open_customer_inbox_conversationRefresh safe activity metadata for one conversation selected in the embedded Supovia inbox. This app-only tool reads no thread and returns no message body, preview, message id, customer detail, or internal note.
| Name | Required | Description | Default |
|---|---|---|---|
| conversationId | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| status | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds valuable context: it is app-only, reads no thread, and explicitly lists what it does not return (message body, preview, message id, customer detail, internal note). This goes beyond the annotation baseline and helps set expectations without contradicting the 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?
The description is extremely concise, consisting of two sentences with zero fluff. It front-loads the primary purpose and immediately adds a clarifying clause about what it does not do. Every sentence earns its place, making it easy to parse quickly.
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 a single parameter and an output schema (present), and annotations cover safety. The description explains the tool's scope and excluded content, which is good. However, it omits any explanation of the conversationId parameter, which is essential for a correct call. Given the low schema coverage, this is a notable gap, leaving the description only partially complete for an agent.
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 description coverage is 0%, meaning the conversationId parameter has no schema-level documentation. The description fails to explain what conversationId represents, its format, or how it should be obtained. This is a critical gap for an agent calling the tool, as it must infer the meaning from the tool name alone.
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's function: refreshing safe activity metadata for a single conversation in the embedded Supovia inbox. It is specific about the resource and action, and it clarifies what it does not return, which helps distinguish it from content-fetching tools. However, it does not explicitly differentiate from sibling tools like get_conversation or list_conversations, so it lacks explicit sibling differentiation.
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 implies usage by noting it is app-only and returns no thread content, suggesting it should be used when the agent needs to refresh metadata without content. It does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusion conditions or prerequisites. The context is implied but not formally stated.
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 targets a distinct action: retrieving single resources (get_conversation, get_document, get_website), listing sets (list_*), searching (search_documents), mutating state (resolve_conversation, send_message), and rendering views (show_customer_inbox, show_support_overview). Even the two inbox-related tools are clearly separated: one opens and counts, the other refreshes a specific conversation. No two tools could be easily confused.
All tool names follow a consistent verb_noun pattern with clear verbs: get, list, search, open, resolve, send, show. The naming is uniform across the set, with no mixed conventions (e.g., camelCase or vague verbs like 'process'). The pattern makes it predictable for an agent to infer tool purposes.
With 14 tools, the server is well-scoped for a customer support/support-documentation domain. Each tool covers a distinct responsibility without redundancy, and the count is within the ideal 3–15 range. There is no bloat or triviality.
The tool surface covers the core workflows: listing and retrieving conversations, resolving or sending messages, reading and searching documents, and listing websites/campaigns. Minor gaps exist (e.g., no document update/delete, no campaign creation/send), but these appear intentionally read-only for safety and the main support operations are well covered.