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Jake-loranger

Algorand MCP Server

echo

Test connectivity and verify message transmission within the Algorand blockchain network by returning the exact input provided.

Instructions

Echo back the provided message

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesThe message to echo back

Implementation Reference

  • The handler function for the 'echo' tool, which parses the input arguments using EchoArgsSchema and returns the echoed message.
    case 'echo': {
        const parsed = EchoArgsSchema.parse(args);
        return {
            content: [
                {
                    type: 'text',
                    text: `Echo: ${parsed.message}`,
                },
            ],
        };
    }
  • Zod schema for validating the input arguments of the 'echo' tool.
    const EchoArgsSchema = z.object({
        message: z.string(),
    });
  • src/index.ts:100-113 (registration)
    Registration of the 'echo' tool in the TOOLS array, including its name, description, and JSON input schema.
    {
        name: 'echo',
        description: 'Echo back the provided message',
        inputSchema: {
            type: 'object',
            properties: {
                message: {
                    type: 'string',
                    description: 'The message to echo back',
                },
            },
            required: ['message'],
        },
    },
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 the tool echoes back a message, implying a read-only or output operation, but does not disclose any behavioral traits like whether it modifies data, requires authentication, has rate limits, or what the output format is. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 and front-loaded, consisting of a single sentence 'Echo back the provided message' that directly conveys the tool's function without any waste. Every word earns its place, making it efficient and easy to parse.

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 (one parameter, no output schema, no annotations), the description is minimal but incomplete. It lacks context about the tool's role in the server (e.g., among Algorand-related siblings), behavioral details, or output expectations. While the purpose is clear, the overall description does not provide enough information for confident use without additional inference.

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 'message' fully documented as 'The message to echo back'. The description adds no additional meaning beyond this, as it only repeats the parameter's purpose without providing extra context like formatting or constraints. With high schema coverage, 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.

Purpose4/5

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

The description 'Echo back the provided message' clearly states the tool's function with a specific verb ('echo back') and resource ('the provided message'), making the purpose immediately understandable. However, it does not explicitly differentiate from sibling tools, which include various Algorand-related operations like 'calculate' or 'send_payment', though the function is distinct enough that confusion is unlikely.

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 lacks context about its role among sibling tools, such as whether it's for testing, debugging, or simple output, and does not mention any prerequisites or exclusions. This leaves the agent without explicit usage instructions.

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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