remove_list_itemRemove an item from a saved list. Removing an item that isn't in the list is a no-op. Free to call.
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | Yes | The item's id as shown in get_list (domain, podcast_id, category_id, or contact_id) | |
| list_id | Yes | The list's id (from list_lists) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide idempotentHint and non-destructive/non-read-only flags. The description adds that removing a missing item is a no-op, which aligns with idempotency, but does not disclose other behavioral traits.
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?
Three concise sentences front-loaded with purpose. Every sentence adds value with no redundancy.
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?
Given the simple nature of the tool (2 required params, no output schema, no nested objects), the description adequately covers behavior, idempotency, and usage context.
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% with clear descriptions. The tool description adds no additional meaning beyond what the schema provides.
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 removes an item from a saved list, distinguishing it from siblings like add_list_item (adds) and delete_list (deletes entire list).
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?
It notes that removing a non-existent item is a no-op and that the tool is free to call, but does not explicitly contrast with alternatives or state 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 have clear, distinct purposes. Overlap exists between search/search_sponsors and fetch/get_sponsor, but descriptions clarify the different use cases (deep-research compatibility vs. full structured queries). Overall, an agent can reliably differentiate tools.
The naming convention is predominantly verb_noun (e.g., create_list, get_sponsor), but two tools (fetch, search) are single verbs without a noun, breaking consistency. This introduces minor confusion about their scope.
17 tools is a reasonable number for a podcast sponsorship database, covering search, retrieval, list management, and contact operations. It's not overly heavy nor too thin for the domain.
The tool surface covers primary operations: searching sponsors/podcasts, retrieving details, managing lists, and handling contacts. Minor gaps exist (e.g., no tool to update sponsor data, but that's likely read-only), but core workflows are well-supported.