WolframAlpha LLM MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ask_llmC | Ask WolframAlpha a query and get LLM-optimized structured response with multiple formats |
| get_simple_answerB | Get a simplified, LLM-friendly answer focusing on the most relevant information |
| validate_keyB | Validate the WolframAlpha LLM API key |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
The tools 'ask_llm' and 'get_simple_answer' have overlapping purposes—both process WolframAlpha queries to return answers, with only subtle differences in output format. An agent could easily confuse them, as the descriptions don't clearly delineate distinct use cases, leading to potential misselection.
The naming follows a consistent snake_case pattern across all tools, with clear verb_noun structures (e.g., ask_llm, get_simple_answer). However, 'validate_key' deviates slightly by not directly involving query processing, though it maintains the same stylistic convention.
With only 3 tools, the count feels thin for a server named 'WolframAlpha LLM MCP Server', which suggests broader functionality. While the tools cover core querying and key validation, the limited number may restrict agent capabilities in handling diverse WolframAlpha tasks.
The tool surface is significantly incomplete for interacting with WolframAlpha's capabilities. It lacks operations for different query types (e.g., computational, visual, step-by-step), error handling, or advanced features, leaving obvious gaps that could cause agent failures in complex scenarios.