get_calendar_gapsDetect open days in the publishing calendar and return gap metadata for planning.
| Name | Required | Description | Default |
|---|---|---|---|
| end | Yes | Range end date in YYYY-MM-DD format. | |
| start | Yes | Range start date in YYYY-MM-DD format. | |
| team_id | No | Team ID to operate in team scope. Get available teams with list_teams. If omitted, uses personal scope. | |
| platforms | No | Optional platform subset to inspect. |
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 disclose readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the concept of 'open days' and 'gap metadata' but does not explain how gaps are computed, whether team context affects results, or what the returned metadata structure looks like. It adds some value beyond annotations but not substantial behavioral depth.
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, tightly worded sentence that front-loads the core action ('Detect open days') and immediately clarifies the output ('gap metadata'). Every word contributes to the tool's purpose, 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?
The tool has no output schema, so the description should ideally clarify what 'gap metadata' includes or how open days are determined. The annotations mitigate safety concerns, and the schema covers inputs, but the absence of return-value detail and algorithm behavior leaves the agent with only a partial picture for a planning-oriented 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?
The input schema already provides 100% parameter coverage with descriptions for start, end, team_id, and platforms. The description does not reference any parameters or add meaning beyond the schema, so the baseline of 3 is appropriate.
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 uses a specific verb ('Detect') with a clear resource ('open days in the publishing calendar') and states the result ('return gap metadata for planning'). This clearly differentiates it from sibling tools like get_best_times or get_calendar_recommendations, which focus on optimization rather than gap detection.
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 phrase 'for planning' implies a use case, but the description gives no explicit guidance on when to choose this over alternative tools such as get_calendar_recommendations, suggest_next_schedule_time, or get_best_times. It neither names alternatives nor provides exclusions, so usage context is only implied.
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.