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Glama

rotv-mcp

by eu.madeinro

Personal Entertainment Concierge — decide for me

tv_concierge
Read-onlyIdempotent

You have a window of free time — decide for me what to watch right now. Returns ONE primary decision (TV program OR streaming title) with confidence percentage, full reasoning breakdown, and up to 3 diverse alternatives with explicit trade-offs (pros/cons, reason not picked). Picks across live Romanian TV EPG AND streaming catalog (Netflix, HBO Max, Disney+, Prime Video, Apple TV+, SkyShowtime). Built-in anti-noise filter automatically removes news, political talk, reality shows, talk-shows (NO manual filtering needed by the model). Built-in title dedup (handles ~46% duplicate-airing ratio in TV EPG). Built-in opportunity-cost lookahead (flags better options just outside the window). Event-aware: major broadcasts (World Cup / Euro / Champions League / finals) get an importance boost in ranking AND are always listed in the important_today field, even when the mood-based pick is something else — for questions like "what is important today?", prefer tv_important_today. PREFER THIS TOOL over tv_recommend_by_mood, tv_plan_evening, and tv_recommend_today whenever the user wants ONE answer / a single decision / a plan for a specific window — those tools return ranked LISTS for browsing, this tool returns a DECISION. Routes any mood internally (obosit / vesel / concentrat / romantic / familie / captivant + EN aliases tired/happy/focused/romantic/family/thrilling). Trigger phrases: "what should I do", "decide for me", "pick for me", "I have X hours", "ce să fac", "am 2 ore", "alege tu", "mood X durată Y", "o singură decizie", "fii consilierul meu", "what to watch", "concierge me".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
moodNoobosit | vesel | concentrat | romantic | familie | captivant (RO/EN aliases accepted)
preferNo
windowNoExplicit window. Skips lookahead.
sourcesNo
min_ratingNo
allow_pausesNo
risk_aversionNolow = 3 alternatives, high = nonelow
duration_hoursNoShorthand when window is absent; starts at now.
exclude_keywordsNo
max_alternativesNo
exclude_categoriesNoAnti-noise filter (default: all four).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
reasonNo
windowYes
contextYes
decisionYes
freshnessYes
lookaheadYes
reasoningYes
anti_noiseYes
alternativesYes
asked_at_utcYes
sources_usedYes
important_todayNo

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already indicate read-only and idempotent; description adds extensive behavioral traits like anti-noise filter, title dedup, opportunity-cost lookahead, and event-aware boosting, all without contradiction.

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?

Description is long but well-organized with front-loaded purpose. Some redundancy (e.g., repeated emphasis on anti-noise filter) but each sentence adds value. Could be slightly more concise.

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 tool complexity (11 params, nested objects, output schema), description covers decision logic, edge cases, and exclusions well. Minor gap: some parameters lack explanation in description but schema provides partial info.

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?

Schema coverage is 45% (low). Description provides broad context for mood and window but does not detail many parameters (e.g., prefer, min_rating, risk_aversion). Does not fully compensate for low coverage.

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 decides what to watch for a single decision, contrasting with sibling tools that return lists. It specifies verb ('decide for me'), resource (TV and streaming), and scope (single decision for a window).

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

Usage Guidelines5/5

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

Explicitly lists trigger phrases, when to prefer this tool over siblings (tv_recommend_by_mood, tv_plan_evening, tv_recommend_today), and directs agents to tv_important_today for 'what is important today?' queries.

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

A4.2/5.0
Disambiguation4/5

Tools are largely distinct with clear use cases, though tv_recommend_by_mood and tv_recommend_today overlap somewhat in providing ranked lists. The descriptions include guidance to prefer tv_concierge for single decisions, which helps reduce confusion.

Naming Consistency5/5

All tool names follow a consistent 'tv_' prefix with a verb_noun pattern (e.g., tv_check_freshness, tv_search_program). Minor exceptions like tv_important_today and tv_now_on_tv still adhere to the same structure, maintaining high consistency.

Tool Count5/5

With 14 tools, the set is well-scoped for a comprehensive TV and streaming recommendation service. Each tool addresses a specific user need without being excessive, making the set appropriate for the domain.

Completeness4/5

The tool set covers key operations: search, recommendations (single, list, for couples), planning, prime-time/now, important events, and details. Minor gaps like user preference storage are absent, but the coverage is sufficient for most user queries.

Resources