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Blackie360

Luma Events MCP Server

by Blackie360

Invite guests from another Luma event

invite_guests_from_event

Invite approved or waitlisted guests from a source Luma event to a target event, removing duplicates and previously invited contacts. Preview aggregate counts first to protect privacy, then confirm to send soft invitations.

Instructions

Build a privacy-conscious audience from selected guest statuses on a source event, remove duplicate emails and anyone already on the target event, and send soft Luma invitations in batches. Call with confirmed=false first to preview aggregate counts without exposing identities; call again with confirmed=true only after explicit approval.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNo
confirmedNoFalse returns an aggregate preview. True rebuilds the audience and sends the invitations.
source_event_idYes
source_statusesNo
target_event_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.7.5

TDQS

A4.1/5.0
Behavior4/5

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

The description adds meaningful behavior beyond the annotations: duplicate removal, suppressing guests already on the target event, batching, soft invitations, and privacy-preserving aggregate previews. These details clarify the tool's side effects and safety model. It could also mention batch sizes, rate limits, or irreversibility, but current disclosure is strong.

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?

The description is two sentences with no filler. The core behavior is front-loaded, and the two-phase confirmed=false/true usage is placed immediately after. Every sentence earns its place.

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?

The two-step preview/approval workflow is well covered and the deduplication behavior is explained. However, with no output schema, the description does not state the return format beyond 'aggregate counts,' nor does it address the optional message parameter or the exact consequences of sending after confirmation. These are notable gaps for a tool that sends invitations.

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 only 20%, so the description must compensate. It partially does by referencing source/target events and 'selected guest statuses' for source_statuses, and it clarifies the confirmed false/true flow. However, it does not explain message, the default for source_statuses, or ID expectations, leaving clear gaps.

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 names a specific verb+resource workflow: build an audience from guest statuses on a source event, remove duplicates, and send soft Luma invitations. It clearly differentiates this tool from sibling invite/send tools by the 'from another Luma event' and deduplication behavior.

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?

The description gives explicit invocation guidance: call with confirmed=false first to preview, then call with confirmed=true only after explicit approval. It does not explicitly name alternative sibling tools or state when not to use this tool, which prevents a 5.

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