Skip to main content
Glama

Boxlist

Match for two

match_for_two
Read-onlyIdempotent

Find titles two people will both enjoy, from one to five titles each of them loves. Blends their two Taste Fingerprint™ profiles the way Boxlist's Watch together does, skips anything either is likely to dislike, and returns picks with a band for the pair and for each person, plus where their tastes agree and differ. Defaults to films and TV; books and games can be added. Anonymous, nothing stored.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "personA": "<personA>",
      "personB": "<personB>"
    },
    "name": "match_for_two"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many picks (1-5, default 5).
mediaNoSame as suggestFrom (either name works).
personAYes
personBYes
suggestFromNoMedia to pick from. Omit for films and TV (watching together); add "book" or "game" if wanted.

Output Schema

NameRequiredDescriptionDefault
picksYes
personAYes
personBYes
privacyYes
nextStepYes
attributionYesRequired credit: show "Data from Boxlist" with the link wherever these results are used.
commonGroundYesWhere the two tastes agree and where they pull apart.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavior: it blends two profiles like 'Watch together,' filters out titles either person is likely to dislike, and states 'Anonymous, nothing stored' — a real privacy guarantee not present in structured fields. It stops short of describing confidence or what happens with sparse or unparseable titles.

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

Conciseness4/5

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

The core purpose leads the sentence and behavioral detail follows, so it is well front-loaded with no wasted length. 'The way Boxlist's Watch together does' is brand-flavored filler, but it does convey the blending mechanism in few 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?

For a five-parameter nested tool with an output schema, the description covers purpose, media scope, privacy posture, and the shape of the result (pair band plus per-person bands and agreement/difference notes). The only shortfall is that it never routes the agent away from single-person taste tools, which matters given several overlapping siblings.

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?

At 60% schema description coverage the baseline is 3, and the description largely restates the schema: the 1-5 lovedTitles limit, and the film/TV default with books and games optional mirrors the suggestFrom description. It adds no new meaning for limit, name, or the media/suggestFrom aliasing that the schema already explains.

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

Purpose4/5

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

States a specific verb and resource: 'Find titles two people will both enjoy, from one to five titles each of them loves,' which makes the two-person joint-recommendation scenario unmistakable. It doesn't reference any sibling tool (find_similar_in_feel, quick_taste_read, search_titles), so the agent must infer the boundary from the two-person framing alone rather than being told explicitly.

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

Usage Guidelines3/5

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

'Defaults to films and TV; books and games can be added' gives scope guidance, and the two-person framing implies when to reach for it. But there is no explicit when-to-use/when-not statement and no named alternative for the single-person case, leaving the agent to infer routing from sibling names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources