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FallseF

Fusion MCP Server

by FallseF

fusion_analyze

Analyze data using fusion algorithms with statistical, machine learning, or hybrid methods to process and transform information for enhanced analytical insights.

Instructions

Analyze data using fusion algorithms

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesData to analyze
methodNoAnalysis method to use

Implementation Reference

  • The main handler function that executes the logic for the 'fusion_analyze' tool. It destructures the input arguments, performs a simulated analysis, and returns a structured text response.
    async handleAnalyze(args) {
      const { data, method = "statistical" } = args;
    
      // Simulate analysis
      const result = {
        method,
        dataLength: data.length,
        analysis: `Analyzed using ${method} method`,
        timestamp: new Date().toISOString(),
      };
    
      return {
        content: [
          {
            type: "text",
            text: `Analysis complete:\n${JSON.stringify(result, null, 2)}`,
          },
        ],
      };
    }
  • Defines the input schema for the 'fusion_analyze' tool, specifying the expected parameters 'data' (required string) and optional 'method' (enum).
    inputSchema: {
      type: "object",
      properties: {
        data: {
          type: "string",
          description: "Data to analyze",
        },
        method: {
          type: "string",
          enum: ["statistical", "ml", "hybrid"],
          description: "Analysis method to use",
        },
      },
      required: ["data"],
    },
  • src/index.js:37-55 (registration)
    Registers the 'fusion_analyze' tool in the ListTools response, including its name, description, and input schema.
    {
      name: "fusion_analyze",
      description: "Analyze data using fusion algorithms",
      inputSchema: {
        type: "object",
        properties: {
          data: {
            type: "string",
            description: "Data to analyze",
          },
          method: {
            type: "string",
            enum: ["statistical", "ml", "hybrid"],
            description: "Analysis method to use",
          },
        },
        required: ["data"],
      },
    },
  • src/index.js:83-85 (registration)
    Registers the handler for 'fusion_analyze' in the switch statement within the CallToolRequestSchema handler.
    case "fusion_analyze":
      return await this.handleAnalyze(args);
    case "fusion_transform":
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 mentions 'analyze' but doesn't specify whether this is a read-only operation, if it modifies data, requires authentication, has rate limits, or what the output looks like. The description is too vague to understand the tool's behavior beyond the basic action.

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

Conciseness4/5

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

The description is a single, straightforward sentence with no wasted words. It's appropriately concise for a simple tool, though it could be more informative. The structure is clear but lacks depth that might be needed for better understanding.

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 complexity implied by 'fusion algorithms' and the lack of annotations and output schema, the description is insufficient. It doesn't explain what analysis results to expect, how 'fusion' differs from other methods, or any behavioral traits. For a tool with two parameters and no structured output information, more context is needed to be complete.

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

Parameters3/5

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

The input schema has 100% description coverage, with clear documentation for both parameters ('data' and 'method' with enum values). The description doesn't add any meaningful information beyond what's in the schema—it doesn't explain what 'fusion algorithms' entail or how they relate to the parameters. Since schema coverage is high, the baseline score of 3 is appropriate.

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

Purpose3/5

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

The description states the tool 'Analyze data using fusion algorithms', which provides a basic purpose (analyzing data) and mentions the technique (fusion algorithms). However, it lacks specificity about what kind of analysis is performed or what 'fusion' means in this context. It doesn't clearly distinguish from the sibling tool 'fusion_transform', which might also process data.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'fusion_transform' or suggest scenarios where analysis is preferred over transformation. There's no information about prerequisites, input data formats, or expected outcomes that would help an agent decide when to invoke it.

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

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