complex knowledge pipelines run.
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Here's how it actually works
The bottom line is you move from having scattered documents to having an active map of knowledge that your agent can navigate.
First, use the tool to ingest your raw documents or text into the knowledge base.
Next, run the processing step. This builds the structured graph by extracting entities and defining relationships from the ingested data.
Finally, query the system using a natural language question; the MCP returns context-aware answers based on traversing those defined connections.
Who is this actually for?
Research analysts who spend hours sifting through PDFs, or ML engineers building complex data pipelines. If your job involves connecting disparate pieces of information—like tracing a concept from an old paper to a modern product—you need this.
Uses the MCP to process dozens of academic papers, finding connections between authors, theories, and dates that aren't explicitly mentioned together.
Connects internal documentation sources—meeting minutes, technical specs, and client reports—to build a unified graph of company knowledge.
Builds advanced RAG pipelines that require more than simple vector search; they need to understand the dependency structure between data points.
What Changes When You Connect
The cognee_cognify tool builds a graph that understands how facts connect, not just what they say. This means your agent sees the foundational links between concepts automatically.
When you query using the cognee_search function, you get context-aware answers from graph traversal and semantic search combined. It’s way smarter than standard document retrieval.
Need to see how different topics relate? Use cognee_get_insights. This pulls out structured entity relationships, letting you visualize hidden connections across your knowledge pool.
The process starts simply: use the initial tool to load raw text and documents into the system. You don't have to manually clean or structure anything first.
It tracks time too. The MCP has temporal awareness, so it can reason over facts based on when they were added or discovered.
See it in action
Analyzing historical research papers
A historian uploads several decades of academic articles. Instead of manually cross-referencing authors and theories, the agent runs cognee_cognify to map all relationships. The user then uses cognee_get_insights to discover that three seemingly unrelated concepts were actually introduced by the same person thirty years apart.
Onboarding new technical staff
A company uploads all its internal documentation, specs, and client reports. The agent uses cognee_add_data followed by processing to build a single knowledge graph. New hires can then ask complex questions via the MCP, letting the system retrieve context-aware answers using cognee_search.
Debugging data pipelines
An ML engineer feeds the MCP raw log files and database schemas. The agent uses cognee_cognify to map dependencies, allowing the engineer to pinpoint exactly which upstream data failure caused a downstream pipeline error.
The honest tradeoffs
Using RAG for complex relationships
A user asks: 'What are the three main causes of Model X, and what foundational technology was used in 2015?' Standard retrieval might pull three documents that mention those things separately. The AI reads them but can’t connect the timeline.
You must use cognee_add_data first, then run the graph processing step to build relationships. Finally, use cognee_search. Only the MCP knows how to trace a relationship across time and documents.
Thinking data is clean before ingestion
A user assumes that because they are uploading PDFs, all the text will be perfectly structured. They run cognee_cognify but get an output filled with errors or disconnected concepts.
Always treat raw input as messy. Use cognee_add_data to load everything first. Then let the MCP’s graph processing handle the cleanup and structure building.
When It Fits, When It Doesn't
Use this MCP if your data problem is about connections, not just content volume. If you need an AI agent to answer questions that require understanding how one concept influenced another, or how multiple documents relate over time, use Cognee. Don't use it if all you need is a basic search function on simple text files—a standard vector store will suffice there. But don't underestimate the power of the graph; for deep research and knowledge synthesis, this MCP is necessary.
Questions you might have
How do I start with cognee_add_data? +
You use cognee_add_data to ingest your raw files or text first. This is just the storage step; it loads the data but doesn't build any relationships yet.
Is cognee_cognify better than standard embedding? +
Yes. Standard embeddings only capture similarity of words, while cognee_cognify explicitly extracts and maps entities and their defined relationships, giving you a deeper understanding of the data.
What does cognee_search actually do? +
cognee_search takes your natural language question and runs it against the full graph. It doesn't just find keywords; it follows the established relationships to build a fully context-aware answer.
Can I use cognee_get_insights on data I added yesterday? +
Yes, as long as you have already run cognee_cognify since adding that data. The MCP needs the relationships built into the graph before it can retrieve insights.
What data formats can I pass to cognee_add_data? +
It handles multiple file types including raw text, PDFs, and structured documents. The tool processes the input, so you don't need to worry about manual parsing or cleaning before ingestion.
If my data has ambiguous relationships, what does cognee_cognify do? +
It uses advanced reasoning to infer likely connections and build a draft graph. For critical or complex relationships, you should review the generated structure in the knowledge view for confirmation.
Is the data accessed by cognee_get_insights secure and private to my account? +
Absolutely. All insights are strictly limited to the datasets associated with your API key. Your access remains isolated from other users' knowledge graphs.
Does the search time for cognee_search increase dramatically as my graph grows? +
No, it doesn't. Because the tool combines vector similarity with targeted graph traversal, query performance stays consistent even when searching across massive, complex knowledge bases.
How is Cognee different from standard RAG? +
Standard RAG splits documents into chunks and finds similar text using vector search — but it loses the relationships between facts. Cognee builds a knowledge graph that preserves entity relationships, temporal connections, and hierarchical structures. When you search, Cognee uses graph traversal combined with vector similarity and LLM reasoning, resulting in more accurate, context-aware answers that understand HOW facts relate to each other.
What search types are available? +
Cognee supports four retrieval strategies: GRAPH_COMPLETION (default — combines vector search + graph traversal + LLM reasoning for context-aware answers), SUMMARIES (fast hierarchical overview search), INSIGHTS (structured entity relationships), and CHUNKS (pure vector similarity for raw text passages). Each strategy optimizes for different use cases.
Is Cognee open-source? +
Yes! Cognee is fully open-source under the Apache 2.0 license. You can self-host the entire platform including the knowledge graph engine, vector database, and API server. A managed cloud version with API keys is also available for teams that prefer not to manage infrastructure.
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