Skip to main content
Glama

Hermoso

by io.github.hermoso-ai

Get render job

get_job
Read-only

Poll a render job by id. Returns status (queued|running|done|error), progress, and on done the served media URL. Renders take 1–3 minutes: keep calling this until done/error without asking the user — several calls is normal, not a stall. An id that does not exist on this account answers status "not_found" — that is FINAL: stop polling it, and do not re-fire the render (that double-charges).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesthe job id, e.g. job_xxx

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. It adds valuable behavioral context: the polling loop, expected latency, and the terminal nature of 'not_found' with charging implications. This goes well beyond the annotations, giving the agent a full picture of the tool's runtime behavior.

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 concise and well-structured. The first sentence states the purpose and return values, the second explains polling behavior and timing, and the third addresses the edge case of not_found. No redundant sentences; each earns its place, and key operational guidance is front-loaded.

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?

For a tool with a single parameter and no output schema, the description is complete. It explains all return fields (status, progress, URL), the polling pattern, and terminal conditions. No aspect an agent needs to call it correctly is missing, and given the lack of an output schema, the description fulfills the role admirably.

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 coverage is 100% with a clear description for the 'id' parameter ('the job id, e.g. job_xxx'). The tool description does not add new semantic detail about the parameter itself, but it does contextualize how the id is used (e.g., polling, not_found handling). Since the schema adequately describes the parameter, a baseline of 3 is appropriate.

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?

The description clearly states the tool polls a render job by id and returns status, progress, and served URL on completion. It distinguishes itself from sibling tools like list_jobs and render_ad by specifying the polling action and the specific resource. The verb 'poll' and the resource 'render job' make the purpose unambiguous.

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

Usage Guidelines5/5

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

Explicit usage guidance is provided: it instructs to keep polling until done/error without pestering the user, notes normal polling duration (1–3 minutes), and warns that a 'not_found' status is final and should stop polling to avoid double-charging. This tells the agent exactly when to use the tool, how long to persist, and when to stop, clearly differentiating it from alternatives like re-rendering.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Hermoso toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

With 293 tools, the surface is enormous and many tools have overlapping purposes—multiple posting tools (post_to_meta, post_to_linkedin, schedule_post, etc.), multiple analytics tools per channel, and several search tools (search_meta_ads, search_instagram, search_reddit...). While each description is detailed, the volume makes it difficult for an agent to reliably distinguish between similar tools without careful reading, leading to frequent misselection.

Naming Consistency4/5

The naming is largely consistent with a verb_noun pattern (post_to_*, list_*, create_*, delete_*, update_*, manage_*). There are clear families for major operations. A few outliers like 'google_business_account', 'hermoso_capabilities', and 'store_get' break the pattern, but the overwhelming majority follow a predictable structure, making navigation somewhat easier.

Tool Count1/5

293 tools is far beyond any reasonable scope for a single MCP server, even for a comprehensive marketing platform. The calibration guide flags 50+ as an extreme mismatch, and this is nearly six times that threshold. Such a large surface overwhelms context windows, increases the probability of misselection, and makes it impractical for agents to learn or use effectively.

Completeness4/5

The tool set covers a vast domain: ad creation and rendering, posting across nine+ social channels, analytics and reporting, file management (Drive/OneDrive), competitor research, brand management, and more. It appears to provide CRUD and lifecycle coverage for most resources. While there may be minor gaps given the immense scope, the overall coverage is impressively comprehensive.

Resources