autopilot_generate_planGenerate a weekly content plan for an autopilot goal.
Chains 5 AI agents: Strategy Planner -> Content Ideation -> Calendar Optimizer -> Multi-Format Composer -> Variant Repurposer. Returns a plan with individual post items for review.
| Name | Required | Description | Default |
|---|---|---|---|
| goalId | Yes | The autopilot goal ID to generate a plan for | |
| weekNumber | No | Specific week number to generate (optional, auto-increments) |
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 already indicate this is a mutating, open-world operation. The description adds behavioral context beyond annotations by detailing the 5-agent chain and that the output is for review, not execution. This explains the internal process and intent without contradicting the annotations. No annotation contradiction.
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 three sentences, front-loaded with the core action, then adds the agent chain and output purpose. Every sentence earns its place with no redundancy or fluff.
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?
With no output schema, the description explicitly states what is returned ('a plan with individual post items for review'), which is essential. It also provides enough process context (agent chain) for an agent to set expectations. It could be more complete by noting that the plan is not yet approved or scheduled, but 'for review' implies this and sibling tools cover approval.
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% for both parameters (goalId and weekNumber), so the schema already documents their meaning. The description adds minimal parameter context—'weekly' aligns with weekNumber but does not clarify defaults or auto-increment behavior beyond the schema. Baseline 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 uses a specific verb and resource: 'Generate a weekly content plan for an autopilot goal.' It clearly indicates the scope (weekly, autopilot goal) and distinguishes from sibling tools like autopilot_execute_plan and autopilot_approve_plan by stating it returns a plan for review. The agent chain further clarifies the purpose.
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 gives clear context: this is for generating a weekly plan for an autopilot goal, implying use before approve/execute steps. However, it does not explicitly mention alternatives or when not to use it (e.g., for manual content generation, use generate_content instead). This misses the 'explicit alternatives' bar for 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.