get_brandGet a brand's full canonical record — name, domain, voice (tone_of_voice), story, visual identity (logo, primary color, visual assets), and counts. Use to inspect what a brand carries before deciding which Heist context to run, or to read the brand voice directly when writing copy. Free, read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| brand_id | Yes | Brand to inspect. Get from list_brands. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds 'Free, read-only' and specifies the exact data fields returned (name, domain, voice, story, visual identity, counts), providing valuable behavioral context beyond the annotations' safety signals.
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-to-three sentences, front-loaded with the core purpose and a concise list of returned fields. Every sentence adds value—no fluff, optimal length for quick comprehension.
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 simple tool with one parameter and no output schema, the description adequately covers what is returned (canonical record with specific fields) and offers usage context. It lacks detail on optional counts or pagination, but given the tool's specificity, it is near complete.
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 coverage is 100% with one parameter brand_id. The schema already includes a description ('Brand to inspect. Get from list_brands.'). The tool description does not add new parameter-level information, meeting the baseline for high coverage without further enrichment.
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 it returns a brand's full canonical record, enumerating specific fields (name, domain, voice, story, visual identity, counts). This distinguishes it from sibling tools like list_brands (which lists brands) and other retrieval tools, providing a precise verb-resource combination.
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 explicitly advises when to use the tool: 'inspect what a brand carries before deciding which Heist context to run' or 'to read the brand voice directly when writing copy.' It implies a pre-execution inspection role but does not explicitly mention when not to use it or list alternative tools, though the context is clear.
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.
Despite the high tool count, most tools have distinct purposes with thorough descriptions that specify when to use each. Some overlap exists among creative direction tools (call_creative_worlds vs chat_with_creative_worlds), but the descriptions clarify usage patterns.
Naming conventions are inconsistent overall: some follow verb_noun (create_powersource_url, decode_ad), others use noun_verb or compound names (adformula_intelligence, fleet_analytics_overview). However, subgroups like dispatch_* and list_*_presets maintain internal consistency.
112 tools is far beyond the typical 3-15 range for well-scoped servers. While the server covers a broad domain, the sheer number likely overwhelms agents and suggests insufficient consolidation of related operations.
The tool set covers core creative intelligence workflows: brand analysis, ad decoding, script generation, creative direction, and research. Minor gaps exist (e.g., no social media publishing tools), but the main use cases are well-supported.