Connect Front to Claude: Manage Team Workflows & Scheduling

If your team uses ChatGPT, check out our guide on connecting Front to ChatGPT or explore our broader architectural overview on connecting Front to AI Agents.

Front is the operational nervous system for teams that rely on shared inboxes - support, operations, account management, and logistics. Giving Claude read and write access to your Front instance allows you to build AI agents that can automatically triage incoming requests, draft context-aware replies based on historical thread data, and reassign conversations based on workload.

Connecting a Large Language Model (LLM) to Front requires a Model Context Protocol (MCP) server. This server acts as the translation layer between Claude's tool calling capabilities and Front's REST APIs. You can either spend weeks building, hosting, and maintaining this integration infrastructure yourself, or you can use a managed platform like Truto to dynamically generate a secure, authenticated MCP server URL in seconds.

This guide breaks down exactly how to use Truto to generate a secure MCP server for Front, connect it to Claude, and execute complex shared inbox workflows using natural language.

The Engineering Reality of the Front API

A custom MCP server is a self-hosted API gateway that maps natural language requests to HTTP verbs. While the MCP specification dictates how tools are presented to Claude, the real engineering challenge lies in handling the vendor-specific quirks of the target API.

Front's API is powerful but highly idiosyncratic. If you build a custom MCP server for Front, you are responsible for maintaining the entire integration lifecycle, which includes dealing with several Front-specific design patterns.

The 301 Redirect on Merged Conversations Front allows users to merge duplicate conversations. When this happens, the original conversation ID becomes obsolete. If your AI agent attempts to call an update method (like update_a_front_conversation_by_id) on an ID that has been merged, Front does not return a standard 400 or 404 JSON error. Instead, it returns an HTTP 301 Redirect to the new conversation ID. LLMs do not inherently know how to follow HTTP redirects. If your custom MCP server doesn't catch the 301, parse the Location header, and recursively retry the tool call with the new ID, your agent will fail silently or hallucinate a success state.

The Channel ID Requirement for Drafts In standard email APIs, you send a message to a thread. In Front, you cannot just append an email to a conversation. Front requires you to explicitly declare which channel (e.g., support@yourcompany.com vs billing@yourcompany.com) is originating the message. When calling the draft creation endpoints, your AI agent must first query the available channels, extract the correct channel_id, and inject it into the draft payload. Building a custom MCP server means writing the routing logic to ensure the LLM understands this strict relational hierarchy.

Tier-Based Rate Limiting and Backoff Front enforces strict API quotas based on your pricing tier (often capped at a specific number of requests per minute). When an AI agent enters a multi-step planning loop - for example, scanning fifty recent conversations to generate a daily summary - it can easily burn through that rate limit in seconds. When Front returns an HTTP 429 Too Many Requests, Truto passes that error directly back to the caller (Claude). Truto normalizes the upstream rate limit information into standard IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset). Truto does not automatically retry or absorb these rate limit errors. It is up to your agent framework to read the normalized headers and implement the correct exponential backoff before retrying the tool call.

By using Truto as your MCP layer, you bypass the need to write boilerplate code for authentication, pagination normalization, and schema mapping, allowing your engineering team to focus entirely on agent prompt design.

How to Generate a Front MCP Server

Truto derives MCP tools dynamically from your connected Front integration. Rather than writing tool definitions by hand, Truto reads the integration's internal resource schemas and documentation, automatically generating a list of JSON-RPC 2.0 compatible tools.

Each MCP server is scoped to a specific integrated account. You can create a Front MCP server either through the Truto UI or programmatically via the API.

Method 1: Via the Truto UI

For internal workflows or rapid prototyping, generating an MCP server from the dashboard is the fastest approach.

1. Log into your Truto dashboard and navigate to Integrated Accounts.
2. Select your connected Front account.
3. Click on the MCP Servers tab.
4. Click Create MCP Server.
5. Select your desired configuration (e.g., restrict to read methods, or filter by specific tags like conversations).
6. Copy the generated MCP Server URL (it will look like https://api.truto.one/mcp/a1b2c3d4...).

Method 2: Via the Truto API

For production deployments where you need to programmatically provision MCP servers for your end-users, you can call the Truto API. The API validates the configuration, generates a cryptographically hashed token, and provisions the server in real-time.

