list_memoryList the durable facts & preferences saved in this workspace’s Memory (what the studio remembers about the brand, audience, taste, and do/don’t rules) — the same Memory the web app shows. These shape every future ad. Read-only, free.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max items (default 50, max 200) | |
| category | No | filter to one bucket (Brand/Audience/Taste/Do/Don’t/Preference) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description explicitly confirms 'Read-only, free.' It adds context about the Memory's influence on future ads and that it mirrors the web app, which is useful behavioral context. However, no additional behaviors (e.g., ordering, pagination details) are disclosed beyond what annotations and schema already provide, so a baseline 3 is appropriate.
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 compact and front-loaded: it states the core action and resource first, then clarifies content, impact, and safety in a few sentences. Every sentence adds value—no filler or repetition. It is appropriately sized for a simple read-only tool.
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 only two optional parameters, no output schema, and full annotation coverage, the description is complete. It explains what Memory is, its scope (workspace), its influence, and that it is read-only. An agent would have all needed information to decide when and how to call it correctly.
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 description coverage is 100%; both limit and category have descriptive comments in the input schema. The description adds no parameter-specific details, relying fully on the schema. Per the rubric, with high coverage, a baseline 3 is correct.
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 lists durable facts and preferences saved in the workspace's Memory, explicitly enumerating what those facts cover (brand, audience, taste, do/don't rules). It also differentiates this from other list tools by noting it shows the same Memory as the web app, leaving no ambiguity about the resource being listed.
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 implies the tool is the canonical way to read Memory, and the read-only/free note signals safe usage. It does not explicitly name alternatives (e.g., 'use remember to add facts'), but the context and sibling tools make the intended use clear. Lacks explicit when-not-to-use guidance, but the purpose is specific enough.
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.
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.
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.
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.
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.