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tosea_wait_for_job

Monitor presentation job status until completion or failure, returning final results for automated presentation workflows.

Instructions

Poll a presentation job until completed or failed and return the final job payload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
presentation_idYes
timeout_secondsNo
poll_interval_secondsNo
max_poll_interval_secondsNo

Implementation Reference

  • The tool 'tosea_wait_for_job' is defined and implemented in src/tools.ts using the MCP server tool registration. It validates parameters and calls 'client.waitForJob' to perform the actual polling logic.
    server.tool(
      "tosea_wait_for_job",
      "Poll a presentation job until completed or failed and return the final job payload.",
      {
        presentation_id: z.string().uuid(),
        timeout_seconds: z.number().int().min(5).max(3600).default(900),
        poll_interval_seconds: z.number().int().min(1).max(60).default(2),
        max_poll_interval_seconds: z.number().int().min(1).max(120).default(10)
      },
      async ({
        presentation_id,
        timeout_seconds,
        poll_interval_seconds,
        max_poll_interval_seconds
      }) => {
        try {
          return asToolResult(
            await client.waitForJob(presentation_id, {
              timeoutMs: timeout_seconds * 1000,
              pollIntervalMs: poll_interval_seconds * 1000,
              maxPollIntervalMs: max_poll_interval_seconds * 1000
            })
          );
        } catch (error) {
          throw wrapToolError(error);
        }
      }
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions polling behavior and terminal states (completed/failed), but lacks critical details: whether this is a blocking call, how errors are handled, if it respects rate limits, what authentication is required, or what the 'final job payload' contains. For a polling tool with potential long-running operations, this is insufficient.

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 front-loads the core action and outcome with zero wasted words. Every element ('Poll... until... return...') earns its place, 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.

Completeness2/5

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

Given the complexity of a polling tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the polling algorithm, error conditions, return format, or how it interacts with other tools that create jobs. For proper agent use, more context about behavior and integration is needed.

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?

Schema description coverage is 0%, so the description must compensate but adds no parameter information beyond implying 'presentation_id' identifies the job. It doesn't explain what timeout_seconds, poll_interval_seconds, or max_poll_interval_seconds control operationally, leaving parameters semantically unclear. However, the schema provides constraints (min/max/defaults), establishing a baseline.

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 action ('Poll a presentation job') and outcome ('until completed or failed and return the final job payload'), distinguishing it from sibling tools that focus on editing, exporting, or listing rather than job monitoring. However, it doesn't specify what type of 'presentation job' this refers to (e.g., generation, export, rendering), which prevents a perfect 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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., that a job must be initiated first using another tool like tosea_pdf_to_presentation) or when not to use it (e.g., for immediate status checks without polling). This leaves the agent to infer context from sibling tool names 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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