ChatGPT Enterprise, Team, and Education users can connect to Tallyfy through its MCP server. You manage workflows using natural language - type what you need and ChatGPT handles the API calls.
This guide covers setup, what works well, and what doesn’t.
ChatGPT MCP support status
Availability - Team, Enterprise, and Education plans support custom MCP server connections via Developer Mode. Plus and Pro only get pre-built connectors.
Access method - Developer Mode and Apps (formerly “connectors”)
Operations - Full read/write via Developer Mode, read-only via Deep Research
Authentication - OAuth 2.1 with PKCE (per RFC 8414) via /.well-known/oauth-authorization-server
Scopes - Dot notation like mcp.tasks.read, mcp.processes.write, mcp.templates.read
Apps SDK - Build custom UIs alongside MCP servers
Developer Mode must be enabled in ChatGPT settings to use MCP servers in regular conversations.
Prerequisites
ChatGPT Enterprise, Team, or Education subscription
A Tallyfy account with organization access
Access to create custom apps in ChatGPT
Familiarity with OAuth authentication flows
Setting up Tallyfy MCP server with ChatGPT
Access ChatGPT’s apps settings
In ChatGPT Enterprise/Team/Education, go to Settings, then Apps. You can also reach this through Deep Research, then Custom Apps.
Create a new MCP app
Click “Add Custom App” and enter the MCP server URL:
https://mcp.tallyfy.com
ChatGPT discovers Tallyfy’s OAuth endpoints automatically through the /.well-known/oauth-authorization-server metadata endpoint (RFC 8414).
Authenticate via OAuth 2.1
ChatGPT initiates OAuth 2.1 with PKCE using Dynamic Client Registration (RFC 7591). You’ll be redirected to Tallyfy’s login page to authorize access.
During authorization, you’ll select which organization to connect and approve scopes like mcp.tasks.read, mcp.processes.write, etc.
Security tip - Consider using an account with only the permissions ChatGPT needs rather than a full admin account.
Set up app instructions
Add instructions so ChatGPT knows how to interact with Tallyfy:
You are connected to a Tallyfy organization's MCP server. Use this connection to:
- Search for tasks, processes, and templates
- Retrieve workflow information
- Analyze template health and suggest improvements
- Help users manage their work
Always confirm destructive actions before executing.
Format responses clearly with relevant details.
Test the connection
Start a conversation and test with a simple query:
"Show me all active processes in Tallyfy"
Publish to workspace (optional)
For team-wide access, publish the app to your ChatGPT workspace. Admins can manage visibility and permissions in workspace settings.
Practical examples
Finding overdue tasks
Prompt:
Using Tallyfy, find all overdue tasks assigned to the marketing team and summarize them by priority.
What happens:
ChatGPT calls search_for_tasks with an overdue filter
Filters results by marketing team assignment
Groups findings by priority
Returns a formatted summary
Analyzing template health
Prompt:
Analyze our "Customer Onboarding" template in Tallyfy and suggest improvements.
What happens:
Calls get_template to pull template details
Runs assess_template_health for analysis
Identifies bottlenecks or redundant steps
Suggests specific optimizations
Creating workflow documentation
Prompt:
Generate documentation for our "Invoice Processing" workflow, including all steps and form fields.
What happens:
Retrieves template structure using get_template
Lists all steps with descriptions
Documents form fields for each step
Includes automation rules
Limitations of the text-based UI
ChatGPT’s text interface hits real walls with certain Tallyfy features:
Form field interactions
Dropdowns, multi-selects, and complex inputs don’t translate well to plain text. A dropdown with 20+ options becomes a wall of text. Date pickers become “type the date in YYYY-MM-DD format.” File uploads aren’t possible at all.
Visual workflow representation
ChatGPT can’t display Tallyfy’s visual process tracker or workflow diagrams. You lose step-by-step flow visualization, real-time progress updates, and dependency mapping.
Bulk operations
Reassigning 50 tasks means 50 individual commands (or one complex bulk instruction that might fail). Filtering and sorting large process lists produces pages of text. Batch template updates give no visual confirmation.
Real-time collaboration
ChatGPT’s turn-based model doesn’t support live updates. Your teammate finishes a task - you won’t know until you ask again. Notifications get buried in conversation history. Two people can’t work on the same template simultaneously.
Features that don’t work well in ChatGPT
Visual process tracker
Tallyfy’s Tracker View shows running processes with progress bars and color-coded statuses. ChatGPT can’t render any of it - so monitoring process health at a glance, spotting bottlenecks across workflows, and tracking SLA compliance visually aren’t possible.
