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Tutorial : cognee-mcp (#3025)
Co-authored-by: kevco <kevin.cojean@agysoft.fr> Co-authored-by: Rizel Scarlett <rizel@squareup.com>
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Kevin Cojean
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documentation/docs/mcp/cognee-mcp.md
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259
documentation/docs/mcp/cognee-mcp.md
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---
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title: Cognee Extension
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description: Add Cognee MCP Server as a Goose Extension
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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import CLIExtensionInstructions from '@site/src/components/CLIExtensionInstructions';
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This tutorial covers how to add the [Cognee MCP Server](https://github.com/topoteretes/cognee) as a Goose extension to enable knowledge graph memory capabilities, connecting to over 30 data sources for enhanced context and retrieval.
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:::tip TLDR
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**Command**
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```sh
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uv --directory /path/to/cognee-mcp run python src/server.py
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```
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**Environment Variables**
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```
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LLM_API_KEY: <YOUR_OPENAI_API_KEY>
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EMBEDDING_API_KEY: <YOUR_OPENAI_API_KEY>
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```
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:::
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## Configuration
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:::info
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Note that you'll need [uv](https://docs.astral.sh/uv/#installation) installed on your system to run this command, as it uses `uv`.
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:::
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<Tabs groupId="interface">
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<TabItem value="cli" label="Goose CLI" default>
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1. First, install Cognee:
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```bash
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# Clone and install Cognee
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git clone https://github.com/topoteretes/cognee
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cd cognee-mcp
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uv sync --dev --all-extras --reinstall
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# On Linux, install additional dependencies
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sudo apt install -y libpq-dev python3-dev
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```
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2. Run the `configure` command:
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```sh
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goose configure
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```
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3. Choose to add a `Command-line Extension`
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```sh
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┌ goose-configure
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│
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◇ What would you like to configure?
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│ Add Extension (Connect to a new extension)
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│
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◆ What type of extension would you like to add?
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│ ○ Built-in Extension
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// highlight-start
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│ ● Command-line Extension (Run a local command or script)
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// highlight-end
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│ ○ Remote Extension
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└
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```
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4. Give your extension a name
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```sh
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┌ goose-configure
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│
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◇ What would you like to configure?
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│ Add Extension (Connect to a new extension)
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│
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◇ What type of extension would you like to add?
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│ Command-line Extension
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│
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// highlight-start
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◆ What would you like to call this extension?
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│ Cognee
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// highlight-end
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└
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```
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5. Enter the command
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```sh
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┌ goose-configure
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│
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◇ What would you like to configure?
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│ Add Extension (Connect to a new extension)
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│
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◇ What type of extension would you like to add?
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│ Command-line Extension
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│
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◇ What would you like to call this extension?
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│ Cognee
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│
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// highlight-start
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◆ What command should be run?
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│ uv --directory /path/to/cognee-mcp run python src/server.py
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// highlight-end
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└
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```
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6. Enter the number of seconds Goose should wait for actions to complete before timing out. Default is 300s
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```sh
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┌ goose-configure
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│
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◇ What would you like to configure?
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│ Add Extension (Connect to a new extension)
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│
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◇ What type of extension would you like to add?
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│ Command-line Extension
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│
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◇ What would you like to call this extension?
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│ Cognee
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│
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◇ What command should be run?
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│ uv --directory /path/to/cognee-mcp run python src/server.py
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│
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// highlight-start
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◆ Please set the timeout for this tool (in secs):
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│ 300
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// highlight-end
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│
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└
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```
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7. Choose to add a description. If you select "Yes" here, you will be prompted to enter a description for the extension.
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```sh
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┌ goose-configure
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│
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◇ What would you like to configure?
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│ Add Extension (Connect to a new extension)
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│
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◇ What type of extension would you like to add?
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│ Command-line Extension
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│
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◇ What would you like to call this extension?
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│ Cognee
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│
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◇ What command should be run?
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│ uv --directory /path/to/cognee-mcp run python src/server.py
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│
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◇ Please set the timeout for this tool (in secs):
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│ 300
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│
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// highlight-start
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◇ Would you like to add a description?
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│ No
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// highlight-end
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│
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└
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```
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8. Add the required environment variables:
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:::info
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You'll need OpenAI API keys for both LLM and embedding models. [Get your API keys here](https://platform.openai.com/api-keys).
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:::
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```sh
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┌ goose-configure
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│
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◇ What would you like to configure?
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│ Add Extension (Connect to a new extension)
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│
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◇ What type of extension would you like to add?
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│ Command-line Extension
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│
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◇ What would you like to call this extension?
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│ Cognee
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│
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◇ What command should be run?
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│ uv --directory /path/to/cognee-mcp run python src/server.py
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│
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◇ Please set the timeout for this tool (in secs):
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│ 300
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│
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◇ Would you like to add a description?
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│ No
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│
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// highlight-start
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◆ Would you like to add environment variables?
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│ Yes
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│
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◇ Environment variable name:
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│ LLM_API_KEY
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│
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◇ Environment variable value:
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│ ▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
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│
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◇ Add another environment variable?
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│ Yes
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│
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◇ Environment variable name:
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│ EMBEDDING_API_KEY
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│
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◇ Environment variable value:
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│ ▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
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│
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◇ Add another environment variable?
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│ No
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// highlight-end
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└ Added Cognee extension
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```
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</TabItem>
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</Tabs>
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## Example Usage
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Cognee provides knowledge graph memory capabilities for Goose, allowing it to remember and connect information across conversations and documents.
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:::info LLM
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OpenAI's GPT-4o was used for this task.
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:::
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### Goose Prompt
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> _Goose, please cognify this information: "I prefer Python for data analysis and use pandas extensively. My current project involves analyzing customer behavior data." Then search for information about my programming preferences._
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### Goose Output
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:::note CLI
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<details>
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<summary>Tool Calls</summary>
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─── cognee_cognify | abcdefgh ──────────────────────────
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data: I prefer Python for data analysis and use pandas extensively. My current project involves analyzing customer behavior data.
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─── cognee_search | abcdefgh ──────────────────────────
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search_query: programming preferences Python data analysis
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search_type: GRAPH_COMPLETION
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</details>
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I'll help you store that information in the knowledge graph and then search for your programming preferences.
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First, let me cognify the information about your preferences:
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**Cognify Status:**
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Background process launched due to MCP timeout limitations. The information about your Python and pandas preferences for data analysis has been added to the knowledge graph.
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Now let me search for information about your programming preferences:
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**Search Results:**
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Based on the knowledge graph, I found information about your programming preferences:
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- **Language Preference**: You prefer Python for data analysis
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- **Key Libraries**: You use pandas extensively for data manipulation
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- **Current Focus**: Your current project involves analyzing customer behavior data
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- **Domain**: Data science and analytics workflows
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- **Technical Stack**: Python + pandas for data analysis tasks
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The knowledge graph has successfully stored and can now retrieve your programming preferences and project context for future conversations.
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:::
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