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OverviewDiscussionsAdmin

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=).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "event_id": "<event_id>"
    },
    "name": "get_luma_guests"
  }
}

Input 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_ofNoA teammate's email, or an agency client's from list_agency_clients, to run this call in that 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
    • changedInput schema / properties / on_behalf_of / description
      Previous value: -"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."New value: +"A teammate's email, or an agency client's from list_agency_clients, to run this call in that account: their agents, settings, connected accounts, and credits. Omit it to act on your own account."
  2. 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."
+}
  3. 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."
+}
  4. 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, and the description goes well beyond them: it discloses the exhaustion/pagination behavior and the side effect of persisting each guest as an agent_search_results row in the Output tab, keyed by LinkedIn id or email. It also documents best-effort persistence (a persist failure never fails the read), in-place updates on re-run, and that user-removed guests stay removed.

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 summary is front-loaded and the returns block is clearly separated, with each paragraph carrying distinct information (pagination rationale, persistence side effect, return shape). It is somewhat long, but the length is justified by the non-obvious write side effect.

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?

There is no output schema, yet the description supplies an inline returns section covering event_id, guests, count, the list summary object, and the useful per-guest fields. Combined with the persistence and pagination notes, 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 description coverage is 100%, so event_id, list_name, and on_behalf_of are already documented in the schema. The description adds context about what guest data contains (registration_answers, utm_source) but nothing new about how to set the parameters, 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?

Opens with a specific verb and resource: 'List every guest of a Luma event, with full registration detail.' That clearly separates it from get_luma_events (events themselves) and send_luma_invites (outbound invites) among the siblings.

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?

Gives a concrete motivating use case ('deciding who hasn't registered yet') and explains why the tool never partial-pages or field-trims. It does not explicitly name alternatives or when-not-to-use, so it falls short of a 5, but the operating context 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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