Make an authenticated POST request to /integrated-account/:id/mcp:

curl -X POST https://api.truto.one/integrated-account/<FRONT_ACCOUNT_ID>/mcp \
  -H "Authorization: Bearer <YOUR_TRUTO_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Front Support Agent Server",
    "config": {
      "methods": ["read", "write"],
      "tags": ["conversations", "drafts", "comments"]
    },
    "expires_at": "2026-12-31T23:59:59Z"
  }'

The response will contain the securely generated URL required by your MCP client:

{
  "id": "mcp_srv_9x8y7z6",
  "name": "Front Support Agent Server",
  "config": {
    "methods": ["read", "write"],
    "tags": ["conversations", "drafts", "comments"]
  },
  "expires_at": "2026-12-31T23:59:59.000Z",
  "url": "https://api.truto.one/mcp/abc123def456..."
}

How to Connect the MCP Server to Claude

Once you have your Truto MCP URL, you must register it with Claude so the model can discover and execute the Front tools.

Method A: Via the Claude UI

If you are using Claude Desktop or Claude Web (depending on your organization's Anthropic plan tier):

1. Open Claude and navigate to Settings -> Integrations (or Connectors).
2. Click Add MCP Server.
3. Paste the Truto MCP URL.
4. Save the configuration. Claude will instantly perform a tools/list handshake and register the Front tools.

Method B: Via Manual Config File (Claude Desktop)

For developers using the local Claude Desktop app, you can map the server using the claude_desktop_config.json file. Because Truto exposes a standard HTTP POST endpoint, you will use the official @modelcontextprotocol/server-sse wrapper to bridge the connection.

Open your config file (typically located at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS) and add the following:

{
  "mcpServers": {
    "front-truto": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "https://api.truto.one/mcp/abc123def456..."
      ]
    }
  }
}

Restart Claude Desktop. Look for the hammer icon in the chat interface to verify that the Front tools have loaded correctly.

Hero Tools for Front Automation

Truto exposes dozens of Front endpoints as MCP tools. By passing the exact schemas derived from the Front integration, Claude knows exactly which parameters are required and which are optional. Here are the highest-leverage tools for shared inbox automation.

list_all_front_conversations

This tool retrieves a reverse-chronological list of conversations. It is the primary entry point for AI agents searching for unassigned tickets, SLA breaches, or specific subject lines. The tool automatically handles pagination via the next_cursor parameter.

"Fetch the latest 10 unassigned conversations from Front. Filter the results to only show conversations that have been in the 'open' status for more than 24 hours."

get_single_front_conversation_by_id

Once an agent identifies a conversation, it uses this tool to pull the complete metadata profile, including custom fields, tags, assignees, and external links. This is critical for context gathering before drafting a reply.

"Get the full details for conversation ID 'cnv_12345'. Extract any custom fields related to 'Account Tier' and list all the tags applied to this thread."

list_all_front_conversation_messages

Conversations in Front are containers; messages are the actual email bodies, SMS texts, or chat payloads. This tool fetches the chronological message history for a given conversation ID, allowing Claude to read the actual back-and-forth dialogue.

"Read all the messages in conversation ID 'cnv_12345'. Summarize the customer's core complaint and identify any troubleshooting steps my team has already suggested."

create_a_front_draft

This tool allows Claude to generate a new outbound email draft within a specific channel. This is highly useful for proactive outreach or initiating a new support ticket on behalf of a user.

"Create a new draft in channel 'chn_789'. The subject should be 'Q3 QBR Follow-up' and the body should include a summary of the metrics we discussed. Assign the draft to me for review."

front_drafts_reply

This is the most frequently used tool for support automation. It creates a draft reply attached to the last message in an existing conversation. The AI can draft the response, leaving it in Front for a human agent to review and send.

"Draft a reply to the latest message in conversation 'cnv_12345'. Apologize for the delay, explain that the bug has been patched, and ask them to refresh their browser. Leave the draft mode as 'shared'."

create_a_front_conversation_comment

Internal comments are essential for team collaboration. AI agents can use this tool to leave summaries, translation notes, or suggested routing instructions internally without emailing the customer.

"Add an internal comment to conversation 'cnv_12345'. Tag teammate ID 'tea_456' and summarize the billing issue the customer is experiencing, noting that it requires a manual refund."

front_conversations_update_assignee

AI agents excel at triage. After analyzing the intent of a conversation, Claude can use this tool to route the thread to the appropriate human agent based on workload or expertise.

