ip2location-automation
by ComposioHQip2location-automation helps Claude run IP2Location workflows through Composio Rube MCP with schema-first tool discovery, connection checks, and result handling guidance.
This skill scores 66/100, which makes it acceptable but limited for directory listing. Directory users can understand that it automates IP2Location operations through Composio/Rube MCP and gives agents a basic discovery-first execution pattern, but they should expect to rely on live tool search and external toolkit docs for the actual operation schemas and concrete use cases.
- Valid skill frontmatter with a clear MCP requirement: Rube MCP and an active `ip2location` connection.
- Explicitly instructs agents to call `RUBE_SEARCH_TOOLS` first, which helps avoid stale schemas and improves triggerability through tool discovery.
- Includes prerequisite and setup steps for checking Rube availability, managing the Ip2location connection, and confirming ACTIVE status before execution.
- No support files, scripts, README, or install command are included; setup is described only in SKILL.md.
- Workflow guidance is mostly generic Rube MCP discovery/connection flow and does not show concrete IP2Location task examples or expected outputs.
Overview of ip2location-automation skill
What ip2location-automation is for
ip2location-automation is a Claude skill for running IP2Location-related operations through Composio’s Rube MCP toolkit. It is designed for users who want an agent to discover the currently available IP2Location tools, check the active connection, and execute geolocation-style workflow steps without guessing stale tool names or schemas.
Best-fit users and workflows
This skill is most useful for workflow automation teams, support engineers, security analysts, growth operations teams, and data enrichment users who already rely on IP-based lookup or enrichment tasks. The strongest fit is ip2location-automation for Workflow Automation, where the agent needs to turn a request such as “look up these IPs and format the results for triage” into tool discovery, connection validation, execution, and result handling.
Key differentiator: schema-first execution
The important design choice in the ip2location-automation skill is that it instructs the agent to call RUBE_SEARCH_TOOLS before executing any IP2Location action. That matters because Composio tool names, fields, and execution plans can change. Instead of hard-coding assumptions, the skill pushes the model to retrieve current tool schemas and known pitfalls first.
Adoption considerations
This is a compact skill with one main source file, SKILL.md, and no extra scripts, rules, or reference assets. That makes it easy to inspect, but it also means success depends heavily on your MCP setup, active IP2Location connection, and the quality of the task prompt you provide. It is not a standalone IP geolocation library; it is an agent workflow wrapper around Rube MCP.
How to Use ip2location-automation skill
ip2location-automation install and setup path
Install the skill in your Claude skills environment, for example:
npx skills add ComposioHQ/awesome-claude-skills --skill ip2location-automation
Then configure Rube MCP in your client by adding the MCP server endpoint:
https://rube.app/mcp
Before expecting useful output, confirm three things: RUBE_SEARCH_TOOLS is available, RUBE_MANAGE_CONNECTIONS can manage the ip2location toolkit, and the IP2Location connection status is ACTIVE. If the connection is not active, follow the auth link returned by Rube before running the workflow.
Inputs the skill needs from you
For reliable ip2location-automation usage, provide the agent with the concrete task, the IP addresses or source of IPs, desired output fields, output format, and any handling rules. A weak prompt is:
“Check these IPs.”
A stronger prompt is:
“Use ip2location-automation to look up these IP addresses through Rube MCP: 203.0.113.10, 198.51.100.7. First discover current IP2Location tools and schemas. Return country, region, city, ISP or organization if available, confidence or missing-field notes, and a CSV-ready table. Do not invent unavailable fields.”
This gives the agent enough context to search the right tool use case, map returned fields into your preferred format, and avoid fabricating unavailable values.
Practical workflow to invoke the skill well
A good ip2location-automation guide follows this sequence:
- Ask the agent to use the skill and search tools first with
RUBE_SEARCH_TOOLS. - Have it check the
ip2locationconnection withRUBE_MANAGE_CONNECTIONS. - Review the discovered tool schema before execution if the task is sensitive or high-volume.
- Run the selected IP2Location operation.
- Ask the agent to normalize results into your workflow format, such as CSV, JSON, Markdown table, ticket comments, or enrichment notes.
- Validate ambiguous or missing fields instead of treating every blank as a successful lookup.
This sequence is especially important when you are embedding lookups into repeatable business automation rather than doing a one-off query.
Repository files to inspect first
Start with composio-skills/ip2location-automation/SKILL.md. It contains the skill’s prerequisites, setup sequence, tool discovery rule, and core workflow pattern. There are no bundled helper scripts or supporting reference folders in the current skill package, so SKILL.md is the source of truth. Also check the linked toolkit documentation at composio.dev/toolkits/ip2location if you need to understand the broader Composio IP2Location tool surface.
ip2location-automation skill FAQ
Is ip2location-automation enough by itself?
No. The skill does not perform IP lookups locally. It depends on Rube MCP and an active Composio IP2Location toolkit connection. Think of ip2location-automation as the agent instruction layer that helps Claude discover and call the correct external tools safely.
How is this better than an ordinary prompt?
A generic prompt may cause the agent to guess tool names, assume old schemas, or skip connection checks. This skill explicitly prioritizes RUBE_SEARCH_TOOLS and current schema discovery before execution. That makes it more suitable for MCP-based automation where the available actions and fields may differ from what the model remembers.
Is it beginner-friendly?
It is beginner-friendly if you are comfortable adding an MCP server and completing an authorization flow. The skill’s usage pattern is simple, but beginners may be blocked if they expect it to work without configuring Rube MCP or activating the IP2Location connection.
When should I not use this skill?
Do not use it when you need an offline IP geolocation database, a custom IP enrichment pipeline, or guaranteed fields independent of an external toolkit. It is also a poor fit for prompts that require legal, compliance, or threat-intelligence conclusions from IP data alone. Use it for structured lookup automation, not for unsupported attribution claims.
How to Improve ip2location-automation skill
Improve prompts with exact task shape
To get better results from ip2location-automation, describe the operational goal instead of only naming the tool. Include volume, input source, required fields, format, and downstream use. For example:
“Enrich the IPs from this incident report for SOC triage. Use current IP2Location schemas, return a Markdown table, flag private/reserved IPs separately, and include only fields returned by the tool.”
This helps the agent choose the right discovered tool and produce output that fits the workflow.
Prevent common failure modes
The most common failures are skipped tool discovery, inactive connection state, invented output fields, and unclear formatting requirements. Counter them directly in the prompt: “Search tools first,” “confirm the ip2location connection is active,” “do not infer missing fields,” and “return JSON matching this schema.” These instructions reduce guesswork and make the result easier to validate.
Iterate after the first output
After the first run, improve quality by asking the agent to reconcile missing fields, deduplicate repeated IPs, separate invalid or private addresses, or convert the results into your final workflow format. If the output will feed another system, provide that system’s exact schema or column list before rerunning.
Extend the skill for team workflows
Teams can improve the ip2location-automation skill by adding local guidance around approved output formats, rate-limit expectations, privacy rules, and examples of common lookup tasks. Since the upstream skill is intentionally minimal, organization-specific rules can add real value without changing the core Rube MCP discovery-first pattern.