reply_to_conversationSend an agent reply through a supported SingChat real-time inbox. This takes the conversation over from the AI and broadcasts the message to the visitor and agent inbox. Email, Telegram, and WhatsApp conversations are rejected before any change.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | The reply text to send to the visitor. | |
| conversationId | Yes | The conversation id to reply in. |
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?
Describes key behavioral traits: takes over conversation from AI, broadcasts message to both visitor and agent, and rejects unsupported channels before any change. No annotations provided, so description carries full burden and does so thoroughly.
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?
Three sentences with no redundancy. Every sentence adds value: purpose, effect, and constraint. Front-loaded with clear verb and resource.
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?
Adequately covers core functionality, effect, and constraints for a simple tool with 2 params and no output schema. Lacks post-condition description (e.g., confirmation of success or failure), but overall sufficient given low complexity.
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 covers both parameters with descriptions. The tool description adds no new meaning beyond the schema; the channel rejection context is behavioral, not param-specific. Baseline 3 justified by 100% schema coverage.
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?
Clearly states the tool sends an agent reply through a SingChat real-time inbox, taking over from AI and broadcasting to visitor and agent. Distinguishes from read-only or resolution tools like get_conversation and resolve_conversation. Also specifies which channels are rejected.
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?
Implicitly indicates when to use: when a human reply is needed that takes over from AI. Excludes email, Telegram, and WhatsApp explicitly. However, no explicit comparison to sibling tools or mention of prerequisites.
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.
Tools are largely distinct, with clear purposes for knowledge management, conversations, FAQs, and setup. The only potential overlap is between 'search' (general help) and 'search_knowledge' (workspace KB), but descriptions clarify the context.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., add_knowledge, list_conversations, manage_faq). No mixing of conventions or vague verbs.
17 tools is well-scoped for a live-chat and AI agent workspace server. The set covers setup, knowledge base, conversations, FAQs, analytics, keywords, and embedding without being overwhelming.
The tool surface is comprehensive for core workspace management and support: setup, knowledge ingestion/search, conversation handling, FAQs, analytics, and keywords. Minor gaps like user management or advanced channel configuration, but nothing that critically hinders agent workflows.