generate_post_bundleGenerate multi-variant AI content with quality scoring for multiple platforms. Returns the best variant plus alternatives.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Content topic or instructions (required) | |
| platforms | Yes | Target platforms (required) | |
| generation | No | Generation parameters (tone, style, CTA) | |
| variant_count | No | Number of variants to generate (1-5, default: 3) |
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?
The description adds behavioral traits beyond annotations: 'quality scoring' and 'returns the best variant plus alternatives.' However, it does not disclose whether the generated content is persisted, whether it affects system state, or any side effects. With readOnlyHint=false, the agent knows it's not read-only, but the description doesn't clarify what write behavior occurs. It offers some value but remains incomplete.
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 sentences, front-loaded with the main action ('Generate multi-variant AI content'), and contains no filler. Every word contributes meaning. It is concise and well-structured.
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?
The tool has nested objects and no output schema, yet the description provides only a high-level view of the output ('Returns the best variant plus alternatives'). It does not specify the output structure, the meaning of 'best' in quality scoring, or any prerequisites. Given the absence of an output schema and sparse annotations, the description is adequate but leaves gaps.
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%, so parameters like 'prompt', 'platforms', 'generation', and 'variant_count' are already documented. The tool description does not add extra meaning about how parameters interact with quality scoring or variant selection. Baseline of 3 is appropriate since the description adds no parameter-specific value.
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 function: 'Generate multi-variant AI content with quality scoring for multiple platforms. Returns the best variant plus alternatives.' This is a specific verb+resource combination that distinguishes it from siblings like 'generate_content' (likely single variant) and 'score_content' (scoring only).
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 usage context: generating multiple variants across platforms with quality scoring and returning the best plus alternatives. It does not explicitly name alternatives or exclusion criteria, but the multi-variant and multi-platform scope implies when it should be preferred over simpler generation tools. Lacks explicit 'when not to use' guidance, so not a 5.
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.