list_meta_pagesList the Facebook Pages (with any linked Instagram business account) and ad accounts on the connected Meta account — use before post_to_meta / create_meta_campaign to pick the target. Requires the user to have connected Meta (Settings ▸ Connectors ▸ Meta); returns a connect hint if not.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint=true and destructiveHint=false. The description adds value by disclosing the prerequisite of Meta connection and the behavior when not connected (returns connect hint). This goes beyond the structured data. Minor gap: it does not describe the exact return structure (e.g., whether IDs are included), but that is not critical for a list tool.
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 sentences, front-loaded with the core purpose, followed by usage context and prerequisite. No unnecessary words or redundancy. Every phrase adds value.
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 no-parameter, read-only list tool, the description covers what is returned (pages, Instagram accounts, ad accounts), the intended usage context, and prerequisite/error behavior. Since output schema is absent, this is sufficient for an agent to understand what to expect and when to call it.
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?
The tool has zero parameters, so the baseline is 4 per the rubric. The description adds no parameter info because none exists; it focuses on the output and prerequisite, which is appropriate. No ambiguity to resolve.
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's action: listing Facebook Pages (with linked Instagram accounts) and ad accounts from the connected Meta account. It also explicitly names the downstream tools (post_to_meta/create_meta_campaign) that this serves, distinguishing it from other 'list' siblings like list_meta_posts or list_meta_comments.
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 explicit guidance on when to use: 'use before post_to_meta / create_meta_campaign to pick the target.' It also states the prerequisite of a connected Meta account and mentions the 'connect hint' returned if not, which helps the agent anticipate failure modes.
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.