Delete a label. It is removed from all assistants it was assigned to — the assistants themselves are NOT affected.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| message | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (destructiveHint=true), the description adds important detail about the cascading effect on assistants and clarifies that assistants are not affected, enhancing transparency.
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 (two sentences) and well-structured, with no redundant information.
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 delete tool with an output schema, the description covers the main effect and side effect. It lacks details on error scenarios or permissions, but overall is fairly 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?
With schema description coverage at 0%, the description fails to mention the single parameter 'id' or its role, leaving the agent to infer its meaning from context. This is a significant gap.
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 action ('Delete a label') and explains the specific side effect (removed from assistants but assistants unaffected), distinguishing it from other delete tools for different entities.
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 provides clear context on the consequence (removal from assistants), but does not explicitly state when to use this tool versus alternatives or mention 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.
Most tools target distinct resources and actions clearly (e.g., create_assistant vs create_campaign). However, there is some overlap like get_outbound_assistants vs get_assistants and list_all_phone_numbers vs get_phone_numbers, causing minor ambiguity.
Tools follow a consistent verb_noun pattern with underscores (e.g., create_document, delete_label, get_voices). Only minor deviations exist, such as generate_ai_reply and list_all_phone_numbers, but overall the pattern is maintained.
With 75 tools, the server is excessively large. Although the domain is broad (assistants, calls, messaging, etc.), the number of tools makes it difficult for agents to navigate and select the correct one, reducing coherence.
The tool set covers CRUD operations for most resources, plus webhooks, AI replies, and reference data fetching. Minor gaps exist (e.g., no delete_conversation, no attach phone number to assistant), but the surface is largely comprehensive for the platform's purpose.
