graphhopper-automation

Overview of graphhopper-automation skill

What graphhopper-automation does

graphhopper-automation is a Claude skill for running GraphHopper-related workflow automation through Composio’s Rube MCP server. Instead of hard-coding GraphHopper tool calls, the skill tells the agent to discover current Rube tool schemas first, verify the GraphHopper connection, then execute the appropriate operation with the returned tool names and input fields.

Use this skill when you want an AI agent to help with routing, distance or travel-time calculations, route planning, logistics experiments, or other GraphHopper-backed tasks without manually browsing Composio’s toolkit schema each time.

Best-fit users and workflows

The graphhopper-automation skill is most useful for developers, operations teams, logistics analysts, and AI workflow builders who already use Claude or another MCP-capable client and want GraphHopper actions inside an agent workflow. It fits tasks such as:

• preparing routing or travel-time requests from structured addresses or coordinates
• checking available GraphHopper actions exposed by Composio
• building repeatable workflow prompts around GraphHopper data
• combining GraphHopper results with spreadsheet, CRM, dispatch, or reporting workflows

It is less useful if you only need a one-off manual route lookup in a map UI.

Key differentiator: schema discovery first

The main value of graphhopper-automation is not a static list of GraphHopper commands. Its core pattern is: search Rube tools first, inspect the current schema, check the GraphHopper connection, and only then call the tool. That matters because MCP tool names, fields, and required parameters can change. The skill reduces failed calls caused by stale assumptions.

Requirements before adoption

You need an MCP client that can connect to Rube, access to RUBE_SEARCH_TOOLS, and an active GraphHopper connection managed through RUBE_MANAGE_CONNECTIONS. The upstream skill has a single SKILL.md and no helper scripts, so expect prompt-driven automation rather than a packaged CLI or SDK wrapper.

How to Use graphhopper-automation skill

graphhopper-automation install context

Install the skill into a compatible skills-enabled environment, for example:

npx skills add ComposioHQ/awesome-claude-skills --skill graphhopper-automation

Then add Rube MCP as a server in your client configuration using:

https://rube.app/mcp

After the MCP server is available, verify that RUBE_SEARCH_TOOLS responds. Next, use RUBE_MANAGE_CONNECTIONS with toolkit graphhopper and complete the returned authorization flow if the connection is not ACTIVE.

Start with the only source file

Read composio-skills/graphhopper-automation/SKILL.md first. There are no README.md, scripts/, references/, or resources/ folders in this skill, so the operational guidance is concentrated in that file. Pay particular attention to:

• prerequisites for Rube MCP and GraphHopper connection status
• the instruction to call RUBE_SEARCH_TOOLS before execution
• the core workflow pattern for discovery, connection check, and tool call
• examples of passing a specific use case into tool discovery

This is important because the skill depends on live schema discovery, not bundled documentation.

Turn a rough goal into a usable prompt

Weak prompt:

Find the best route for my deliveries.

Stronger prompt for graphhopper-automation usage:

Use graphhopper-automation for Workflow Automation. First call RUBE_SEARCH_TOOLS for a GraphHopper delivery routing or route optimization task and inspect the returned schema. Confirm the GraphHopper connection is active. Then plan a workflow for 8 stops starting from 40.7128,-74.0060, ending at 40.7306,-73.9352, using car travel mode if supported. If optimization is not available in the discovered tools, explain the closest supported alternative before calling anything.

This improves results because it gives the agent coordinates, task type, constraints, fallback behavior, and permission to validate the current tool schema before execution.

Practical workflow tips

For better output, provide coordinates when possible; addresses can introduce geocoding ambiguity if the available tool expects latitude and longitude. State travel mode, start and end points, optimization preference, time windows, avoid areas, or vehicle assumptions only if they matter. Ask the agent to show the discovered tool slug and required fields before making the final call when accuracy matters.

If the first tool search is too broad, rerun RUBE_SEARCH_TOOLS with a narrower use case such as “GraphHopper matrix travel times for 20 coordinates” or “GraphHopper isochrone from one coordinate.”

graphhopper-automation skill FAQ

Is graphhopper-automation a GraphHopper API client?

Not directly. graphhopper-automation is an agent skill that guides Claude to use Composio’s GraphHopper toolkit through Rube MCP. It does not replace GraphHopper’s API documentation or SDKs. It is best understood as an MCP workflow layer for discovering and calling available GraphHopper tools.

Why not just ask Claude for a GraphHopper request?

A generic prompt may invent field names or assume outdated API shapes. This skill’s main guardrail is to call RUBE_SEARCH_TOOLS first so the agent sees current tool slugs, schemas, and pitfalls before execution. That makes it more reliable for live workflow automation than a static “write me a GraphHopper call” prompt.

Is the graphhopper-automation skill beginner friendly?

It is beginner friendly if your MCP client is already configured and you can follow an auth link for the GraphHopper connection. It is not ideal for users who have never configured MCP servers, do not know whether their client supports tools, or need a visual route-planning application instead of an agent workflow.

When should I not use it?

Do not use this skill when you need offline routing, a guaranteed stable local API wrapper, or a fully scripted pipeline with tests. The upstream skill contains guidance, not executable helper code. Also avoid it when your task requires GraphHopper functionality that is not exposed by the current Composio toolkit; the agent should confirm availability through tool discovery before promising execution.

How to Improve graphhopper-automation skill

Improve graphhopper-automation inputs

The biggest quality improvement is better task framing. Include the job type, locations, units, mode of travel, required output format, and acceptable fallback. For example:

Search current GraphHopper tools for matrix travel times. Use these 12 latitude/longitude pairs, return a CSV-style table of pairwise durations in minutes, and do not guess missing coordinates.

This is stronger than “calculate distances” because it tells the agent what GraphHopper capability to look for, what data shape to use, and how to format the result.

Watch for common failure modes

Common issues include inactive GraphHopper connection, skipped tool discovery, ambiguous addresses, missing required schema fields, and assuming a tool supports optimization when only routing or matrix calculation is available. A good graphhopper-automation guide prompt should explicitly say: discover tools first, check connection status, inspect required fields, then execute.

Iterate after the first result

After the first output, ask the agent to compare the result against the discovered schema and your original constraints. Useful follow-ups include:

• “Show which input fields were required versus optional.”
• “Rerun the tool search for route optimization specifically.”
• “Convert the result into a dispatch-ready table.”
• “Explain any GraphHopper limitation or missing parameter that affected the result.”

This turns a one-shot call into a dependable workflow.

Extend the skill for team use

If your team uses graphhopper-automation regularly, add internal examples for your common tasks: route planning, matrix calculations, depot-to-stop analysis, or territory checks. Store preferred coordinate formats, output templates, and connection troubleshooting notes near the skill. The upstream repository is intentionally minimal, so local examples can materially improve consistency without changing the core Rube MCP discovery pattern.