remove_company_perk
Remove a perk/benefit from your company on Himalayas. Requires employer authentication.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Perk ID to remove (use get_company_perks to find IDs) |
Remove a perk/benefit from your company on Himalayas. Requires employer authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Perk ID to remove (use get_company_perks to find IDs) |
Changes observed during successful MCP inspections.
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Input schema / additionalPropertiesRemoved value: -falseDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does state 'Requires employer authentication,' which is useful, but it does not disclose whether the removal is permanent, reversible, or the impact on existing data. The verb 'Remove' implies deletion, but further details on side effects or response behavior are absent.
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 a single, compact sentence that conveys the essential purpose and a key prerequisite immediately. No filler words or redundant information, making it efficient and well-structured.
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 operation with one parameter and no output schema, the description provides enough context: what is removed, where, and the authentication requirement. It does not specify return values, but that is often implicit for such operations, and the absence of an output schema reduces the need for detailed return behavior.
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 100% for the single parameter 'id' (Perk ID to remove), including a pointer to get_company_perks. The description adds no additional parameter meaning beyond what the schema already provides, so the baseline of 3 applies.
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 (Remove) and the resource (perk/benefit from your company on Himalayas). It is specific and distinct from siblings like add_company_perk and get_company_perks, making the tool's purpose unambiguous.
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 does not explicitly indicate when to use this tool versus alternatives. It mentions the prerequisite of employer authentication, which is a precondition, but gives no guidance on when removal is appropriate or when to prefer other tools. The schema's reference to get_company_perks helps with finding IDs but not with usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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