"Update the assignee for conversation 'cnv_12345' to teammate ID 'tea_999' because this request pertains to Enterprise API access."

For the complete inventory of available Front tools, query schemas, and return types, view the Front integration page.

Workflows in Action

MCP tools become exponentially more powerful when chained together. Because Claude understands the input and output schemas of every tool, it can pass data from one Front endpoint directly into another without any glue code.

Workflow 1: The Automated Triage Agent

Persona: Support Operations Manager looking to eliminate manual ticket routing.

"Check the shared support inbox for any new, unassigned conversations. Read the latest message in each thread. If the message is about billing or invoices, add the 'Billing' tag and assign it to the finance team lead. Otherwise, leave an internal comment summarizing the issue."

Execution Steps:

1. list_all_front_inboxes: Claude identifies the ID for the "Support" inbox.
2. list_all_front_inbox_conversations: Claude fetches the list of recent conversations within that specific inbox.
3. list_all_front_conversation_messages: For each conversation, the model reads the text of the latest message to determine intent.
4. create_a_front_conversation_tag: If the intent is billing-related, Claude applies the appropriate tag.
5. front_conversations_update_assignee: Claude reassigns the conversation to the specific user ID associated with the finance lead.
6. create_a_front_conversation_comment: For non-billing threads, Claude writes a short executive summary and posts it as an internal note.

Output: The shared inbox is fully triaged. Finance tickets are routed appropriately, and general support tickets have helpful AI summaries attached for the next available human.

Workflow 2: The Account Manager Prep Assistant

Persona: Enterprise Account Manager preparing for a client renewal call.

"Look up all recent conversations linked to the contact 'sarah@acmecorp.com'. Give me a timeline of every issue she has reported in the last 6 months, and then draft an email to her asking if those specific issues have been permanently resolved ahead of our call tomorrow."

graph TD
    A[list_all_front_contacts] --> B{Extract Contact ID}
    B --> C[list_all_front_contact_conversations]
    C --> D[list_all_front_conversation_messages]
    D --> E{LLM Summarization}
    E --> F[create_a_front_draft]

Execution Steps:

1. list_all_front_contacts: Claude searches for the email address to retrieve the Front contact_id.
2. list_all_front_contact_conversations: Claude pulls all historical threads associated with that contact.
3. list_all_front_conversation_messages: Claude iterates through the threads, reading the actual payload of the messages to build a contextual timeline of reported bugs or feature requests.
4. create_a_front_draft: Using the context gathered, Claude drafts a highly personalized email in the account manager's private channel, ready for review.

Output: The Account Manager opens Front to find a perfectly drafted email sitting in their drafts folder, complete with references to historical tickets.

Security and Access Control

When connecting powerful AI models to enterprise shared inboxes, access control is non-negotiable. Truto provides granular security features at the MCP token level to ensure your agents cannot accidentally mutate data they shouldn't access.

• Method Filtering (config.methods): You can restrict an MCP server to specific HTTP verbs. By passing methods: ["read"] during server creation, you ensure the LLM can only execute get and list operations, physically preventing the agent from sending emails, deleting tags, or mutating assignees.
• Tag Filtering (config.tags): Truto allows you to filter the exposed tools by operational categories. If you only want your agent to read documentation, you can pass tags: ["knowledge_base"]. The MCP server will dynamically exclude all conversational and teammate tools from the LLM's context.
• Secondary Authentication (require_api_token_auth): By default, an MCP token URL authorizes the request. For high-security environments, setting this flag to true requires the connecting client to also pass a valid Truto API token in the Authorization header. This ensures that even if the MCP URL is leaked in internal logs, it cannot be used without valid environment credentials.
• Automatic Expiration (expires_at): You can generate ephemeral MCP servers for temporary agent tasks or contractor access. Once the expires_at timestamp is reached, Truto automatically purges the token from the database and edge KV storage, immediately invalidating the server URL.

The Strategic Advantage of Managed MCP Infrastructure

Building AI features shouldn't require your team to become experts in Front's pagination quirks, channel ID requirements, or rate limiting headers. By utilizing a managed MCP server via Truto, you decouple your AI prompt engineering from the underlying REST integration code.

Your agents get native, real-time access to shared inboxes, drafts, and teammate data, while your infrastructure team gets centralized token management, logging, and granular access controls. You focus on building smarter support workflows; Truto handles the API translation.