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CodeDreamer06

MonkeyType MCP Server

is_submission_enabled

Check if quote submission is enabled for typing tests on MonkeyType to verify whether users can submit new quotes for typing practice.

Instructions

Check if quote submission is enabled

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for the is_submission_enabled tool. It makes a GET request to the MonkeyType API endpoint '/quotes/submission-enabled' using the shared apiKey and returns the JSON response.
    case "is_submission_enabled": {
      const result = await callMonkeyTypeApi('/quotes/submission-enabled', 'GET', apiKey);
      return {
        content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
      };
    }
  • Zod input schema for the is_submission_enabled tool, extending BaseApiSchema with no additional parameters.
    const IsSubmissionEnabledSchema = BaseApiSchema.extend({});
  • server.js:263-267 (registration)
    Registration of the is_submission_enabled tool in the ListTools response, specifying name, description, and input schema.
    {
      name: "is_submission_enabled",
      description: "Check if quote submission is enabled",
      inputSchema: zodToJsonSchema(IsSubmissionEnabledSchema),
    },
Behavior2/5

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

With no annotations provided, the description carries full burden but only states what the tool does without disclosing behavioral traits. It doesn't mention whether this is a read-only operation, if it requires authentication, rate limits, or what the return value looks like (e.g., boolean, status object).

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

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 0-parameter check tool with no output schema, the description is minimally adequate but lacks completeness. It doesn't explain the return type or format, which is crucial for an agent to interpret results correctly, leaving gaps in contextual understanding.

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 there's no need for parameter explanation. The description appropriately focuses on the tool's purpose without redundant parameter details, earning a high baseline score.

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 ('Check') and resource ('quote submission'), making it immediately understandable. However, it doesn't differentiate from sibling tools, which are mostly data retrieval functions, so it doesn't reach the highest 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 prerequisites, context, or exclusions, leaving the agent to infer usage from the purpose 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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