get_conversationFetch a single conversation with its contact details and the most recent messages (oldest→newest).
| Name | Required | Description | Default |
|---|---|---|---|
| conversationId | Yes | The conversation id (from list_conversations). |
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?
Without annotations, the description carries the burden; it specifies that messages are returned oldest-to-newest, but lacks details on auth, rate limits, or destructive potential.
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?
Single sentence, front-loaded with the verb and resource, no unnecessary words.
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 1-param tool with no output schema, the description adequately explains what is returned, though it could mention pagination or size limits.
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 coverage is 100%, so baseline is 3; the description adds context about what the response includes but not param-specific details beyond 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 clearly states the tool fetches a single conversation with contact details and recent messages, distinguishing it from siblings like 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?
No explicit guidance on when to use this tool versus alternatives; usage is implied but not clarified.
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.