neuron

Update Ad Request

neuron_update_ad_request

Idempotent

Update a draft or rejected ad request. Only draft and rejected ad requests can be edited. Rejected requests revert to draft on update.

Input Schema

TableJSON Schema

Name	Required	Description
`budget`	No	Updated budget in kobo
`message`	No	Updated ad message content
`mediaUrl`	No	Updated media URL (set to null to clear)
`targetGoal`	No	Updated target number of deliveries
`targetType`	No	Target type: 'contacts' or 'groups'
`adRequestId`	Yes	Unique identifier (UUID) of the ad request to update
`messageType`	No	Updated message type: 'text', 'image', 'video', or 'document'
`targetingMode`	No	Targeting mode: 'auto' for system matching or 'manual' for hand-picked pool entries
`targetDemographics`	No	Updated demographic targeting criteria
`selectedPoolEntryIds`	No	Pool entry UUIDs for manual contact targeting
`selectedGroupPoolEntryIds`	No	Group pool entry UUIDs for manual group targeting

TDQS

A4.1/5.0

Behavior4/5

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

Description adds behavioral context beyond annotations: it restricts updates to specific statuses and specifies that rejected requests become draft. Annotations include idempotentHint=true, which aligns with update behavior. No contradiction.

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?

Two sentences, no fluff. Key information is front-loaded and every sentence adds value.

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?

No output schema, and description does not mention return values or error conditions. Given the tool's complexity (11 params, nested objects), some additional context about expected output or errors would be beneficial, but idempotentHint partially compensates.

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 100% with all 11 parameters described in the schema. Description does not add extra parameter semantics beyond stating the status constraint, which is relevant to adRequestId. Baseline score of 3 is appropriate.

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?

Description clearly states the tool updates ad requests, specifying that only draft or rejected requests can be edited and that rejected requests revert to draft. It distinguishes from sibling tools like create_ad_request or delete_ad_request.

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?

Explicitly states when to use the tool (only for draft or rejected ad requests) and describes the side effect of rejected reverting to draft. Does not mention alternatives explicitly, but the constraint is clear.

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

B3.4/5.0

Disambiguation3/5

Most tools are clearly separated by resource type, but there is meaningful overlap in messaging entry points (send_message, send_whatsapp, compose_message, bot_api_send) and contact ingestion/sync tools (import_contacts, populate_contacts, sync_whatsapp_contacts). The descriptions help disambiguate, but with 309 tools an agent will frequently need to read closely to pick the right one.

Naming Consistency4/5

The overwhelming majority of tools follow a consistent verb_noun snake_case pattern: create_*, get_*, list_*, update_*, delete_*. Minor deviations like sales_stats, lead_stats, wallet_balance, and whoami break the pattern slightly, but overall naming is highly predictable.

Tool Count1/5

309 tools is an extreme count for any MCP server, even a broad platform. This creates significant cognitive load and navigation overhead for agents, and far exceeds the well-scoped 3-15 tool range where coherence is strongest.

Completeness4/5

The tool surface is remarkably comprehensive across bots, contacts, campaigns, flows, knowledge bases, personas, marketplace, wallet, and products. Minor gaps exist — lead sources lack update/delete tools, and there is no single get_task or get_webhook alongside their list/update/delete counterparts — but these are workable gaps rather than dead ends.

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