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MCP Server

Tallyfy MCP Server (Preview)

Feature in Development

The Tallyfy MCP Server is currently in development and subject to change. The features and capabilities described here represent the current state of development and may evolve before the official release.

Tallyfy’s MCP (Model Context Protocol) Server enables you to interact with your Tallyfy workflows using natural language through any AI platform that supports MCP. Instead of learning specific API endpoints or using web interfaces, you can simply describe what you want to accomplish, and the AI will execute the appropriate Tallyfy actions on your behalf.

What is MCP?

The Model Context Protocol (MCP) is an open standard that allows AI assistants to securely connect to external systems and data sources. It provides a standardized way for AI models to access and interact with tools and resources while maintaining security and control.

How Tallyfy MCP Server works

The Tallyfy MCP Server acts as a bridge between AI assistants (like Claude, ChatGPT, or others that support MCP) and your Tallyfy organization. You provide your Tallyfy API token to authenticate the connection, and then you can:

  • Ask questions about your processes and tasks in natural language
  • Create and manage tasks through conversation
  • Analyze templates and get suggestions for improvements
  • Search across your organization’s workflows
  • Manage users and guests

Example interactions:

  • “Show me all overdue tasks for John”
  • “Create a task for reviewing the quarterly budget, due next Friday”
  • “What templates do we have for customer onboarding?”
  • “Analyze the health of our ‘Employee Onboarding’ template”

Current capabilities (Preview)

Based on the current development state, the MCP Server provides access to the following functionality organized by category:

Search and discovery tools

Search across your organization:

  • search_for_tasks - Find tasks using natural language queries
  • search_for_processes - Locate specific processes by description or criteria
  • search_for_templates - Discover templates that match your needs

Examples:

  • “Find all tasks related to budget approval”
  • “Search for processes containing ‘customer onboarding’”
  • “Show me templates for HR workflows”

User and access management

Organization members:

  • get_organization_users - List all members in your organization
  • get_organization_users_list - Get a simplified list of users
  • invite_user_to_organization - Add new members to your Tallyfy organization

Guest management:

  • get_organization_guests - View all guests with access to your workflows
  • get_organization_guests_list - Get a simplified guest list

Helper functions:

  • resolve_user_ids - Convert user names or emails to internal IDs
  • resolve_group_ids - Convert group names to internal identifiers

Task management and tracking

Personal and team tasks:

  • get_my_tasks - Retrieve your assigned tasks
  • get_user_tasks - View tasks assigned to specific team members
  • get_tasks_for_process - List all tasks within a particular process

Task creation:

  • create_task_from_text - Create new tasks using natural language descriptions with automatic due date extraction

Process monitoring:

  • get_organization_runs - View all running processes with filtering options

Template design and optimization

Template analysis:

  • get_template - Retrieve detailed information about specific templates
  • assess_template_health - Get comprehensive analysis of template effectiveness
  • get_step_dependencies - Understand how steps relate to each other
  • suggest_step_deadline - Get AI-powered deadline recommendations

Form field management:

  • add_form_field_to_step - Add new form fields to template steps
  • update_form_field - Modify existing form field properties
  • move_form_field - Relocate form fields between steps
  • delete_form_field - Remove unnecessary form fields
  • suggest_form_fields_for_step - Get AI suggestions for useful form fields

Dropdown field management:

  • get_dropdown_options - View available options for dropdown fields
  • update_dropdown_options - Modify dropdown choices

Template structure:

  • add_assignees_to_step - Assign members or guests to template steps
  • edit_description_on_step - Update step descriptions and instructions
  • add_step_to_template - Insert new steps into templates

Automation and workflow logic

Automation rules:

  • create_automation_rule - Set up new automations for templates
  • update_automation_rule - Modify existing automation logic
  • delete_automation_rule - Remove automation rules
  • analyze_template_automations - Get insights into current automation setup

Optimization:

  • consolidate_automation_rules - Simplify complex automation setups
  • suggest_automation_consolidation - Get recommendations for automation improvements
  • get_step_visibility_conditions - Understand when steps appear or hide

Kickoff optimization:

  • suggest_kickoff_fields - Get recommendations for process launch form fields

System utilities

Server management:

  • get_server_status - Check MCP server health and connectivity
  • get_available_tools - List all available MCP tools and their descriptions

Security and authentication

The MCP Server requires your Tallyfy API token for authentication. This ensures that:

  • Only authorized users can access your organization’s data
  • All actions are performed with your permissions and access levels
  • Audit trails maintain proper attribution of actions
  • Data security follows Tallyfy’s existing security protocols

Getting started (When available)

Once the MCP Server is officially released, you’ll be able to:

  1. Set up the connection using your Tallyfy API credentials
  2. Configure your AI assistant to connect to the Tallyfy MCP Server
  3. Start conversing with your Tallyfy data using natural language
  4. Perform complex workflows through simple descriptions

Available MCP integrations

We provide detailed guides for connecting Tallyfy’s MCP Server with popular AI platforms:

Future capabilities

The development roadmap includes expanding the MCP Server to support:

  • Process launching and management
  • Advanced reporting and analytics
  • Integration with other middleware platforms
  • Custom workflow automation through conversational interfaces
  • Team collaboration features

Technical architecture

The current implementation includes:

  • Core SDK - Python-based SDK for Tallyfy API interaction
  • MCP Server - Model Context Protocol server implementation
  • Tool Framework - Structured tool definitions for AI interaction
  • Authentication Layer - Secure token-based API access
  • Natural Language Processing - Smart parsing of user intents and data extraction

Major limitations of text-based AI interfaces

While MCP enables powerful capabilities, traditional text-based AI chat interfaces present significant limitations when working with Tallyfy’s rich workflow features:

Visual interface constraints

Text-based AI chat cannot effectively represent Tallyfy’s visual elements:

