get_best_timesGet the optimal posting times for a platform based on your historical engagement data.
Returns top time slots ranked by engagement score. Falls back to industry defaults when insufficient personal data exists.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of time slots to return (default 5, max 20) | |
| platform | Yes | Platform to get best times for |
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 declare readOnlyHint=true and idempotentHint=true, so the agent knows it's a safe read. The description adds valuable context: it returns top time slots ranked by engagement score and falls back to industry defaults when insufficient personal data exists. This goes beyond the annotations and clarifies expected behavior.
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 exceptionally concise, only two sentences, and front-loads the main purpose in the first sentence. The second sentence adds necessary detail about output ranking and fallback. Every word earns its place, with no redundancy.
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 simple read tool with two well-described parameters and no output schema, the description covers the essential aspects: what the tool returns, the basis (engagement data), and the fallback behavior. It is sufficiently complete given the tool's low complexity and strong annotations, though it could mention the time slot format for extra clarity.
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 coverage is 100%, with both platform and limit described in the input schema. The description adds no additional parameter-level detail beyond the schema, so a baseline score of 3 is appropriate. It does mention engagement data and fallback behavior, which indirectly relates to platform, but doesn't enrich parameter semantics.
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: to get optimal posting times for a platform based on historical engagement data. It uses a specific verb (get) and resource (optimal posting times), and the title reinforces the purpose. It distinguishes itself from general analytics and scheduling tools by focusing on best times.
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 implies the tool is for when you need optimal posting times based on engagement data, and mentions fallback to industry defaults. However, it does not explicitly contrast with sibling tools like 'suggest_next_schedule_time' or 'suggest_queue_slots', so the guidance remains implied rather than explicit.
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.