Manually trigger a monitor poll
monitors_triggerQueue an immediate check for an active monitor (async — returns once queued, not once the check finishes).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
monitors_triggerQueue an immediate check for an active monitor (async — returns once queued, not once the check finishes).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the operation is async and returns once queued, not after the check completes. This adds valuable behavior beyond the annotations (readOnlyHint=false indicates a write). It also hints at the 'active monitor' requirement, though it doesn't elaborate on failure modes or side effects. No contradiction with 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?
One sentence, front-loaded with the primary action, and includes the key behavioral nuance (async) without extra words. Exemplary conciseness.
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?
Adequate for a simple trigger tool: explains the action and async return. However, it omits explicit parameter binding, doesn't clarify error conditions (e.g., if the monitor is inactive or nonexistent), and offers no details on the return value's shape. The gaps are notable but not crippling.
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 schema provides no description for 'id' (0% coverage), and the description does not explicitly state that 'id' refers to the monitor ID. While the tool name and title imply it, the agent is left to infer the binding. This is a significant gap given the single parameter and lack of schema documentation.
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?
States a specific verb 'Queue', resource 'monitor', and adds clarifying detail about the async behavior. This clearly distinguishes it from sibling tools like monitors_list, monitors_get, and monitors_checks_list, which cover listing, retrieval, and check history respectively.
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 makes clear when to use it (to trigger an immediate check on an active monitor) but does not explicitly contrast it with alternatives or state prerequisites (e.g., that the monitor must exist and be active). It provides context without exclusions, falling short of a 5.
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.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.