  • No visual process tracker: Cannot display Tallyfy’s bird’s-eye view of running processes with visual progress indicators
  • Missing template builder: Cannot show the drag-and-drop template interface or visualize step dependencies and branching logic
  • Lack of aggregated views: Cannot present easily filterable views to see processes and tasks at a glance
  • No real-time progress: Cannot show live updates as team members complete tasks or processes advance

Form field interaction challenges

Tallyfy’s rich form fields become cumbersome in plain text:

  • Dropdown fields: When a task has dropdown boxes with multiple options, text-based AI must list all options as plain text, making selection tedious
  • Multi-select inputs: Complex form inputs lose their intuitive interface when reduced to text commands
  • Date/time pickers: Visual calendar and time selection tools must be replaced with manual text entry
  • File uploads: Cannot properly handle file upload fields through conversational interface

Assignment and collaboration barriers

Tallyfy’s sophisticated assignment features don’t translate well to text:

  • Assignee selection: Cannot easily pick assignees as Tallyfy’s UI suggests members, guests, job titles, and groups with visual indicators
  • Bulk operations: Managing multiple assignments or reassigning many tasks becomes inefficient through individual text commands
  • Team collaboration: Real-time collaborative features like simultaneous template editing are reduced to sequential interactions

Template creation limitations

Building and editing templates through text chat is extremely limited:

  • Step visualization: Cannot see all steps in a template at once or understand their relationships
  • Reordering steps: No drag-and-drop capability; must use complex text commands
  • Automation configuration: Complex automation rules are difficult to set up through conversational interfaces
  • Preview functionality: Cannot visually preview how the template will function before deployment

Ideal use cases for MCP with text-based AI

Despite limitations, MCP excels at specific workflows where text-based interaction is actually advantageous:

Natural language search and discovery

MCP shines when finding information using conversational queries:

  • Template search: “Find all templates related to employee onboarding that include background check steps”
  • Process discovery: “Show me all customer onboarding processes that took longer than 5 days to complete”
  • Task queries: “Which tasks are overdue and assigned to the sales team?”
  • Cross-reference search: “Find templates that use the ‘Budget Approval’ form field”

Intelligent template generation

AI excels at creating structured content from unstructured inputs:

  • Auto-creating form fields: Upload existing forms or documents and let AI automatically generate appropriate form fields with validation rules
  • Flowchart conversion: Study flowcharts or process diagrams to automatically create templates with proper step sequences
  • Automation generation: Analyze business rules in plain language and convert them to automation rules
  • Bulk field creation: Generate multiple related form fields based on document analysis

What-if scenario testing

MCP enables powerful simulation capabilities:

  • Automation testing: “If I set up an automation to assign tasks based on deal value, show me how it would route these 5 example deals”
  • Process optimization: “What would happen if we removed the approval step for purchases under $1,000?”
  • Workload analysis: “Show me how task distribution would change if we reassigned all of John’s templates to Sarah”
  • Time estimation: “Based on historical data, estimate completion time if we add a review step”

Intelligent process updates

AI excels at understanding changes and applying them systematically:

  • Document-based updates: “Here’s our updated SOX compliance procedure. Update our audit template to match these new requirements”
  • Change highlighting: AI can identify exactly what changed and update only the affected parts
  • Version comparison: Compare old and new process documents to generate precise template modifications
  • Bulk updates: Apply consistent changes across multiple templates based on policy updates

Pattern recognition and optimization

MCP can identify improvement opportunities from actual usage:

  • Ad-hoc task analysis: “Look at one-off tasks added to hiring processes last month and suggest which should be added to the template”
  • Bottleneck identification: “Analyze which steps consistently cause delays and suggest optimizations”
  • Template consolidation: “Find similar templates that could be merged into a single, more flexible template”
  • Best practice extraction: “Identify common patterns in high-performing processes and suggest template improvements”

Complex reporting with citations

AI provides detailed analysis with direct references:

  • Performance queries: “Which step in our sales process has the longest average completion time, and which team members are fastest?”
  • Compliance reporting: “Show all instances where required approvals were skipped, with links to specific processes”
  • Trend analysis: “How has our customer onboarding time changed over the last 6 months, and what factors contributed?”
  • Cross-functional insights: “Which departments most frequently add ad-hoc tasks, and what types of tasks are they?”

Stay tuned for official release announcements and detailed setup instructions as this feature moves from development to production.

Mcp Server > Using Tallyfy MCP Server with ChatGPT

ChatGPT Enterprise Team and Education users can now connect to Tallyfy’s MCP server through Deep Research enabling natural language workflow management with powerful search and analysis capabilities while being limited by text-based interactions that lack visual process tracking form field interactions and real-time collaboration features making it best suited as a complement to Tallyfy’s native interface rather than a replacement.

Mcp Server > Using Tallyfy MCP Server with Claude (Text Chat)

Claude Desktop provides native MCP integration enabling seamless workflow management through natural language commands that connect directly to Tallyfy’s API for task searching process analysis and automated reporting while maintaining security through local configuration and offering unique features like persistent project context and local file system access.

Mcp Server > Using Tallyfy MCP Server with Microsoft Copilot Studio

Microsoft Copilot Studio provides enterprise-grade MCP support for integrating Tallyfy’s workflow management capabilities with AI agents enabling organizations to automate processes manage workflows and interact with business data using natural language while leveraging Microsoft’s ecosystem security features and multi-agent orchestration capabilities.

Miscellaneous > Glossary

This comprehensive glossary defines essential workflow platform terminology to help teams understand Tallyfy’s features including administrators with full access permissions agentic workflows using AI agents analytics for performance insights API integrations automation rules conditional logic templates processes tasks assignments and various user roles for effective business process management and digital transformation.
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