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Get Luma Guests

get_luma_guests
Read-only

Paginates to exhaustion — the returned list is the complete guest roster, every approval status included (approved, pending approval, waitlisted, invited, declined). Deciding "who hasn't registered yet" from a partial roster produces false negatives that re-nag people who already signed up, so there is deliberately no page or field trimming here.

When this runs in an agent, each guest is also saved and linked to the agent's Output tab as an agent_search_results person row (keyed by their LinkedIn identifier when the form captured one, else their email), so the roster persists without a separate record step. Best-effort: a persist failure never fails the read. Re-running updates rows in place; a guest the user has removed from the list stays removed. Dict with event_id, guests array, count, and (when saved in an agent) a list summary {list_name, created, updated, total}. Each guest dict is Luma's guest object, the useful fields being: user_email, user_name, approval_status, registered_at, invited_at (null when the guest found the event themselves), registration_answers (the event form's question/answer pairs — a LinkedIn-URL question shows up here), utm_source (which outreach channel drove the registration, when the invite link carried ?utm_source=).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
event_idYesThe Luma event id from get_luma_events (starts with "evt-").
list_nameNoShort kebab slug naming the Output-tab list bucket. Absent, guests land in the 'default' list.
on_behalf_ofNoAn agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / on_behalf_of
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "An agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account."
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / list_name
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Short kebab slug naming the Output-tab list bucket. Absent,\nguests land in the 'default' list."
+}
  3. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true; the description goes well beyond that, disclosing the non-obvious persistence side effect (guests saved as agent_search_results rows), its best-effort/never-fails-the-read semantics, in-place update on re-run, and the sticky removal behavior. These are exactly the traits an agent could not infer from structured fields.

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?

Front-loaded summary followed by a clear returns block; every section is purposeful. The rationale for exhaustive pagination is slightly verbose but justified by the false-negative risk, and nothing is wasted structurally.

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

Completeness5/5

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

With no output schema, the description supplies a returns block enumerating the useful guest fields (user_email, approval_status, utm_source, registration_answers, etc.), and it covers the side-effect and idempotency behavior. An agent has everything needed to call and interpret it.

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%, so all three parameters (event_id, list_name, on_behalf_of) are already documented in the schema, including the get_luma_events cross-reference and agency-account semantics. The description adds no additional parameter meaning, so the baseline 3 applies.

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?

States a specific verb+resource ('List every guest of a Luma event') and adds scope ('full registration detail', complete roster across all approval statuses). An agent can distinguish this from get_luma_events and send_luma_invites without opening either schema.

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?

Explains the context that selects this tool — you need the exhaustive roster to decide 'who hasn't registered yet' — and warns that a partial roster causes false negatives. It doesn't explicitly name alternative tools or state when-not to use it, so it stops short of full routing guidance.

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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