# MCP-wolfram-alpha
A MCP server to connect to wolfram alpha API.
<a href="https://glama.ai/mcp/servers/q5fud9cttp">
<img width="380" height="200" src="https://glama.ai/mcp/servers/q5fud9cttp/badge" />
</a>
## Components
### Prompts
This is analogous to the `!wa` bang in duckduckgo search.
```python
def wa(query: str) -> f"Use wolfram alpha to answer the following question: {query}"
```
### Tools
Query Wolfram Alpha api.
```python
def query_wolfram_alpha(query: str) -> str
```
## Configuration
You **must** set the `WOLFRAM_API_KEY` environment variable. Get an api ket from [Wolfram Alpha](https://products.wolframalpha.com/api).
This was tested with the full results API, but it might not be required.
```json
{
"mcpServers": {
"MCP-wolfram-alpha": {
"command": "uv",
"args": [
"--directory",
"C:\\Users\\root\\Documents\\MCP-wolfram-alpha",
"run",
"MCP-wolfram-alpha"
],
"env": {
"WOLFRAM_API_KEY": "your-app-id"
}
}
}
}
```
## Development
### Debugging
Since the official MCP inspector does not have good environment support, I reccommend using wong2's [mcp-cli-inspector](https://github.com/wong2/mcp-cli).
Create a config.json file in the same style as claude desktop.
```json
{
"mcpServers": {
"MCP-wolfram-alpha": {
"command": "uv",
"args": [
"--directory",
"/full/path/to/MCP-wolfram-alpha",
"run",
"MCP-wolfram-alpha"
],
"env": {
"WOLFRAM_API_KEY": "your-app-id"
}
}
}
}
```
Then run:
```bash
npx @wong2/mcp-cli -c .\config.json
```