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Create Trend Research Automation

create_trend_research_automation
Idempotent

Collect explicit values one missing field at a time in the returned order: topic, platform, frequency, conditional weekday/day_of_month, exact daily_time, timezone, and an explicit recipient/account-email choice. Resolve 'local' or a city to a canonical IANA timezone before supplying timezone; never pass the literal 'local'. Once complete, creation writes immediately; the first run is scheduled for the returned next_run_at. Region Global is fixed product behavior.

Input Schema

NameRequiredDescriptionDefault
uidNoOptional Firebase uid. Must match the URL uid unless local fallback is enabled.
topicNo
weekdayNo
platformNo
timezoneNoCanonical IANA timezone for the schedule, resolved from the user's explicit answer; never the literal 'local'.
frequencyNo
daily_timeNo
bearer_tokenNoOptional Firebase ID token. Hosted clients normally authenticate with an API key in the Authorization header or ?uid= in the URL instead.
day_of_monthNo
conversation_idNoCreator-list key for durable state. Reuse a returned list_id for follow-ups; when omitted, MCP uses the active generated list for this tool chain.
recipient_emailNo
preferences_modeNoHow to apply preferences for this call: 'stored' uses users/{uid}.agentPreferences, 'override' uses agent_preferences only, and 'none' disables stored filtering for this run.
agent_preferencesNoOptional per-call filtering preferences. Use with preferences_mode='override'. For backwards compatibility, passing this without preferences_mode implies override.
use_account_emailNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

With annotations already covering safety (readOnlyHint=false, destructiveHint=false, idempotentHint=true), the description adds real behavioral context the annotations cannot: creation commits immediately with no confirmation step, the first run is scheduled at the returned next_run_at rather than running now, and Region Global is fixed product behavior. It still omits what happens on duplicate/repeat calls (idempotency semantics) and whether the automation is active on creation.

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?

Three sentences, front-loaded with the elicitation order, then the timezone constraint, then the write/schedule consequence. Dense and mostly waste-free, though sentence one is a long comma chain and the trailing "Region Global is fixed product behavior" reads as an appended caveat rather than integrated guidance.

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

Completeness3/5

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

For a 14-parameter, zero-required mutation tool with no output schema, the description covers the happy-path elicitation well but leaves gaps: it references "the returned order" and "the returned next_run_at" without explaining what the response contains or how follow-ups should reuse it. It also never explains what the automation actually does once created or how the returned record maps to pause_automation/run_automation_now.

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 only 43%, so the description must compensate, and it does for the critical fields: it establishes ordering, marks weekday/day_of_month as conditional on frequency, requires an exact-format daily_time, mandates a canonical IANA timezone resolved from 'local'/a city (explicitly forbidding the literal 'local'), and frames recipient_email vs. account email as a choice. The auth/session params (uid, bearer_token, conversation_id, preferences_mode, agent_preferences) receive no mention, which the description leaves entirely to the schema.

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

Purpose3/5

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

The body never states plainly what the tool creates — the closest is "creation writes immediately", which implies a persisted resource but never says this schedules a recurring trend-research report on Instagram/TikTok delivered to a recipient. The concrete values listed (topic, platform, frequency, daily_time, recipient) let an agent infer the artifact's shape, but the purpose comes largely from the name/title, not the description. It also does not distinguish itself from create_lead_automation or update_trend_research_automation.

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?

It gives a strong interaction protocol (collect one missing field at a time, in the returned order), which is genuine usage guidance for the create flow. However there is no guidance on when to prefer update_trend_research_automation over creating a new automation, no prerequisites, and no exclusions. Usage is implied by the elicitation sequence rather than stated as when/when-not.

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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