set_default_brand_voiceSet a brand voice profile as the default. The default brand voice is automatically used by all AI content generation tools.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The brand voice profile ID to set as default |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate a write operation (readOnlyHint=false) and non-idempotent behavior. The description adds context that the default is automatically used by all AI content generation tools, which is a useful behavioral consequence. However, it does not disclose potential side effects like overwriting the previous default or required permissions.
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 two concise sentences. The first sentence states the purpose directly, and the second adds a key consequential detail. No filler or repetitive content.
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 one-parameter tool with no output schema, the description covers the purpose and the effect on AI content generation. Combined with annotations, it gives sufficient context for an agent to select and invoke the tool 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?
The single parameter 'id' has a clear schema description ('The brand voice profile ID to set as default'), resulting in 100% schema coverage. The tool description adds no extra semantic detail beyond the schema, so the baseline score of 3 is appropriate.
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 action (set) and the resource (brand voice profile) with the scope (default), and explains the automatic consequence on all AI content generation tools. It distinguishes from siblings like create_brand_voice and list_brand_voices by focusing on setting the default.
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 provides clear context: the tool sets the default brand voice that is automatically used by AI content generation tools. It implies when to use it (to change the global default) but does not explicitly mention alternatives or exclusions, such as using create_brand_voice to add new profiles.
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 148 tools, there is significant overlap. For example, generate_content, publish_ai, generate_post_bundle, and request_project_content all generate content; get_analytics, get_unified_analytics, get_post_analytics, get_ad_performance, and get_unified_ad_report all fetch performance metrics; and list_inbox vs list_conversations blur comment and conversation management. Descriptions help, but boundaries between tools are often unclear.
Most tools follow a verb_noun pattern (e.g., list_teams, create_goal, delete_post), but there are notable deviations: create_library_item vs save_to_library, publish_content vs publish_ai, schedule_content vs schedule_content_advanced, and connect_platform vs connect_connector. Mixed prefixes like 'autopilot_', 'check_', and 'get_' are fine, but overlapping verbs and a hyphen in 'connect_linkedin-page' reduce consistency.
148 tools is extreme for any server. Even for a broad social media management platform, this is far beyond what an agent can effectively navigate. The count is unwieldy and suggests the surface should be split into multiple focused servers (publishing, analytics, connectors, workflows, etc.).
The core social publishing workflow is well covered (create, schedule, publish, edit, delete, retry), and there are extensive features for analytics, workflows, connectors, and AI agents. However, some resources have CRUD gaps: no update/delete for brand voices, no delete_project, no update/delete for Product Hunt goals, and no explicit get_workflow. These are workable but notable omissions.