fathom_list_meetings

List Fathom meetings with summaries and optional date filtering
READ

fathom_list_meetings

Retrieves a list of meeting recordings from your Fathom account with optional date filtering and AI-generated summaries. Use this tool to discover available meetings before retrieving transcripts.

Fathom includes AI-generated summaries in the list results, giving you a quick overview of meeting content without downloading full transcripts.

Prerequisites

Fathom Integration Required

This tool requires Fathom integration to be configured. See the Fathom Setup Guide for configuration instructions.

Parameters

fromDate

Typestring (ISO 8601 date)
RequiredNo
DescriptionStart date for filtering meetings. Format: YYYY-MM-DD. If omitted, returns meetings from the beginning of your account history.
Example2024-11-01

toDate

Typestring (ISO 8601 date)
RequiredNo
DescriptionEnd date for filtering meetings. Format: YYYY-MM-DD. If omitted, returns meetings up to the current date.
Example2024-11-30

limit

Typenumber
RequiredNo
DescriptionMaximum number of meetings to return. Default: 10. Maximum: 100.
Example20

Request

1{
2 "fromDate": "2024-11-01",
3 "toDate": "2024-11-30",
4 "limit": 20
5}

Response

1{
2 "meetings": [
3 {
4 "meetingId": "meeting_abc123",
5 "title": "Product Planning - Q4 Roadmap",
6 "date": "2024-11-15T14:00:00Z",
7 "duration": 3600,
8 "summary": "Discussed Q4 product roadmap priorities. Team agreed to focus on enterprise features and API improvements. Marketing will need updated positioning by Dec 1.",
9 "participants": [
10 {
11 "name": "Sarah Chen",
12 "email": "sarah.chen@company.com"
13 },
14 {
15 "name": "Mike Johnson",
16 "email": "mike.johnson@company.com"
17 },
18 {
19 "name": "Alex Rivera",
20 "email": "alex.rivera@company.com"
21 }
22 ]
23 },
24 {
25 "meetingId": "meeting_def456",
26 "title": "Client Check-in - Acme Corp",
27 "date": "2024-11-18T10:00:00Z",
28 "duration": 1800,
29 "summary": "Client reported 40% improvement in efficiency since implementation. Requested training for 5 new team members. Follow-up scheduled for Dec 5.",
30 "participants": [
31 {
32 "name": "Jennifer Wu",
33 "email": "jennifer@acmecorp.com"
34 },
35 {
36 "name": "Tom Davis",
37 "email": "tom.davis@company.com"
38 }
39 ]
40 }
41 ],
42 "total": 42,
43 "hasMore": true
44}

Response Properties

PropertyTypeDescription
meetingsarrayArray of meeting objects with metadata
meetings[].meetingIdstringUnique identifier for the meeting (use with other Fathom tools)
meetings[].titlestringMeeting title from calendar
meetings[].datestringISO 8601 timestamp of when the meeting occurred
meetings[].durationnumberMeeting duration in seconds
meetings[].summarystringAI-generated summary of meeting content
meetings[].participantsarrayArray of participant objects
meetings[].participants[].namestringParticipant’s full name
meetings[].participants[].emailstringParticipant’s email address
totalnumberTotal number of meetings matching the filter criteria
hasMorebooleanWhether there are more meetings beyond the limit

Common Use Cases

Use AI summaries to quickly review meeting outcomes without reading full transcripts.

Example:

"Show me summaries of my team meetings from this week"

Natural language workflow:

  • AI uses fathom_list_meetings with date filter for this week
  • Returns meetings with AI-generated summaries
  • You get instant overview of what was discussed

What you get:

  • Quick meeting recaps
  • Key decisions and outcomes
  • Action items at a glance
  • No need to read full transcripts

Discover meetings with specific participants before creating minds.

Example:

"Find all my Fathom meetings with client stakeholders
from the enterprise segment this quarter"

Natural language workflow:

  • AI lists meetings from Q4
  • Filters to those with external participants
  • Identifies enterprise client meetings
  • Provides list for selection

What you get:

  • Relevant meeting list
  • Participant details
  • Meeting summaries
  • IDs for transcript retrieval

Use summaries to find meetings discussing specific topics.

Example:

"Show me all meetings where we discussed API integration
or technical architecture"

Natural language workflow:

  • AI lists recent meetings
  • Searches summaries for keywords
  • Returns matching meetings

What you get:

  • Topic-filtered meeting list
  • Context from summaries
  • Participant information
  • Quick topic-based discovery

Error Responses

1{
2 "error": "Unauthorized",
3 "message": "Fathom authentication failed"
4}

Solution: Verify your Fathom API key is configured correctly. See the Fathom Setup Guide.

1{
2 "error": "Bad Request",
3 "message": "Invalid date format. Use YYYY-MM-DD"
4}

Solution: Ensure dates are in ISO 8601 format: YYYY-MM-DD.

1{
2 "error": "Rate Limit Exceeded",
3 "message": "Too many requests to Fathom API"
4}

Solution: Wait before retrying. Reduce request frequency or use date filtering.

1{
2 "meetings": [],
3 "total": 0,
4 "hasMore": false
5}

Solution: Not an error—no meetings match your criteria. Try:

  • Widening the date range
  • Removing date filters
  • Verifying meetings exist in Fathom
  • Checking account permissions

Best Practices

Leverage AI Summaries
  • Read summaries before downloading full transcripts
  • Use summaries for quick meeting recaps
  • Filter meetings by summary content
  • Identify key meetings without full transcript review
  • Use date ranges to narrow results
  • Start with recent meetings (last week/month)
  • Filter by participant for targeted discovery
  • Use limit parameter to control result size
  • List meetings first, then get transcripts selectively
  • Use participant info to identify mind creation targets
  • Check duration to prioritize substantive meetings
  • Save meeting IDs for later retrieval

Integration with Mind Reasoner

Typical Workflow

1

List Meetings

Use fathom_list_meetings to discover available meetings

2

Review Summaries

Read AI summaries to identify relevant meetings

3

Select Meetings

Choose meetings based on participants and topics

4

Get Transcripts

Use fathom_get_transcript for selected meetings

5

Create Minds

Build minds from transcript data

Natural language example:

"Show me my client meetings from last month, then create
minds for the client contacts who were most engaged"

See Also