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Glama

sponsorable

Sponsor Contacts

list_sponsor_contacts
Idempotent

Contacts at the sponsoring company who are the likely buyers of podcast sponsorships — the people a podcaster or sales agency would pitch. Ordered by likelihood of owning the ad budget. Free to call. On credit-based plans, locked contacts come back masked (initials, no email/LinkedIn) with disclosure fields — has_email, email_domain, has_linkedin — showing what reveal_contact buys. Plans with contact data included get every contact unmasked here directly; those listings record deduplicated internal usage for billing parity (no credits are charged, nothing else changes).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesThe sponsor's domain

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate idempotency and non-destructiveness, but readOnlyHint is false. The description adds value by explaining that calls are free, no credits are charged, and for credit-based plans contacts come masked with disclosure fields. It also mentions internal usage recording, which is a side effect not captured by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by concise details on plans and behavior. Each sentence is informative and efficient, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input schema (1 parameter) and no output schema, the description adequately explains the output (masked/unmasked contacts, disclosure fields) and side effects (usage recording). It is comprehensive enough for an agent to understand the tool's behavior, though it could mention error cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'domain' with a minimal description ('The sponsor's domain'). Schema coverage is 100%, so baseline is 3. The tool description does not add further details about the parameter, but the purpose clarifies its role.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns sponsor contacts at a sponsoring company, specifically the likely buyers of podcast sponsorships. It uses a specific verb ('list') and resource ('sponsor contacts'), effectively distinguishing from sibling tools like 'search_sponsors' or 'reveal_contact'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool (to get sponsor contacts) and provides context about different plans (credit-based vs. included). It mentions 'reveal_contact' for unmasking, offering an alternative. However, it does not explicitly state when not to use this tool or provide clear exclusions.

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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TDQS

A4/5.0
Disambiguation4/5

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.

Naming Consistency3/5

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.

Tool Count4/5

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.

Completeness4/5

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.