Template builder
Creating and editing templates through ChatGPT is awkward. No drag-and-drop for reordering steps, no visual branching logic, and no preview of the template structure.
Advanced filtering and views
Tallyfy’s filtering doesn’t translate to text. You can’t save custom views, multi-dimensional filtering requires complex natural language, and there are no visual indicators for results.
Analytics
No charts, graphs, or trend visualizations. For visual analytics, connect your BI tools to Tallyfy Analytics instead.
Where ChatGPT shines with Tallyfy MCP
Natural language search
"Find all templates related to employee onboarding that include background check steps"
ChatGPT searches template names, descriptions, and step content - finding what you need in seconds.
Form field generation
"Add appropriate form fields to collect customer feedback in our support process"
ChatGPT examines your process context and suggests fields with appropriate validation rules.
What-if automation scenarios
"If I set up an automation to assign tasks based on deal value, show me how it would route these 5 example deals"
ChatGPT runs the simulation without touching your live processes.
Process documentation updates
"Here's our updated SOX compliance procedure. Update our audit template to match these new requirements, highlighting what changed"
ChatGPT reads your document, identifies changes, and updates your template accordingly.
Template optimization from task patterns
"Analyze one-off tasks added to our hiring processes last month and suggest which should be added to the template"
ChatGPT spots patterns and suggests which ad-hoc tasks deserve a permanent place in your template.
Complex query answering
"Which step in our sales process has the longest average completion time, and which team members are fastest at completing it?"
ChatGPT analyzes your data and returns the answer with links to the specific processes it examined.
Security considerations
Access control - Use accounts with minimal required permissions. Consider a dedicated service account for the ChatGPT integration.
Data sensitivity - Review which templates and processes contain sensitive information. Audit data access logs regularly.
Prompt injection - Be cautious of templates or data containing prompt-like text. Verify unexpected ChatGPT behaviors and report suspicious activity to both OpenAI and Tallyfy support.
Known issues
Deep Research is read-only - Enable Developer Mode in ChatGPT Settings for write operations
Session timeouts - Long-running queries may timeout after 60 seconds
Context window limits - Large template structures may exceed token limits
No real-time updates - Changes in Tallyfy aren’t reflected until the next query
Limited file handling - Can’t process attachments or generate files
No webhook support - Can’t trigger or respond to Tallyfy webhooks
Terminology - OpenAI renamed “connectors” to “apps” in December 2025, so some docs may use the old term
Best practices
Be specific - Instead of “show me tasks,” try “show me high-priority tasks assigned to John due this week”
Batch requests - Combine related queries in a single prompt
Ask for analysis - Request insights and patterns, not just raw data
Save effective prompts - Reuse prompts that work well for common workflows
Combine with Tallyfy’s UI - Use ChatGPT for analysis and planning, then execute in Tallyfy’s visual interface
What’s next
Write capabilities - Already available via Developer Mode
OAuth 2.1 - Live now with PKCE and Dynamic Client Registration
Apps SDK - Build custom UIs alongside MCP servers
Real-time sync - Live updates between ChatGPT and Tallyfy (coming soon)
OpenAI and Tallyfy are both expanding MCP features. MCP is governed by the Agentic AI Foundation under the Linux Foundation.
ChatGPT users on Plus or higher plans can connect to Tallyfy’s MCP server at https://mcp.tallyfy.com using OAuth 2.1 with PKCE to search and manage tasks and processes and templates and automations through plain language — with over 30 available tools covering everything from task creation to template health audits — though the text-based interface cannot replace Tallyfy’s visual workflow tracking or handle file uploads and
Tallyfy’s MCP Server lets you control workflows through plain English in any major AI platform like ChatGPT or Claude or Gemini or Copilot using the open Model Context Protocol standard to search tasks and launch processes and manage templates and set up automations without needing any API knowledge.
Claude Desktop provides the most mature MCP client experience for connecting to Tallyfy’s API through natural language and this guide covers setup via manual JSON configuration or one-click Desktop Extensions along with practical workflow examples like task management and process analysis and hybrid automation combining text-based MCP data queries with Claude Computer Use for visual UI tasks.
Microsoft Copilot Studio connects to Tallyfy’s hosted MCP server through Power Platform custom connectors and OpenAPI specifications enabling enterprise teams to automate workflows and manage processes via natural language while benefiting from strong security features like VNet isolation and DLP policies and multi-agent orchestration across HR and IT and training departments.