create_scheduled_reportCreate a recurring analytics report that's generated and delivered automatically.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Report name | |
| schedule | Yes | Report frequency | |
| platforms | No | Platforms to include | |
| reportType | Yes | Report type | |
| deliveryMethod | No | How to deliver the report | |
| deliveryTarget | No | Email address, Slack channel, or webhook URL |
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 readOnly=false and destructive=false; the description adds that reports are generated and delivered automatically, which is useful context about side effects. However, it does not disclose other behavioral traits such as validation behavior, prerequisites, or failure scenarios.
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 well-structured sentence that immediately states the purpose. Every word contributes meaning, with no redundancy or filler.
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's 6 parameters, 3 enums, and no output schema, the description is minimally adequate. It does not explain what the tool returns (e.g., an ID for the created schedule) or what happens after creation, but the schema covers parameter semantics sufficiently.
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 descriptions provide 100% coverage of all six parameters, so the description does not need to add parameter-level detail. It provides only general context about recurrence and delivery, which goes beyond the schema but does not materially enhance parameter understanding.
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 creates a recurring analytics report with automatic delivery, using a specific verb (create) and resource (scheduled report). It emphasizes 'recurring' and 'delivered automatically', which distinguishes it from one-time report tools like run_analytics_report.
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 this tool (when a recurring, automated report is needed) but does not explicitly contrast with alternatives or state exclusions. It lacks guidance on choosing between this and run_analytics_report or scheduling workflows.
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.