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StreamPick

Where to watch in Korea

where_to_watch
Read-only

Where a movie or series can be watched in South Korea: subscription, rental and purchase services, the cheapest monthly plan or bundle (KRW), when the next episode airs, and a StreamPick link. Pass an id from search_titles, or a title.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "where_to_watch"
  }
}

Input Schema

NameRequiredDescriptionDefault
idNoStreamPick id such as 'tmdb-tv-93405'
yearNoRelease year to pick the right title among remakes
titleNoTitle in Korean or English, used when no id is given

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered; the description's added value is disclosing the shape of the response (service types, KRW pricing, air dates, StreamPick link), which matters because there is no output schema. It does not cover failure behavior, such as what happens when a title has no Korean availability or multiple matches, keeping it short of a 5.

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?

One front-loaded sentence states the purpose first and then enumerates return contents with zero filler. Every clause earns its place, especially since there is no output schema to carry the return-shape information.

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?

For a three-parameter, read-only lookup with no output schema, the description covers purpose, input sourcing, and return contents adequately. The main gap is disambiguation behavior — nothing says how ambiguous titles are resolved or that year exists for that purpose.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds genuinely useful meaning by positioning id as coming from search_titles and title as the fallback ('or a title'). The year parameter is left to its schema description for remake disambiguation, so the added value is real but partial.

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 names a specific resource and scope ('Where a movie or series can be watched in South Korea') and enumerates exactly what the tool returns: subscription/rental/purchase services, cheapest monthly plan in KRW, next episode air time, and a StreamPick link. This is clearly distinguishable from siblings like search_titles, which is named as the source of the id.

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?

It gives a concrete invocation path: 'Pass an id from search_titles, or a title,' establishing the recommended search-then-lookup workflow and the id/title fallback relationship. It stops short of stating when not to use the tool or how to handle ambiguous title matches, so it is clear context without explicit 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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