get_job
Get the status and screenshots for a preview job. Poll until status is "completed".
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job ID returned by submit_preview |
Get the status and screenshots for a preview job. Poll until status is "completed".
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job ID returned by submit_preview |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses polling behavior and return fields (status, screenshots), but omits details like error handling, authentication requirements, or rate limits. Adequate but not comprehensive.
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 concise sentences with no wasted words. Front-loads the core purpose and critical polling instruction. Ideal for quick agent comprehension.
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 tool with one parameter, no output schema, and simple polling behavior, the description is largely sufficient. Could mention failure handling or timeout limits, but overall complete enough for typical use.
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?
Schema coverage is 100%, already documenting job_id as a string. Description adds helpful context that the ID comes from submit_preview, which is valuable for correct invocation. Exceeds baseline 3.
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?
Description explicitly states it gets status and screenshots for a preview job, with a specific verb 'get' and resource. It also includes polling guidance, distinguishing it from siblings like get_screenshot (specific screenshot) and submit_preview (submission).
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 polling instruction 'Poll until status is completed', but does not explicitly explain when to use this tool over siblings or mention alternatives. The context is clear enough for an agent to infer usage.
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 has a clear and distinct purpose: submitting previews, checking job status, fetching specific screenshots, and listing clients. No overlapping functionality.
All tool names follow a consistent verb_noun pattern (get_job, get_screenshot, list_clients, submit_preview), making them predictable and easy to distinguish.
With four tools, the server is well-scoped for its purpose—submitting email previews, tracking jobs, retrieving screenshots, and listing clients. No tools feel extraneous or missing.
The toolset covers the full workflow: submission, status polling, screenshot retrieval, and client discovery. There are no obvious gaps for the stated purpose of rendering email previews.