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decodeBase64

Decode Base64 encoded data quickly and accurately using this MCP-helper-tools utility. Input your Base64 string to retrieve the original decoded content.

Instructions

Decode Base64 input data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesData to decode

Implementation Reference

  • The handler function that executes the decodeBase64 tool logic, decoding the input string from base64 using Node.js Buffer and returning it as text content.
    handler: async ({ input }: { input: string }) => {
      const decoded = Buffer.from(input, 'base64').toString('utf-8');
      return {
        content: [
          {
            type: 'text',
            text: decoded
          }
        ]
      };
    }
  • The input schema defining the expected arguments for the decodeBase64 tool: an object with a required 'input' string.
    inputSchema: {
      type: 'object',
      properties: {
        input: {
          type: 'string',
          description: 'Data to decode'
        }
      },
      required: ['input']
    },
  • src/index.ts:27-33 (registration)
    Registers the decodeBase64 tool (via encodingTools) by including it in the allTools collection used by the MCP server's listTools and callTool request handlers.
    const allTools: ToolKit = {
      ...encodingTools,
      ...geoTools,
      ...generatorTools,
      ...dateTimeTools,
      ...securityTools
    };
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves: error handling for invalid input, output format (string vs binary), character encoding assumptions, or performance characteristics. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational 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 extremely concise at just three words, front-loading the core purpose with zero wasted text. Every word earns its place by directly communicating the tool's function without redundancy or unnecessary elaboration.

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 tool's simplicity (1 parameter, 100% schema coverage) but lack of annotations and output schema, the description is incomplete. It doesn't explain what the decoded output looks like, error conditions, or encoding details. For a decoding operation, knowing the result format is critical, making this description insufficient despite the straightforward schema.

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?

Schema description coverage is 100%, with the single parameter 'input' documented as 'Data to decode'. The description adds no additional parameter semantics beyond what the schema provides. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even without parameter details in the description, which applies here.

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 ('decode') and resource ('Base64 input data'), making it immediately understandable. It distinguishes from siblings like 'encodeBase64' by specifying the opposite operation. However, it doesn't explicitly mention what the output is (decoded text/binary), which prevents 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. It doesn't mention when to choose decodeBase64 over decodeUrl or decodeHtml, nor does it specify prerequisites like ensuring the input is valid Base64. Without any usage context or exclusions, the agent must infer everything from the tool name alone.

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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