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Update influencer agent

update_influencer_agent
DestructiveIdempotent

Enable/resume (enabled=true) or reconfigure an influencer's posting agent: autonomy ('copilot' = every draft needs owner approval; 'auto_approve' = drafts publish THEMSELVES after the veto window — switching this ON authorizes live publishing without human clicks, so only do it on an explicit user instruction), posting cadence (postingPersonality/snacksPerDay), internal credit budget, target accounts (managed or connected), and the standing systemPrompt steer. Pass only what changes. To stop the agent, use pause_influencer_agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
enabledNo
autonomyNo
timezoneNo
accountKindNo
influencerIdYes
snacksPerDayNo
systemPromptNo
vetoWindowHoursNo
postingPersonalityNo
targetSocialBrandIdNo
internalBudgetCreditsNo
targetSocialAccountIdsNo
targetManagedAccountIdsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already signal destructive and non-read-only behavior, but the description adds meaningful context: switching to auto_approve 'authorizes live publishing without human clicks' and 'drafts publish THEMSELVES'. This goes beyond the schema and annotations to disclose the real consequence of the action. It does not contradict the annotations.

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 description is dense but front-loaded with the core action and then systematically walks through configuration categories. Every clause serves a purpose, including the risk warning and the pause alternative. It is long due to complexity, not because of filler.

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 13-parameter mutation tool with no output schema, the description covers the main decision points: what can be updated, the partial-update contract, the high-risk mode, and the stop alternative. It does not describe return values or prerequisites, but those are less critical given the strong annotations and clarity.

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?

With schema description coverage at 0%, the description must compensate, and it does: it defines the autonomy enum values with behavioral consequences, groups posting cadence parameters, and clarifies the 'managed or connected' account kinds. It misses timezone and does not fully disambiguate targetSocialBrandId from target account IDs, so it is strong but not complete.

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 uses specific verbs ('Enable/resume', 'reconfigure') and names the resource ('influencer’s posting agent'). It enumerates the exact areas of configuration, and explicitly distinguishes itself from the sibling pause_influencer_agent, making its purpose unmistakable.

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?

It gives explicit when-to-use guidance ('Pass only what changes'), a clear exclusion ('To stop the agent, use pause_influencer_agent'), and a strong condition for a risky action ('only do it on an explicit user instruction' for auto_approve). This is exemplary routing and scoping.

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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