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claus-92

Super Productivity MCP Server

by claus-92

get_status

Retrieve the current task and task-count status from your Super Productivity system. Check active task details and total task numbers to stay organized.

Instructions

Returns the current task and task-count status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The MCP tool handler for 'get_status'. It has no required parameters (empty schema {}) and calls SpClient.getStatus() to fetch the current task and task count, then returns the result as JSON.
    server.tool(
      "get_status",
      "Returns the current task and task-count status.",
      {},
      async () => {
        const status = await SpClient.getStatus();
        return ok(status);
      }
    );
  • The StatusSchema Zod schema used to validate the API response for /status. It validates currentTask (nullable Task) and taskCount (nullable number).
    const StatusSchema = z.object({
      currentTask: TaskSchema.nullish(),
      taskCount: z.number().nullish(),
    }).passthrough();
  • The SpClient.getStatus() method that makes the actual HTTP GET request to /status on the Super Productivity local API, returning a typed Status object.
    getStatus(): Promise<Status> {
      return request("/status", StatusSchema);
    },
  • src/index.ts:16-16 (registration)
    The tool is registered indirectly via registerTaskTools(server) which is called during server initialization.
    registerTaskTools(server);
Behavior2/5

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

With no annotations, the description carries full burden. It states the tool returns status, implying a read operation, but does not explicitly confirm it is read-only or disclose any other behavioral traits (e.g., side effects, permissions).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence of six words, which is concise and front-loaded. However, it could slightly expand without becoming verbose to add contextual value.

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?

The description lacks detail on the return value structure. There is no output schema, so the description should specify what fields are included (e.g., task id, count, status) but only mentions 'status' vaguely.

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 zero parameters, so no parameter documentation is needed. According to guidelines, this yields a baseline of 4; the description adds no param-specific information, but none is required.

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 returns 'current task and task-count status', specifying both the action and resource. However, it does not distinguish itself from sibling tools like get_current_task or health_check, which also return status-like information.

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. There is no mention of 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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