run_analytics_reportExecute one saved analytics report immediately and return the generated run plus aggregated analytics data.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Compatibility alias for reportId. | |
| team_id | No | Team ID to operate in team scope. Get available teams with list_teams. If omitted, uses personal scope. | |
| reportId | No | Saved analytics report ID. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / anyOfRemoved value: -[
- {
- "required": [
- "reportId"
- ]
- },
- {
- "required": [
- "id"
- ]
- }
-]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool executes a report and returns a generated run plus aggregated data, adding context beyond the annotations (readOnlyHint=false, openWorldHint=true). It clarifies the output and side effect of creating a run, though it doesn't detail permissions or broader system impacts. This is adequate given the 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 a single, direct sentence that front-loads the action and outcome. It contains no filler or redundant information, making it highly concise and well-structured.
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 appropriately specifies the return value ('generated run plus aggregated analytics data') and notes the immediacy of execution. It doesn't address the optionality of parameters, but the schema covers that. Given the annotations and full schema coverage, the description is nearly complete for this complexity level.
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%, with each parameter (id, team_id, reportId) clearly explained in the schema, including the alias relationship and team scope. The description itself adds no extra parameter information, so baseline 3 applies.
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 states a specific action ('Execute one saved analytics report immediately') and resource ('saved analytics report'), clearly distinguishing it from related tools like list_analytics_reports or get_analytics. The verb 'execute' conveys an on-demand run, which is unique among siblings.
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 (to run a saved report immediately) but does not explicitly mention alternatives or exclusions. For example, it doesn't contrast with create_scheduled_report for scheduling or get_analytics for viewing existing data. Usage context is present but not fully 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.