tkmind/tkmind_go
1
0
Fork 0
Code Issues Pull Requests Actions 4 Packages Projects Releases Wiki Activity

docs: update cognee, jetbrains, mbot extensions config (#5172)

Browse Source
This commit is contained in:
dianed-square
2025-10-15 12:28:24 -07:00
committed by GitHub
parent d607f7fe50
commit b23e1e6329
3 changed files with 46 additions and 47 deletions
Download Patch File Download Diff File Expand all files Collapse all files
documentation/docs/mcp/cognee-mcp.md
+7 -13
View File
@@ -7,7 +7,7 @@ import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import CLIExtensionInstructions from '@site/src/components/CLIExtensionInstructions';
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.
This tutorial covers how to add the [Cognee MCP Server](https://github.com/topoteretes/cognee/tree/main/cognee-mcp) as a Goose extension to enable knowledge graph memory capabilities, connecting to over 30 data sources for enhanced context and retrieval.
:::tip TLDR
**Command**
@@ -17,7 +17,6 @@ uv --directory /path/to/cognee-mcp run python src/server.py
**Environment Variables**
```
LLM_API_KEY: <YOUR_OPENAI_API_KEY>
EMBEDDING_API_KEY: <YOUR_OPENAI_API_KEY>
```
:::
@@ -34,7 +33,7 @@ Note that you'll need [uv](https://docs.astral.sh/uv/#installation) installed on
```bash
# Clone and install Cognee
git clone https://github.com/topoteretes/cognee
cd cognee-mcp
cd cognee/cognee-mcp
uv sync --dev --all-extras --reinstall
# On Linux, install additional dependencies
@@ -153,7 +152,7 @@ goose configure
8. Add the required environment variables:
:::info
You'll need OpenAI API keys for both LLM and embedding models. [Get your API keys here](https://platform.openai.com/api-keys).
You'll need an API key for your LLM provider. By default, this is an [OpenAI API key](https://platform.openai.com/api-keys).
:::
```sh
@@ -188,15 +187,6 @@ You'll need OpenAI API keys for both LLM and embedding models. [Get your API key
│ ▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
│
◇ Add another environment variable?
│ Yes
│
◇ Environment variable name:
│ EMBEDDING_API_KEY
│
◇ Environment variable value:
│ ▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
│
◇ Add another environment variable?
│ No
// highlight-end
└ Added Cognee extension
@@ -205,6 +195,10 @@ You'll need OpenAI API keys for both LLM and embedding models. [Get your API key
</TabItem>
</Tabs>
:::info
See the [Cognee MCP documentation](https://docs.cognee.ai/how-to-guides/deployment/mcp) for supported configuration options.
:::
## Example Usage
Cognee provides knowledge graph memory capabilities for Goose, allowing it to remember and connect information across conversations and documents.
documentation/docs/mcp/mbot-mcp.md
+2 -22
View File
@@ -13,18 +13,12 @@ import GooseDesktopInstaller from '@site/src/components/GooseDesktopInstaller';
This tutorial will get you started with [deemkeen's MQTT MCP server](https://github.com/deemkeen/mbotmcp) for the [MakeBlock mbot2 rover](https://www.makeblock.com/products/buy-mbot2), and outline some code changes we made along the way.
:::tip TLDR
<Tabs groupId="interface">
<TabItem value="ui" label="Goose Desktop" default>
[Launch the installer](goose://extension?cmd=/path/to/java&arg=-jar&arg=/path/to/mbotmcp-0.0.1-SNAPSHOT.jar&name=mbot2&description=mbot2&env=MQTT_SERVER_URI%3Dtcp://1.2.3.4:1883&env=MQTT_USERNAME%3Dyour_username&env=MQTT_PASSWORD%3Dyour_password)
</TabItem>
<TabItem value="cli" label="Goose CLI">
**Command**
```sh
/path/to/java -jar /path/to/mbotmcp-0.0.1-SNAPSHOT.jar
```
</TabItem>
</Tabs>
**Environment Variable**
**Environment Variables**
```
MQTT_SERVER_URI: tcp://1.2.3.4:1883
MQTT_PASSWORD: <string or blank>
@@ -35,20 +29,6 @@ This tutorial will get you started with [deemkeen's MQTT MCP server](https://git
## Configuration
<Tabs groupId="interface">
<TabItem value="ui" label="Goose Desktop" default>
<GooseDesktopInstaller
extensionId="mbot2"
extensionName="mbot2"
description="mbot2"
command="/path/to/java"
args={["-jar", "/path/to/mbotmcp-0.0.1-SNAPSHOT.jar"]}
envVars={[
{ name: "MQTT_SERVER_URI", label: "tcp://1.2.3.4:1883" },
{ name: "MQTT_USERNAME", label: "your_username" },
{ name: "MQTT_PASSWORD", label: "your_password" }
]}
/>
</TabItem>
<TabItem value="cli" label="Goose CLI">
1. Run the `configure` command:
```sh