Check a generation job's status and fetch result URLs when it's done. Video jobs take 1-5 minutes — poll this with a few seconds between calls.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | Job id returned by a generation tool |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as readOnly and non-destructive. The description adds useful behavioral context: it fetches result URLs on completion and describes polling behavior. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary purpose, followed by relevant polling guidance. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple status-checking tool with one parameter and good annotations, the description covers the key aspects: what it does, when to use, and what to expect (result URLs). No output schema exists, so the mention of fetching URLs partially addresses return values. Minimal gaps for a low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of the parameter (jobId) with a description: 'Job id returned by a generation tool.' The tool description essentially repeats this without adding new meaning. Baseline of 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check a generation job's status and fetch result URLs when it's done.' This uses a specific verb ('check') and resource (job status), distinguishing it from sibling tools which are generation/editing operations. No ambiguity or tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context on when to use: polling until done, with a specific polling interval ('a few seconds between calls'). It also mentions typical duration for video jobs. However, it does not explicitly state when not to use or name alternatives, so it stops short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct action or resource: photo enhancement, decluttering, staging, day/night conversion, sign placement, video creation, uploads, job checking, credit balance, and library listing. Even related tools like cinematic_motion and before_after_reel have clear differences (single image vs. two frames). No genuine overlap exists.
Most tools follow a clear verb_noun pattern (declutter_photo, enhance_photo, get_job, list_library), but several use noun phrases (cinematic_motion, virtual_staging, before_after_reel, day_to_night). This mixed convention is still readable but not perfectly uniform.
With 13 tools, the server covers a wide range of real-estate media operations without being bloated. Each tool serves a specific need, and the count is well within the typical 3-15 range for a purpose-built server.
The tool surface covers the main workflows: photo enhancement, staging, editing, video creation, uploads, library viewing, job status, and credit management. Minor gaps exist like no delete/update for library items, but these are not critical for the core real-estate editing use case.
