# Cognee MCP for AI Agents AI Agent Connect

> Cognee builds knowledge graphs from messy data so your agent can reason through complex relationships instead of just grabbing random text chunks. It handles the heavy lifting of extracting entities and mapping how they connect, giving you a structured map of your information.

## Overview
- **Category:** ai-frontier
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_c6i3hXOYfoGrkpkYTLcBWkNZG3FKUa2WjFOiOwHJ/ai-agent-connect
- **Tags:** knowledge-graph, unstructured-data, graph-database, ai-reasoning, data-ingestion, semantic-search

## Description

Imagine you have thousands of research papers, meeting notes, and internal docs scattered everywhere. Standard search usually just finds words that look similar, but it often misses the why or the how behind the facts. Cognee changes that by turning that pile of text into a structured knowledge graph. Instead of your agent just looking at isolated pieces of data, it can actually trace connections between different concepts, people, and events. When you use this Connector through the Vinkius catalog, you get a system that understands that Project X is related to Person Y because of Contract Z, even if those words are not in the same paragraph. It combines the best of vector search with the logic of graph traversal, so your agent can give you summaries that actually make sense or find hidden links you did not know existed. It is about moving from find me this word to explain how these things are connected. This setup allows your agent to navigate your specific data with a level of logic that simple document retrieval cannot match.

## Tools

### cognee_add_data
Upload raw text or documents into a specific dataset for processing. This is the first step to building your knowledge graph.

### cognee_cognify
Turn your uploaded data into a structured graph by extracting entities and relationships. This creates the searchable structure.

### cognee_get_insights
Pull structured views of how different topics and entities connect across your data. It helps find hidden links in your knowledge base.

### cognee_search
Ask natural language questions to get context-aware answers. It uses graph traversal to find relevant connections.

## Prompt Examples

**Prompt:** 
```
What are the main connections between the new project's budget and the hardware requirements?
```

**Response:** 
```
Based on the knowledge graph, here are the connections:

| Project Component | Related Entity | Connection Type |
| :--- | :--- | :--- |
| **Budget Line A** | Server Rack 4 | Funding Source |
| **Hardware Req B** | GPU Cluster | Dependent On |

**Key Insight:** The budget for Rack 4 is directly tied to the GPU Cluster requirements, which were finalized in the Q3 planning doc.
```

**Prompt:** 
```
Summarize the evolution of our privacy policy over the last three years.
```

**Response:** 
```
The privacy policy has shifted significantly over three years:

*   **2021:** Focused on basic data collection for internal use.
*   **2022:** Added clauses for third-party sharing with encryption requirements.
*   **2023:** Transitioned to a user-centric model with explicit opt-in for all tracking.

**Trend:** The focus has moved from 'what we can collect' to 'how users control it'.
```

**Prompt:** 
```
Who is the primary contact for the logistics wing and what projects are they leading?
```

**Response:** 
```
The primary contact for the logistics wing is **Sarah Jenkins**. 

She is currently leading the following projects:
*   **Route Optimization Alpha** (Status: Active)
*   **Warehouse Automation Phase 2** (Status: Planning)
*   **Last-Mile Delivery Integration** (Status: Completed)
```

## Capabilities

### Ingest raw documents
Upload text or files into named datasets for processing.

### Extract entities and relationships
Turn messy text into a structured map of connections.

### Query the graph naturally
Ask questions and get context-aware answers using graph traversal.

### Retrieve structured insights
Get a clear view of how different topics and concepts link together.

### Perform hybrid searches
Combine semantic vector search with logical graph navigation.

### Reason over time-based facts
Track and analyze how information has changed over time.

## Use Cases

### Connecting research dots
A scientist wants to know how three different drugs might interact based on 500 separate papers using cognee_search.

### Corporate wiki navigation
An HR lead needs to see all connections between employees, projects, and specific software permissions using cognee_get_insights.

### Legal discovery
A lawyer asks the agent to find all mentions of a specific company and see which other entities it is linked to in contracts.

### Technical troubleshooting
A dev wants to see the relationship between a specific microservice error and the hardware it runs on.

## Benefits

- Get better recall by combining vector search with graph traversal using cognee_search.
- Stop missing context by letting your agent understand how facts connect with cognee_cognify.
- Find hidden connections in your data quickly using cognee_get_insights.
- Handle messy data easily by first using cognee_add_data to organize your source material.
- Reason over time-based facts to see how information has changed over months or years.

## How It Works

The bottom line is you turn messy documents into a structured map your agent can actually navigate.

1. Add your API key to the Connector and choose a dataset name.
2. Upload your raw text or documents to the system.
3. Run the processing step to build your searchable graph.

## Frequently Asked Questions

**How does Cognee help with my messy notes?**
Cognee turns your messy notes into a structured map. It identifies the people, places, and events in your notes and draws lines between them so your agent can understand the full picture.

**Can Cognee find hidden connections?**
Yes, it can. By building a knowledge graph, it allows your agent to follow paths between different pieces of data that might not share any common words but are logically linked.

**Is Cognee better than regular search?**
It is more context-aware. While regular search finds words, Cognee helps your agent understand the relationships between those words, leading to much more accurate and useful answers.

**Can I use Cognee for research papers?**
It is excellent for research. It can connect theories, authors, and dates across hundreds of papers, helping you see how different ideas evolved or influenced one another.

**How does Cognee handle time?**
Cognee tracks when facts were added to the graph. This allows your agent to reason over time-based connections and see how information has changed or progressed.

**Does Cognee work with my existing data?**
Yes, you can ingest any raw text, documents, or structured data you already have. It then processes that data to build your custom knowledge graph.

**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.