Read a conversation label by ID to retrieve its details, such as name and metadata.
Read one conversation label by ID. Labels apply to conversations, not contacts.
| Name | Required | Description | Default |
|---|---|---|---|
| label_id | Yes | Superchat label ID, for example one returned by list_labels. |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the useful domain clarification that labels apply to conversations, not contacts, but does not disclose error behavior or output format.
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 short sentences with no filler. The core action is front-loaded, and the second sentence adds only a relevant distinction without unnecessary detail.
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 one-parameter read operation with strong annotations, the description is nearly complete. It could mention what a valid label ID looks like or what the response contains, but those are minor gaps given the schema constraints and read-only nature.
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 input schema covers 100% of the single parameter, including a description and an example referencing list_labels. The tool description adds no additional parameter-level meaning, so a baseline score of 3 is appropriate.
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 ('Read') and resource ('one conversation label by ID'), making the tool's function immediately clear. It also differentiates labels from contacts, which helps distinguish it from contact-related tools.
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 this tool is for fetching a single label by its ID, and the schema's mention of list_labels suggests an obvious companion tool. However, it lacks explicit guidance on when to prefer this tool over list_labels or other label-related operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.