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encodeBase64

Convert text or data into Base64 format using this tool in the mcp-helper-tools server. Ideal for encoding inputs securely and efficiently.

Instructions

Encode input data to Base64

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesData to encode

Implementation Reference

  • The asynchronous handler function that encodes the input string to Base64 using Node.js Buffer API and returns the result in the standard MCP tool response format.
    handler: async ({ input }: { input: string }) => {
      const encoded = Buffer.from(input).toString('base64');
      return {
        content: [
          {
            type: 'text',
            text: encoded
          }
        ]
      };
    }
  • Defines the input schema for the encodeBase64 tool, requiring an object with a single 'input' property of type string.
    inputSchema: {
      type: 'object',
      properties: {
        input: {
          type: 'string',
          description: 'Data to encode'
        }
      },
      required: ['input']
    },
  • src/index.ts:27-33 (registration)
    Registers encodeBase64 (via encodingTools) into the allTools collection, which is used by the MCP server's ListTools and CallTool request handlers to expose and execute the tool.
    const allTools: ToolKit = {
      ...encodingTools,
      ...geoTools,
      ...generatorTools,
      ...dateTimeTools,
      ...securityTools
    };
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. While 'Encode' implies a transformation rather than a destructive operation, the description doesn't mention any behavioral traits such as error handling, performance characteristics, or what happens with invalid input. It lacks details about the output format or any constraints.

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 extremely concise with a single, clear sentence that directly states the tool's function. There is no wasted language or unnecessary elaboration, making it front-loaded and efficient.

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 the tool returns (e.g., a Base64 string), how errors are handled, or any usage limitations. For a transformation tool, this leaves significant gaps in understanding its behavior and output.

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 the parameter 'input' documented as 'Data to encode'. The description adds no additional meaning beyond this, as it doesn't elaborate on what constitutes valid 'input data' (e.g., text, binary data) or any formatting requirements. This meets the baseline score when schema coverage is high.

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 tool's purpose with a specific verb ('Encode') and resource ('input data'), and specifies the encoding format ('Base64'). However, it doesn't explicitly distinguish this tool from its sibling 'decodeBase64' beyond the obvious encoding vs. decoding distinction, which is why it doesn't reach a perfect score.

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 like 'encodeHtml' or 'encodeUrl', nor does it mention any prerequisites or context for usage. It simply states what the tool does without indicating appropriate scenarios.

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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