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List saved creators

list_creators
Read-only

List this workspace’s SAVED CREATORS — the reusable on-camera cast (AI creators made here, a person pulled from a social profile, a consented photo upload). Read-only, FREE. Each entry gives the name, the PORTRAIT URL, where the portrait came from and whether a real person’s likeness consent is on file, how many extra pose plates exist, and any chosen or cloned voice. TO PUT ONE IN A FINISHED AD, pass their id or name as render_ad’s creator — that casts them for the whole spot (and skips the character-portrait render, so it costs less than not casting anyone). THE PORTRAIT URL IS THE REUSE HANDLE for the raw lanes — pass it as generate_avatar’s image (a talking clip of them), generate_video’s refImage (they star in the scene), recast_motion’s image (they perform a reference clip’s motion), or generate_image’s refImages. CALL THIS BEFORE OFFERING TO GENERATE A NEW PERSON: re-casting somebody the workspace already has keeps the SAME face across every ad, while a fresh person costs credits and breaks that continuity. An empty answer means the workspace genuinely has no cast yet — say so and offer generate_avatar / save_creator, never invent a roster.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNomax creators to return (default 24)

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description builds on that by adding the 'FREE' note, the nature of the returned data (portrait URL, consent flag, pose plates, voice), and that an empty list means genuinely no cast exists. It also reveals the indirect cost implication when reusing a creator vs generating a fresh one. That goes beyond annotations without contradicting them, though it stops short of detailing pagination or potential latency issues.

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 dense but every sentence earns its place. It starts with the core purpose, then details the output fields, then cross-references how to reuse the results, and finally gives explicit usage timing and fallback behavior. No filler, and the most important usage guidance is front-loaded. The length is justified by the complexity of downstream integrations.

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?

Given there is no output schema, the description compensates by fully enumerating the returned fields (name, portrait URL, provenance, consent status, pose plates, voice) and how to interpret each. It explains the empty response case and exactly how to use the data with other tools. For a list tool, this is exceptionally complete — it even covers cost implications and continuity guidance, so an agent can act correctly on the results.

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% — the only parameter 'limit' is fully described ('max creators to return (default 24)'). The description does not add further detail about the parameter, which is acceptable given the schema already covers it. The baseline of 3 applies because the description adds no extra semantic value beyond the schema for this single parameter.

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 states the specific verb 'list' and resource 'saved creators', defines what a creator is (on-camera cast, with examples), and explicitly differentiates from sibling tools by explaining how the output is consumed downstream (render_ad, generate_avatar, etc.). It is far more specific than just 'list creators' and leaves no ambiguity about what the tool returns.

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?

The description gives explicit when-to-use guidance ('CALL THIS BEFORE OFFERING TO GENERATE A NEW PERSON') and reasons (cost savings, continuity), and also explains the empty-answer case with an alternative path (offer generate_avatar/save_creator). It names specific alternative tools (save_creator, generate_avatar) and the condition for using them, leaving no inference needed.

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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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.