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Hermoso

by io.github.hermoso-ai

Read a workspace store

store_get
Read-only

Read one of this workspace’s data stores by key, for visibility into what the app holds — playbooks, swipefile, saved locations, avatars, creations, chats, brand, memory, skills. Read-only, free. Allowed keys: heist.memory.v1, heist.skills.v1, heist.playbooks.v1, heist.avatars.v1, heist.locations.v1, heist.chats.v1, heist.creations.v1, heist.assets.v1, heist.brand.v1, adInspo.swipefile.v1. (The typed tools — list_memory / list_skills / get_brand — are friendlier for those; use store_get for the rest.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesthe store key to read (one of the allowlisted keys)
limitNomax array items to return (default 50)

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description reinforces 'Read-only, free' and adds context about the store contents and the allowlist of keys. It also notes the existence of typed alternatives, which helps the agent choose correctly. This goes beyond simply repeating annotations, though it doesn't detail return format or pagination 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?

Two sentences deliver purpose, scope, allowed keys, and routing guidance with zero filler. The primary action is front-loaded before alternatives are mentioned. Every sentence earns its place.

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 read-only, two-parameter tool with no output schema, the description covers the purpose, allowed keys, and when to use alternatives. It is complete for an agent to call this tool correctly without needing further clarification.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema documents the parameters. The description adds meaningful value by listing the exact allowed key values, which the schema does not enumerate. This goes beyond the baseline of 3, as it provides actionable semantics for the key 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?

States a specific verb ('Read'), a specific resource ('one of this workspace’s data stores'), and a clear access method ('by key'). It enumerates what the stores hold (playbooks, swipefile, etc.) and explicitly contrasts with sibling typed tools (list_memory, list_skills, get_brand), making its function unmistakable.

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?

Explicitly says when to use this tool ('Read-only, free') and when not to: it names the friendlier typed tools for specific keys and advises 'use store_get for the rest.' This gives clear routing guidance against alternatives without requiring the agent to infer.

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.

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