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CodeDreamer06

MonkeyType MCP Server

get_psas

Retrieve public service announcements from MonkeyType to access important community updates and notifications.

Instructions

Get public service announcements

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The switch case handler for the 'get_psas' tool. It calls the MonkeyType API endpoint '/psas' with GET method using the shared apiKey and returns the JSON-stringified result as tool content.
    case "get_psas": {
      const result = await callMonkeyTypeApi('/psas', 'GET', apiKey);
      return {
        content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
      };
    }
  • Zod input schema definition for the 'get_psas' tool. Extends BaseApiSchema with no additional parameters.
    const GetPsasSchema = BaseApiSchema.extend({});
  • server.js:256-260 (registration)
    Tool registration object for 'get_psas' in the ListToolsRequest handler, including name, description, and input schema.
    {
      name: "get_psas",
      description: "Get public service announcements",
      inputSchema: zodToJsonSchema(GetPsasSchema),
    },
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'Get' implying a read operation, but doesn't disclose behavioral traits like authentication needs, rate limits, response format, or whether it returns all PSAs or a filtered subset. For a tool with zero annotation coverage, this minimal description is inadequate.

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, clear sentence with zero waste. It's appropriately sized and front-loaded, efficiently conveying the core purpose without 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 no annotations, no output schema, and a simple tool with 0 parameters, the description is incomplete. It doesn't explain what 'public service announcements' entail, the return format, or any behavioral context. For even a simple tool, more information is needed to guide effective use.

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 input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, but since there are no parameters, this is acceptable. Baseline is 4 for 0 parameters, as no compensation is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

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

The description 'Get public service announcements' states the action (get) and resource (public service announcements), providing a basic purpose. However, it lacks specificity about scope or format, and doesn't differentiate from sibling tools like 'get_configuration' or 'get_stats' that also retrieve information. It's not tautological but remains vague about what exactly is retrieved.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention context, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone. With multiple sibling tools for retrieving data, this lack of differentiation is a significant gap.

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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