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CodeDreamer06

MonkeyType MCP Server

get_last_result

Retrieve your most recent typing test results to track speed and accuracy progress.

Instructions

Get user's last typing test result

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler case for the 'get_last_result' tool. It calls the MonkeyType API endpoint '/results/last' using the shared callMonkeyTypeApi function and returns the JSON-stringified result as tool output.
    case "get_last_result": {
      const result = await callMonkeyTypeApi('/results/last', 'GET', apiKey);
      return {
        content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
      };
    }
  • Zod input schema for the 'get_last_result' tool. Extends BaseApiSchema (empty object), indicating no input parameters are required.
    const GetLastResultSchema = BaseApiSchema.extend({});
  • server.js:215-219 (registration)
    Tool registration in the ListTools response, providing name, description, and input schema reference.
    {
      name: "get_last_result",
      description: "Get user's last typing test result",
      inputSchema: zodToJsonSchema(GetLastResultSchema),
    },
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. It states it 'gets' data, implying a read operation, but doesn't disclose behavioral traits like authentication needs, rate limits, error conditions, or what 'last' means chronologically. The description is minimal and lacks essential context for safe invocation.

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 with zero waste. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 no annotations, no output schema, and a read operation, the description is incomplete. It doesn't explain what the return value includes (e.g., speed, accuracy), how 'last' is determined, or any error handling. For a tool with contextual complexity, this leaves significant gaps for the agent.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter details, but that's appropriate here. A baseline of 4 is applied as it meets the requirement for a parameterless tool.

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 verb ('Get') and resource ('user's last typing test result'), making the purpose specific and understandable. It distinguishes from siblings like 'get_results' (multiple results) and 'get_result_by_id' (specific ID), though it doesn't explicitly name these alternatives.

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 'get_results' or 'get_result_by_id'. It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from the 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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