list_listsThe organization's saved lists — named collections of sponsors, podcasts, categories, and contacts — with item counts. Free to call.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's 'Free to call' adds marginal value beyond stating it's safe. No additional behavioral details like rate limits or data freshness.
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?
Single sentence that covers purpose and a key characteristic (free). No fluff, but could be split for readability or include a note about return format.
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 zero parameters and no output schema. The description explains the output as lists with item counts, but doesn't mention pagination, filtering, or ordering. Adequate for a simple list, but missing details an agent might need.
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?
There are no parameters, so baseline is 4. The description does not need to add parameter info, but mentions output details (item counts), which is irrelevant to parameter semantics but does no harm.
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 returns saved lists with item counts, specifying the kinds of items (sponsors, podcasts, etc.). This distinguishes it from sibling tools like get_list (single list) and create_list (creation).
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 says 'Free to call,' implying no cost, but provides no explicit guidance on when to use this tool versus alternatives like get_list or search. Minimal context about optimal use cases.
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.