save_project_content_to_libraryGenerate project content and save the outputs into the reusable content library.
| Name | Required | Description | Default |
|---|---|---|---|
| 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 |
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 readOnlyHint=false and destructiveHint=false, which already communicate that this is a non-destructive write operation. The description adds that content is both generated and saved, but it does not disclose details about how saving works (e.g., creating a new library item, potential overwrites, side effects). Since annotations cover the basic safety profile, the description provides marginal additional value.
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 that front-loads the primary actions ('Generate' and 'save'). It is efficient and free of fluff, though it could be structured with more detail without becoming verbose. It earns its place but does not provide deeper context.
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?
Given the tool has 6 parameters, no output schema, and very low schema description coverage, the description is far from adequate. It does not explain what 'generate project content' means, how parameters influence generation, what platforms/content types are involved, or what the return value represents. This is insufficient for an agent to use the tool effectively without additional assumptions.
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 17% (only team_id has a description). The description does not mention any parameters, so it fails to compensate for the low schema coverage. Parameters like count, prompt, platforms, and contentType are left completely unexplained, adding no value beyond the raw schema.
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 project content and save the outputs into the reusable content library.' It includes specific verbs ('generate', 'save') and resources ('project content', 'library'), and distinguishes itself from sibling tools like generate_content and save_to_library by combining both actions in one operation.
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 no guidance on when to use this tool versus alternatives such as generate_content or save_to_library. It does not mention appropriate contexts, exclusions, or prerequisites, leaving the agent to infer usage solely from the tool's purpose.
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.