request_project_contentGenerate UGC, slideshow, remix, caption, or calendar content from project intelligence.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | ||
| count | No | ||
| prompt | No | ||
| team_id | No | Optional team ID or slug. Ignored when using a team-scoped API key. | |
| platforms | No | ||
| projectId | Yes | ||
| contentType | No | ||
| saveToLibrary | No | ||
| consentConfirmed | No | Required when using uploaded creator or likeness-like media. |
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?
With annotations indicating readOnlyHint=false and openWorldHint=true, the description adds a little context by listing output types, but it does not disclose behaviors such as whether content is saved to the library, non-determinism of AI generation, or associated costs. Annotations cover the basic safety profile, but the description adds minimal additional behavioral transparency.
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 a single concise sentence with a clear front-loaded verb ('Generate') and specific content types. It is efficient, though it could include more useful detail without becoming verbose.
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 9 parameters, no output schema, and very low schema coverage. The description is far too brief to provide adequate context about what 'project intelligence' means, what the output looks like, or how parameters interact. It leaves critical gaps for an agent to correctly invoke the tool.
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 only 22%, with most parameters lacking descriptions. The description partially maps to the contentType enum ('UGC, slideshow, remix, caption, calendar') but provides no meaning for other key parameters like prompt, count, platforms, or saveToLibrary. Since schema coverage is low, the description should compensate but does not.
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 generates specific content types (UGC, slideshow, remix, caption, calendar) using project intelligence. It identifies the action and resource but does not explicitly distinguish it from sibling tools like 'generate_content' or 'generate_post_bundle', so it misses the top score for sibling differentiation.
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?
There is no guidance on when to use this tool versus alternatives such as 'generate_content' or 'save_project_content_to_library'. No context, prerequisites, or exclusions are provided. The description only implies it is for generating content based on project data, which is not enough to guide tool selection.
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.