get_post_analyticsFetch drilldown analytics for one published post, including per-snapshot metrics and campaign context.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Compatibility alias for postId. | |
| postId | No | Published post ID to inspect. | |
| team_id | No | Team ID to operate in team scope. Get available teams with list_teams. If omitted, uses personal scope. | |
| publishedPostId | No | Compatibility alias for postId. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / anyOfRemoved value: -[
- {
- "required": [
- "postId"
- ]
- },
- {
- "required": [
- "publishedPostId"
- ]
- },
- {
- "required": [
- "id"
- ]
- }
-]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's safe. The description adds domain context about the output (per-snapshot metrics, campaign context) and scope (single published post), which is meaningful behavioral detail beyond what annotations state.
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?
A single, information-dense sentence with no fluff. Key facts (drilldown, one post, per-snapshot, campaign context) are front-loaded efficiently.
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 is a simple read-only fetch, the description covers the essential purpose, scope, and output contents. Absence of an output schema is partially mitigated by describing what analytics include. It does not mention potential errors or pagination, but that is acceptable for this simple 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 100%, so all parameters are already documented. The description adds no extra meaning beyond the schema. The distinction between postId and aliases is clear from schema, so the description does not need to compensate.
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 verb (fetch), the resource (drilldown analytics for one published post), and specific content (per-snapshot metrics and campaign context). It distinguishes itself from sibling analytics tools by focusing on a single post's drilldown rather than aggregate or ad-focused analytics.
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 when to use the tool (when you need detailed per-post analytics) but does not explicitly name alternatives or say when not to use it. With many get_* analytics siblings, explicit differentiation from aggregate or unified analytics would strengthen this.
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.