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List your collections

list_collections

Your collections on refs.video (named sets of films you saved) and your favourites, with ids and film counts. Pass an id to get_collection to read the films and use them as references.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does useful work by disclosing the return content (ids and film counts), which matters because there is no output schema, but it never states the read-only nature, auth requirements, or pagination/ordering behavior.

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?

Two tight sentences with no filler, front-loaded with the resource definition and followed by the sibling routing. Every clause earns its place.

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?

With no output schema and no annotations, the description supplies the essential missing pieces: what the list contains and what the ids are for. Only minor gaps remain, such as ordering, pagination, and auth expectations.

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?

Both parameters (context, llm_model) are fully documented in the schema at 100% coverage, and the description adds nothing about them. Baseline 3 applies since the schema does all the work and these are analytics-only fields.

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?

States a specific verb (list) and resource (your collections and favourites on refs.video), and immediately defines the resource for the reader as 'named sets of films you saved'. It also distinguishes itself from get_collection by describing what it returns (ids and film counts) versus what get_collection does (reads the films).

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?

Explicitly routes the agent: 'Pass an id to get_collection to read the films and use them as references,' which names the alternative and the condition that selects it. It does not state any when-not-to-use condition, but the escalation path is clear enough to call correctly.

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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