tomtom-automation
by ComposioHQInstall and use tomtom-automation to run TomTom tasks through Composio Rube MCP. Learn setup, connection checks, tool discovery, and schema-first workflow steps.
This skill scores 66/100, which makes it an acceptable but limited listing candidate. Directory users can understand when to use it and how an agent should begin TomTom automation through Rube MCP, but the listing should be treated as a thin connector-oriented skill rather than a rich, task-specific workflow package.
- Valid frontmatter clearly declares the skill name, purpose, and required MCP dependency: `mcp: [rube]`.
- Prerequisites and setup steps explain that Rube MCP must be connected, `RUBE_SEARCH_TOOLS` must be available, and a TomTom connection must be activated through `RUBE_MANAGE_CONNECTIONS`.
- The skill gives a repeatable discovery-first execution pattern, explicitly instructing agents to call `RUBE_SEARCH_TOOLS` before running workflows to obtain current schemas and tool slugs.
- Workflow guidance appears mostly generic to Rube MCP tool discovery rather than TomTom-specific operational examples, so users may still need to infer exact task flows after schema lookup.
- No support files, scripts, references, README, or install command are included beyond SKILL.md, limiting adoption guidance and independent verification.
Overview of tomtom-automation skill
What tomtom-automation does
tomtom-automation is a Claude skill for running TomTom-related actions through Composio’s Rube MCP server. Instead of hard-coding a single TomTom API workflow, the skill instructs the agent to discover the currently available TomTom tools first, check authentication, then execute the selected operation using the latest Rube tool schema.
This matters because Composio tool inputs can change. The main value of the tomtom-automation skill is not a long library of examples; it is a safer operating pattern for TomTom automation: discover tools, verify connection, inspect schemas, then run.
Best-fit users and jobs
Use this skill if you want an AI agent to help with TomTom operations inside an MCP-enabled client, especially when you already use Composio/Rube for app integrations. It is best suited for workflow automation tasks where the agent needs to choose from available TomTom toolkit actions rather than rely on a stale prompt.
Typical jobs include preparing a TomTom automation request, checking whether the TomTom connection is active, finding the correct tool for a mapping or location task, and executing the workflow with the current required fields.
Important adoption constraints
The tomtom-automation skill requires Rube MCP. Your client must be able to connect to https://rube.app/mcp, and RUBE_SEARCH_TOOLS must be available. You also need an active TomTom connection through RUBE_MANAGE_CONNECTIONS with toolkit tomtom.
This is not a standalone TomTom SDK, command-line utility, or direct wrapper around TomTom REST APIs. If you need offline scripts, custom API authentication, or deterministic code without an MCP runtime, this skill is not the right starting point.
How to Use tomtom-automation skill
tomtom-automation install and setup path
Install the skill from the GitHub skill repository:
npx skills add ComposioHQ/awesome-claude-skills --skill tomtom-automation
Then configure Rube MCP in your AI client by adding:
https://rube.app/mcp
Before asking for any TomTom action, confirm that the MCP server exposes RUBE_SEARCH_TOOLS. Next, use RUBE_MANAGE_CONNECTIONS with toolkit tomtom and complete the returned authentication flow if the connection is not active. Do not skip this step: most failed tomtom-automation usage comes from asking the agent to run a tool before the TomTom connection is ready.
Prompt inputs that produce better results
A weak prompt is: “Use TomTom to automate this.”
A stronger prompt gives the agent the job, location data, constraints, and expected output:
“Use the tomtom-automation skill. First call RUBE_SEARCH_TOOLS for the current TomTom schema. I need to calculate or retrieve TomTom data for these addresses: [list]. Prefer a structured JSON result with the tool used, required input fields, any missing fields, and the final response. Check the TomTom connection before execution.”
Good inputs usually include:
- the exact TomTom task, not just “mapping”
- addresses, coordinates, place names, or route details
- desired output format, such as JSON, table, or summary
- whether the agent should execute the action or only draft the plan
- error-handling expectations if authentication or required fields are missing
Recommended tomtom-automation usage workflow
Start every workflow with tool discovery:
RUBE_SEARCH_TOOLS with a use case such as "TomTom route planning" or "TomTom location lookup".
Then check the connection:
RUBE_MANAGE_CONNECTIONS with toolkits: ["tomtom"].
After that, ask the agent to map your goal to the discovered tool schema. The best pattern is:
- Discover available TomTom tools.
- Identify the closest tool and required fields.
- Ask clarifying questions for missing fields.
- Execute only after the connection is active.
- Return the raw result plus a human-readable explanation.
This sequence is especially important because the skill’s own source emphasizes that schemas should be discovered at runtime rather than assumed.
Repository files to inspect first
The repository path is:
composio-skills/tomtom-automation
The key file is:
SKILL.md
There are no visible helper scripts, reference folders, rules, assets, or README files in the provided tree, so review SKILL.md carefully. It contains the operational contract: prerequisites, setup, tool discovery, and core workflow pattern. For broader toolkit behavior, use the linked Composio TomTom toolkit documentation at composio.dev/toolkits/tomtom.
tomtom-automation skill FAQ
Is tomtom-automation beginner-friendly?
It is beginner-friendly only if your AI client already supports MCP tools. The skill explains the required sequence clearly, but it assumes you can add an MCP server and respond to connection/authentication prompts. If you have never used MCP or Composio before, expect the first setup to take longer than the actual TomTom task.
How is this better than an ordinary prompt?
An ordinary prompt may invent TomTom tool names or use outdated fields. The tomtom-automation skill specifically tells the agent to call RUBE_SEARCH_TOOLS first, retrieve the current schema, and use RUBE_MANAGE_CONNECTIONS to confirm access. That makes it more reliable for live workflow automation than a static “call the TomTom API” instruction.
When should I not use this skill?
Do not use it when you need a pure TomTom REST API implementation, a production service with audited code paths, or a script that runs without an AI agent. Also avoid it if you cannot connect Rube MCP or cannot authorize the TomTom toolkit. In those cases, use TomTom’s official API documentation or a direct SDK approach instead.
Does it fit Workflow Automation use cases?
Yes, tomtom-automation for Workflow Automation is a reasonable fit when TomTom is one step in a larger AI-assisted workflow, such as enriching records, validating location data, or routing a task through available map tools. Its fit is weaker for high-volume batch jobs unless your MCP environment and Composio connection are designed for that workload.
How to Improve tomtom-automation skill
Make tomtom-automation prompts schema-aware
The biggest improvement is to require the agent to show the discovered schema before execution. Add instructions such as:
“After RUBE_SEARCH_TOOLS, list the chosen tool slug, required fields, optional fields, and any assumptions. Do not execute until I confirm missing values.”
This reduces hidden failures and makes the agent’s choice auditable.
Provide complete task context up front
For location and mapping workflows, small omissions can change results. Include country or region, units, travel mode if relevant, time constraints, and whether approximate matches are acceptable. If you provide coordinates, specify latitude/longitude order. If you provide addresses, include enough postal detail to avoid ambiguous matches.
Better input quality improves both tool selection and final output formatting.
Watch for common failure modes
Common issues include inactive TomTom connection, skipped tool discovery, stale assumed field names, ambiguous place names, and asking the agent to execute before required fields are known. If the first run fails, do not simply retry the same prompt. Ask the agent to restate the active connection status, the tool schema it used, the exact payload, and the returned error.
Iterate from plan to execution
For higher-stakes workflows, use a two-pass process. First ask tomtom-automation to create an execution plan based on discovered tools. Review the selected tool and required inputs. Then approve execution with corrected data. This keeps the skill practical for real automation while preserving control over authentication, payloads, and output format.