Skip to main content
Glama
Garoth

WolframAlpha LLM MCP Server

by Garoth

validate_key

Verify the validity of a WolframAlpha LLM API key to ensure secure and authorized access for querying natural language questions.

Instructions

Validate the WolframAlpha LLM API key

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The 'validate_key' tool handler function. It calls wolframLLMService.validateApiKey() and returns a text response indicating if the API key is valid or invalid.
    handler: async (_args: EmptyArgs): Promise<ToolResponse> => {
      const isValid = await wolframLLMService.validateApiKey();
      return {
        content: [{
          type: "text",
          text: isValid ? "API key is valid" : "API key is invalid"
        }]
      };
    }
  • The core helper method in WolframLLMService class that validates the API key by making a test query to '2+2' and checking the response format.
    async validateApiKey(): Promise<boolean> {
      try {
        const params = new URLSearchParams({
          appid: this.config.appId,
          input: '2+2'
        });
    
        const response = await axios.get(`${this.baseUrl}?${params.toString()}`);
        return typeof response.data === 'string' && response.data.includes('Result:');
      } catch (error) {
        if (axios.isAxiosError(error) && error.response?.data) {
          console.error('API Key Validation Error Response:', error.response.data);
        }
        return false;
      }
    }
  • src/index.ts:26-29 (registration)
    MCP server capabilities registration declaring the 'validate_key' tool as available.
      ask_llm: true,
      get_simple_answer: true,
      validate_key: true
    },
  • src/index.ts:80-82 (registration)
    Specific handling in the MCP CallToolRequestSchema handler for the 'validate_key' tool, invoking it with empty args.
    if (tool.name === 'validate_key') {
      response = await (tool as Tool<EmptyArgs>).handler({});
    } else {
  • Input schema for the 'validate_key' tool, which requires no arguments.
    inputSchema: {
      type: "object",
      properties: {},
      required: []
    },
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('validate') but doesn't explain what validation entails, such as checking key format, connectivity, permissions, or returning success/failure status. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy to understand at a glance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what validation means, what the output might be (e.g., success/failure, error messages), or how it integrates with sibling tools. For a validation tool with no structured context, more detail is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, earning a baseline score of 4 for tools with no parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('validate') and the target resource ('WolframAlpha LLM API key'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'ask_llm' or 'get_simple_answer', which prevents a score of 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the sibling tools. The description lacks context about prerequisites, such as whether validation is needed before using other tools, or any explicit when/when-not scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Garoth/wolframalpha-llm-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server