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Using Tallyfy MCP server with Google Gemini

Overview

Google Gemini brings powerful AI capabilities to Tallyfy workflows through MCP integration. With official MCP support announced in December 2025, Gemini users can now connect to Tallyfy’s MCP server using the Gemini CLI or through Google Cloud’s managed MCP infrastructure.

This guide shows you how to set up the integration, what works well, and how to get the most out of Gemini with Tallyfy.

Google Gemini MCP support status

Here’s the current state of MCP support in Gemini:

  • Official support: MCP announced December 2025 for Gemini models
  • Managed MCP servers: Google Cloud provides managed servers for Maps, BigQuery, Compute Engine, Kubernetes Engine
  • Gemini CLI: FastMCP integration for local MCP server connections
  • Transport methods: Streamable HTTP (aligned with industry standard)
  • Apigee integration: Translate standard APIs to MCP servers
  • Planned rollout: Cloud Run, Storage, AlloyDB, Cloud SQL, Spanner, Looker, Pub/Sub

Note: Google is a supporting member of the Agentic AI Foundation, ensuring continued commitment to MCP standards.

Prerequisites

You’ll need these before getting started:

  • Google Cloud account with Gemini API access
  • Tallyfy API key (from your Tallyfy organization settings)
  • Gemini CLI installed (for local MCP connections)
  • Basic familiarity with Google Cloud console

Setting up Tallyfy MCP Server with Google Gemini

  1. Install Gemini CLI

    The Gemini CLI provides the fastest way to connect to MCP servers:

    Terminal window
    # Install via npm
    npm install -g @google/gemini-cli
    

    # Verify installation
    gemini --version

  2. Configure MCP server connection

    Create a configuration file for the Tallyfy MCP server:

    {
      "mcpServers": {
        "tallyfy": {
          "url": "https://mcp.tallyfy.com",
          "transport": "streamable-http",
          "auth": {
            "type": "oauth2",
            "authorization_url": "https://go.tallyfy.com/mcp/oauth/authorize",
            "token_url": "https://go.tallyfy.com/mcp/oauth/token",
            "scopes": ["mcp:tasks", "mcp:processes", "mcp:templates", "mcp:users", "mcp:read", "mcp:write"]
          }
        }
      }
    }

  3. Authenticate with Tallyfy

    Run the OAuth flow to connect your Tallyfy account:

    Terminal window
    gemini mcp auth tallyfy

    This opens a browser window to authorize Gemini’s access to your Tallyfy workspace.

  4. Verify the connection

    Test the MCP connection with a simple query:

    Terminal window
    gemini chat --mcp tallyfy
    > Show me all open tasks in Tallyfy

  5. Use in Google Cloud (optional)

    For enterprise deployments, configure Tallyfy as a managed MCP server in Google Cloud:

    # gemini-mcp-config.yaml
    apiVersion: aiplatform.googleapis.com/v1
    kind: McpServerConfig
    metadata:
      name: tallyfy-mcp
    spec:
      endpoint: https://mcp.tallyfy.com
      authentication:
        oauth2:
          clientId: ${TALLYFY_CLIENT_ID}
          authorizationEndpoint: https://go.tallyfy.com/mcp/oauth/authorize
          tokenEndpoint: https://go.tallyfy.com/mcp/oauth/token

Practical demonstrations

Example 1: Task management via natural language

User prompt:

Using Tallyfy, find all overdue tasks and create a summary grouped by assignee.

Gemini with MCP will:

  1. Connect to Tallyfy’s MCP server
  2. Execute the search_tasks tool with overdue filter
  3. Group results by assignee
  4. Generate a formatted summary

Example 2: Process analytics

User prompt:

Analyze our "Customer Onboarding" processes from the last month and identify bottlenecks.

Gemini with MCP will:

  1. Query process history using MCP tools
  2. Calculate step completion times
  3. Identify steps with longest durations
  4. Provide optimization recommendations

Example 3: Template optimization

User prompt:

Review our "Invoice Processing" template and suggest improvements based on best practices.

Gemini with MCP will:

  1. Retrieve template structure using get_template
  2. Analyze step dependencies and automation rules
  3. Compare against workflow best practices
  4. Suggest specific optimizations

Unique Gemini features

1. Thinking capabilities

Gemini’s extended thinking mode provides deeper analysis:

  • Multi-step reasoning for complex workflow decisions
  • Detailed explanations of recommendations
  • Consideration of edge cases and exceptions

2. Google Cloud integration

Deep integration with Google’s ecosystem:

  • BigQuery: Analyze Tallyfy data alongside your data warehouse
  • Cloud Run: Deploy custom MCP logic at scale
  • Pub/Sub: Event-driven workflow triggers

3. Multimodal capabilities

Gemini can process multiple input types:

  • Analyze uploaded documents within workflow context
  • Process images for visual inspection workflows
  • Combine text and visual analysis for comprehensive reviews

4. Vertex AI Agent Development Kit

Build sophisticated agents that combine Tallyfy with other services:

  • Multi-agent orchestration
  • Custom tool development
  • Enterprise governance and monitoring

Limitations

Some things to keep in mind:

1. Feature rollout

Current state: MCP support is new (December 2025)

Limitations:

  • Some managed MCP servers still rolling out
  • Regional availability varies
  • Enterprise features may require additional configuration

2. Text-based interface constraints

Same as other AI platforms:

  • No visual process tracker display
  • Complex forms become text-based interactions
  • Real-time updates require explicit queries

3. Google Cloud dependencies

For managed MCP servers:

  • Requires Google Cloud account
  • Some features need specific IAM permissions
  • Billing applies for Cloud usage

Ideal use cases

1. Data-driven workflow optimization

Strength: Combine BigQuery analytics with Tallyfy process data.

Example:

Correlate customer support ticket data from BigQuery with our support process completion times in Tallyfy. Identify patterns and suggest process improvements.

2. Multi-service automation

Strength: Orchestrate across Google Cloud and Tallyfy.

Example:

When a new document is uploaded to Cloud Storage, trigger the document review process in Tallyfy and notify the team via Gmail.

3. Intelligent process monitoring

Strength: Gemini’s analytical capabilities for workflow insights.

Example:

Monitor all active processes and alert me to any that are at risk of missing their SLA, with root cause analysis.

Security considerations

  1. Authentication

    • OAuth 2.1 with PKCE for secure token exchange
    • Tokens stored securely in Google’s infrastructure
    • Refresh tokens for persistent connections

  2. Data handling

    • Data processed according to Google’s AI policies
    • Enterprise controls via Vertex AI
    • Audit logging available in Cloud Logging

  3. Network security

    • All traffic over HTTPS
    • VPC Service Controls for enterprise
    • Private connectivity options available

Best practices

  1. Start with Gemini CLI: Fastest way to test and iterate
  2. Use structured prompts: Gemini performs best with clear, specific instructions
  3. Leverage thinking mode: Enable for complex analytical tasks
  4. Monitor usage: Track API calls and costs in Google Cloud Console
  5. Implement error handling: Build retry logic for production integrations

Future outlook

Google’s MCP roadmap includes:

  • Expanded managed servers: Cloud Run, Storage, AlloyDB, Cloud SQL, Spanner, Looker, Pub/Sub
  • Apigee MCP translation: Convert any API to MCP server automatically
  • Enhanced Vertex AI integration: Deeper agent development capabilities
  • Enterprise governance: Advanced security and compliance features

As a founding member of the Agentic AI Foundation, Google is committed to the continued evolution of MCP as the industry standard.

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