For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
  • Workflows
    • Overview
  • Get Started
    • MCP Quick Start
    • API Quick Start
  • MCP Server
    • Overview
    • Installation
      • Overview
      • get_all_minds
      • search_minds
      • create_mind
      • get_signed_upload_url
      • upload_file_to_signed_url
      • create_snapshot
      • get_snapshot_status
      • simulate
      • gong_list_calls
      • gong_get_transcript
      • gong_get_call_details
      • fathom_list_meetings
      • fathom_get_transcript
      • fathom_get_meeting_details
      • fireflies_list_transcripts
      • fireflies_get_transcript
      • fireflies_search_transcripts
  • Direct API
    • Overview
    • Authentication
    • Error Handling
  • Core Concepts
    • Minds, Snapshots, & Simulations
    • SOC 2, HIPAA, & GDPR
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On this page
  • Available Tools
  • Integration Tools (Optional)
  • Gong Tools (3)
  • Fathom Tools (3)
  • Fireflies Tools (3)
  • Workflow
  • Discovery Phase (Optional)
  • Setup Phase
  • Workflow Phases
  • Setup Phase
  • Training Phase
  • Simulation Phase
  • Common Patterns
  • Discovering Existing Minds
  • First-Time Setup
  • Adding New Data
  • Running Multiple Simulations
  • Error Handling
  • See Also
MCP ServerTools Reference

Tools Reference

Complete documentation of all Mind Reasoner MCP tools
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Previous

get_all_minds

List all minds in your account
Next
Built with

Mind Reasoner provides eight MCP tools that work together in a logical sequence. You don’t need to memorize these tools—simply describe what you want in natural language, and Mind Reasoner will automatically select and execute the appropriate tools.

Available Tools

ToolOperationPurpose
get_all_mindsREADList all minds
search_mindsREADSearch for minds
create_mindCREATECreate digital entity
get_signed_upload_urlGETRequest upload URL
upload_file_to_signed_urlUPLOADUpload transcript file
create_snapshotCREATEStart AI training
get_snapshot_statusGETCheck training progress
simulateRUNRun predictions

Most commonly used: get_all_minds, search_minds, and simulate for working with existing minds. Setup tools are typically used once per mind.

Integration Tools (Optional)

When you connect Gong, Fathom, or Fireflies to your MCP server, up to 9 additional tools become available alongside the core Mind Reasoner tools. These tools let you access existing transcript repositories without manual file uploads.

Gong Tools (3)

ToolPurpose
gong_list_callsList sales call recordings with date filtering
gong_get_transcriptGet detailed transcripts with speaker attribution
gong_get_call_detailsGet participant details, topics, and call analytics

Fathom Tools (3)

ToolPurpose
fathom_list_meetingsList meetings with summaries and participants
fathom_get_transcriptGet transcripts with speaker details
fathom_get_meeting_detailsGet comprehensive meeting information

Fireflies Tools (3)

ToolPurpose
fireflies_list_transcriptsList meeting transcripts with date filtering
fireflies_get_transcriptGet detailed transcripts with speaker analytics
fireflies_search_transcriptsSearch transcripts by keyword
Graceful Degradation

Integration tools only appear when the corresponding credentials are configured. The MCP server works perfectly without any integrations—they’re completely optional and additive.

Learn more:

  • Integration Overview - What integrations are available and why use them
  • Gong Setup - How to connect to Gong
  • Fathom Setup - How to connect to Fathom
  • Fireflies Setup - How to connect to Fireflies

Workflow

Discovery Phase (Optional)

Explore existing minds before creating new ones:

get_all_minds or search_minds
↓ Returns: List of minds with IDs

Setup Phase

Create and train new minds:

1. create_mind
↓ Returns: mindId, digitalTwinId
2. get_signed_upload_url
↓ Returns: signedUrl, artifactId
3. upload_file_to_signed_url
↓ Confirms upload
4. create_snapshot
↓ Returns: snapshotId
5. get_snapshot_status
↓ Waits for: status = "completed"
6. simulate
↓ Returns: AI predictions

Your AI assistant handles the workflow automatically. Understanding this sequence helps when debugging or building custom integrations.

Workflow Phases

Setup Phase

Create the digital entity and upload conversation data.

AspectDetails
Toolscreate_mind, get_signed_upload_url, upload_file_to_signed_url
Time2-5 minutes
FrequencyOnce per mind

Training Phase

Process uploaded data into a trained AI model.

AspectDetails
Toolscreate_snapshot, get_snapshot_status
Time5-15 minutes (asynchronous)
FrequencyOnce per dataset (or when updating)

Simulation Phase

Run unlimited predictions using the trained model.

AspectDetails
Toolssimulate
TimeSeconds per simulation
FrequencyUnlimited

Common Patterns

Discovering Existing Minds

Check what minds already exist before creating new ones.

Time: Seconds

Natural language example:

"Show me all minds I've created"
"Find minds related to sales"
"Do I already have a mind for Sarah?"

First-Time Setup

Run the core workflow to create a mind, upload data, train the model, and run your first simulation.

Time: 10-20 minutes (mostly waiting for training)

Natural language example:

"Create a mind for my sales manager using the transcript
at /Documents/sales-calls.vtt, then simulate how they'd
handle pricing objections."
1

Setup

Create mind, get upload URL, upload transcript

2

Train

Create snapshot, wait for completion

3

Simulate

Run your first prediction

Adding New Data

Update an existing mind by skipping step 1 (mind already exists) and running steps 2-6 with new data.

Time: 7-17 minutes

1

Upload

Get new upload URL, upload additional transcript

2

Retrain

Create new snapshot with updated data

3

Test

Run simulations with improved model

Running Multiple Simulations

After training is complete, run simulate as many times as needed without re-training.

Time: Seconds per simulation

Perfect for:

  • Testing multiple scenarios
  • Training team members
  • QA validation
  • Batch predictions

Error Handling

All tools return standard HTTP error responses. Each tool’s documentation page includes detailed error handling guidance.

401 Unauthorized

Invalid or missing API key. Verify key in MCP server configuration.

404 Not Found

Resource ID is incorrect or deleted. Double-check IDs (case-sensitive).

429 Rate Limit Exceeded

Too many requests. Wait for the time specified in retryAfter before retrying.

400 Bad Request

Invalid parameters or format. Check the error details field for specifics.

See Also

MCP Quick Start

Complete walkthrough from setup to simulation

create_mind

Start here: Create your first digital mind

simulate

Most commonly used: Run AI simulations