autopilot_approve_planApprove all pending items in an autopilot plan, or approve/reject individual items.
After approval, use autopilot_execute_plan to schedule the approved posts.
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | Action for individual item (default: approve) | |
| itemId | No | Individual item ID to approve or reject | |
| planId | No | Plan ID to approve (approves all pending items) | |
| feedback | No | Feedback when rejecting an item |
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 establish this is a mutating operation (readOnlyHint=false) but not destructive (destructiveHint=false). The description adds context that approval can be bulk or per-item and supports rejection with feedback. It also signals the next step in the workflow, giving behavioral context beyond annotations.
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 with no filler. The first sentence states the core functionality; the second provides a critical next-step pointer. Every word earns its place.
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 tool that alters approval state, the description covers the main actions and next step. It lacks information about the return value or how to inspect pending items, but given the absence of an output schema and the straightforward nature, it's sufficiently complete. It could mention that planId approves all pending, but that's implied.
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?
All four parameters are fully described in the schema (100% coverage), so the description doesn't need to repeat them. It does clarify the two usage modes (bulk via planId vs individual via itemId and feedback), which adds semantic meaning to how the parameters should be used together.
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 approves all pending items in an autopilot plan or approves/rejects individual items, using specific verbs (approve/reject) and a clear resource (autopilot plan). It distinguishes from siblings by focusing on plan-level approval and mentioning the follow-up execute tool.
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 explicitly directs the user to use autopilot_execute_plan after approval, providing clear sequential guidance. It implies this tool is for the approval phase, but doesn't explicitly contrast with alternatives like approve_post or list_pending_approvals, though its scope is evident.
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.