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Check a running design job

get_job
Read-onlyIdempotent

Returns the state of a job started by design_board, edit_board, route_board, review_board or answer_question: status (running, needs_input, done, failed), recent progress, the question to put to the user when needs_input, and the result when done (parts, nets, routing, DRC counts, editor link). Waits up to wait_seconds for the job to finish before answering.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "job_id": "<job_id>"
    },
    "name": "get_job"
  }
}

Input Schema

NameRequiredDescriptionDefault
job_idYesThe job_id a tool returned.
wait_secondsNoSeconds to wait for completion (0-45, default 25).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds real behavioral context beyond them: it is a long-poll that waits up to wait_seconds before answering, and it enumerates the status values plus the needs_input question flow. It omits edge behavior such as expired/unknown job_id handling, keeping it short of a 5.

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?

Two sentences, zero waste, front-loaded with what is returned and then the blocking behavior. The dense enumeration of result fields is efficient rather than padded.

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

Completeness5/5

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

There is no output schema, so the description carries the return-value burden and does so by listing status values, progress, the pending question, and the done-state payload (parts, nets, routing, DRC counts, editor link). Combined with the wait semantics, an agent has everything needed to call and interpret it.

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?

Schema coverage is 100%, so baseline is 3, but the description adds meaning: it clarifies that wait_seconds makes the call block until the job finishes rather than being a simple timeout, and ties job_id to the producing tools. That is genuine value beyond the schema's 'Seconds to wait for completion'.

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

Purpose5/5

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

States a specific verb+resource ('Returns the state of a job') and enumerates exactly which sibling tools produce such jobs (design_board, edit_board, route_board, review_board, answer_question), which cleanly separates it from those producers. It also spells out the returned fields, so an agent knows precisely what it gets back.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The list of producer tools makes the trigger condition clear: call this after one of them starts a job. It does not explicitly say what to do on failure, whether to poll again, or when not to use it (e.g. vs. re-calling the originating tool), so it gives clear context but no exclusions.

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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