escalate_to_supportEscalate a social conversation to a connected support platform (Zendesk, Intercom, etc.).
Creates a support ticket from the conversation context.
| Name | Required | Description | Default |
|---|---|---|---|
| note | No | Internal note for the support team | |
| priority | No | ||
| conversationId | Yes | Conversation to escalate | |
| targetConnector | Yes | Support platform to escalate to |
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 indicate this is a mutating operation (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds that it creates a support ticket from the conversation context, providing a useful behavioral detail. However, it does not go deeper into prerequisites or side effects beyond that.
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?
Two concise sentences with no fluff. The first states the core action, and the second specifies the behavioral outcome, making it highly efficient 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?
For a simple escalation action, the description is sufficient: it names the platforms, the input conversation, and the ticket creation. It does not mention preconditions like connector connectivity or return values, but the schema and annotations cover the essential details, making it reasonably complete.
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 cover 3 of 4 parameters (note, conversationId, targetConnector), and the description clarifies that the escalation uses conversation context. The priority parameter lacks a description but is self-explanatory via its enum. The description adds marginal value beyond the schema.
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 ('escalate') with a clear resource ('social conversation') and destination ('connected support platform'), and explicitly states the ticket-creation outcome. This distinguishes it from siblings like reply_to_conversation or list_conversations.
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 clearly implies the use case: when a social conversation needs to be escalated to an external support platform. It does not explicitly name alternatives or exclude other tools, but the context is clear enough given the sibling list.
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.