image status
image_statusPoll image-gen job status by job ID. Returns image base64 PNG when COMPLETED. [free]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| job | Yes | Job ID from image_gen |
image_statusPoll image-gen job status by job ID. Returns image base64 PNG when COMPLETED. [free]
| Name | Required | Description | Default |
|---|---|---|---|
| job | Yes | Job ID from image_gen |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does reveal key behavior: it polls by job ID, and the output is a base64 PNG only when the job is COMPLETED. It does not describe non-completed statuses or failure modes, but for a simple polling tool this is reasonable disclosure.
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?
The description is two short, front-loaded sentences with no wasted words. The core action and the key output condition are immediately clear, and '[free]' is the only minor extra detail.
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 one-parameter tool with no output schema, this description gives sufficient information to invoke the tool correctly: what to pass, what action it performs, and the expected result when complete. The main gap is the lack of detail about responses before completion, but this is not critical for the invocation decision.
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% and the single 'job' parameter already has the description 'Job ID from image_gen'. The description adds little beyond saying 'by job ID', so it does not meaningfully exceed what the schema already provides.
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 states the specific verb 'Poll' and the resource 'image-gen job status', and clarifies the tool returns base64 PNG when completed. This clearly distinguishes it from the sibling image_gen tool, which generates jobs rather than checking their status.
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?
The description clearly implies the tool is used after obtaining a job ID from image_gen, and the schema reinforces this with 'Job ID from image_gen'. It does not explicitly name alternatives or state when not to use it, but the intended context is clear enough for correct selection.
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.
Most tools have distinct purposes, but several clusters overlap: domain_facts, page_meta, and scrape all return page title information, and search_verify, hallucination_check, and sweep all target claim validation. The descriptions usually clarify the use case, but the boundaries are not always obvious.
All names use lowercase snake_case, so there is a baseline consistency, but the pattern is mixed: bare verbs like scrape, summarize, and sweep sit alongside noun+noun forms like domain_facts and noun+verb forms like entity_find. The names are readable but do not form a predictable verb_noun API convention.
At 26 tools, this is heavy and above the typical well-scoped 3-15 range, though the server is explicitly positioned as a broad shelf of paid utilities. Many tools are small one-purpose endpoints, so the count feels more like a catalog than a focused suite, but it is not an extreme mismatch.
The shelf covers the major advertised areas: web page analysis, research verification, text guards and NLP, blockchain reads, and image generation. There are some gaps such as no web search and no transaction sending, but agents can typically work around them or pair this with another server.