google_maps-automation
by ComposioHQgoogle_maps-automation helps Claude run Google Maps workflows through Composio Rube MCP, including geocoding, place search, directions, and distance calculations with tool discovery first.
Score: 70/100. This is an acceptable but limited listing candidate: directory users get enough evidence to understand that the skill automates common Google Maps workflows through Rube MCP and how an agent should begin tool discovery and authentication. However, the repository is only a single SKILL.md with no install command or support assets, and much of the execution specificity depends on runtime Rube discovery rather than documented examples.
- Clear scope and trigger: geocoding, directions, place search, and distance calculations via Composio's Google Maps toolkit over Rube MCP.
- Operational prerequisites are explicit, including Rube MCP availability, Google Maps connection setup through RUBE_MANAGE_CONNECTIONS, and confirming ACTIVE status before workflows.
- The skill instructs agents to call RUBE_SEARCH_TOOLS first, which should reduce schema drift and help agents use current tool names and inputs rather than guessing.
- No support files, scripts, README, or install command are provided beyond the SKILL.md, so adoption depends on already knowing how to install this skill from the parent repo.
- Execution details are delegated to live Rube tool discovery; users will not see fixed Google Maps tool schemas or fully reproducible examples in the repository itself.
Overview of google_maps-automation skill
What google_maps-automation does
google_maps-automation is a Claude skill for running Google Maps workflows through Composio’s Rube MCP server. It is designed for geocoding addresses, reverse geocoding coordinates, finding places, calculating routes, and estimating distance or travel time without manually switching between Maps, API docs, and ad hoc prompts.
Best-fit users and workflows
The google_maps-automation skill fits operators, growth teams, researchers, local SEO teams, logistics planners, and automation builders who need structured Google Maps results inside an AI workflow. It is most useful when your task depends on live tool schemas, authenticated Google Maps access, and repeatable outputs such as “find nearby clinics,” “convert these store addresses to coordinates,” or “compare driving distance between candidate locations.”
Key differentiator: Rube MCP tool discovery
The important behavior is not just “ask about maps.” The skill instructs the agent to use RUBE_SEARCH_TOOLS first, so it can discover the current Google Maps tool slugs, required fields, execution plan, and known pitfalls before calling any action. That matters because MCP tool schemas can change, and stale assumptions are a common cause of failed automations.
Adoption requirements and limits
This is a lightweight skill with a single SKILL.md; it does not include helper scripts, sample datasets, or local test fixtures. You need Rube MCP connected and an active google_maps connection through RUBE_MANAGE_CONNECTIONS. If you only need a one-off explanation of a route concept, an ordinary prompt may be enough. Use this skill when you need the agent to actually invoke Google Maps tools.
How to Use google_maps-automation skill
google_maps-automation install and setup path
Install the skill in your Claude skills environment from the repository path:
npx skills add ComposioHQ/awesome-claude-skills --skill google_maps-automation
Then configure Rube MCP by adding https://rube.app/mcp as an MCP server in your client. Before running a Maps task, confirm that RUBE_SEARCH_TOOLS is available. Next, use RUBE_MANAGE_CONNECTIONS with toolkit google_maps; if the connection is not ACTIVE, complete the returned authorization flow and re-check status.
Inputs the skill needs from you
For reliable google_maps-automation usage, provide the task type, entities, location context, constraints, and output format. Weak prompt: “Find coffee shops near me.” Stronger prompt: “Use google_maps-automation to find 10 coffee shops within 2 km of 1600 Amphitheatre Parkway, Mountain View, CA, prioritize places open now with ratings if available, and return name, address, place ID, rating, distance, and a short note on missing fields.”
For directions, include origin, destination, travel mode, time sensitivity, and whether alternatives are needed. For geocoding, include country or region hints when addresses may be ambiguous.
Recommended workflow for first run
Start by reading composio-skills/google_maps-automation/SKILL.md; it contains the live operating rules. In use, ask the agent to follow this sequence: discover tools with RUBE_SEARCH_TOOLS, inspect the returned schema, choose the matching Google Maps action, execute with explicit parameters, and summarize results with assumptions and missing data. Do not skip discovery just because a previous run worked.
A practical first prompt is: “Use the google_maps-automation skill. First search the available Rube Google Maps tools and schemas, then geocode the following addresses, flag ambiguous matches, and return a CSV-style table with original address, formatted address, latitude, longitude, confidence signals, and errors.”
Tips that improve output quality
Batch similar operations when the available schema supports it, but ask the agent to preserve row-level error reporting. For place search, specify radius, category, language or region preference, and deduplication rules. For distance calculations, define whether you want straight-line distance or route-based travel distance. If you are using results in Workflow Automation, ask for machine-readable output such as JSON, CSV table, or a normalized field list rather than a narrative answer.
google_maps-automation skill FAQ
Is google_maps-automation better than a normal Maps prompt?
Yes, when you need tool execution. A normal prompt can reason about maps in general, but it cannot reliably access your authenticated Google Maps connection or current Rube tool schemas. The google_maps-automation skill gives the agent a repeatable procedure: authenticate, discover tools, run the relevant operation, and report structured results.
What can I automate with this skill?
Common tasks include address-to-coordinate conversion, coordinate-to-address lookup, place discovery, nearby business research, route planning, travel distance comparison, and location enrichment. The skill is especially useful when these tasks feed another workflow, such as lead enrichment, territory planning, local search audits, delivery estimation, or data cleanup.
Is this beginner-friendly?
It is beginner-friendly if your Claude client already supports MCP servers. The main setup hurdle is not the skill itself; it is connecting Rube MCP and authorizing the google_maps toolkit. Once RUBE_SEARCH_TOOLS and RUBE_MANAGE_CONNECTIONS are working, the skill’s usage pattern is straightforward.
When should I not use this skill?
Do not use it for scraping Google Maps pages, bypassing access controls, or replacing compliance review for regulated location data. It is also not ideal for bulk production pipelines without rate-limit planning, validation, and monitoring outside the skill. For static distance formulas or educational examples, a normal prompt may be simpler.
How to Improve google_maps-automation skill
Improve google_maps-automation inputs
The fastest way to improve google_maps-automation results is to remove ambiguity before the tool call. Include full addresses, city, region, country, desired radius, travel mode, and preferred output schema. If names are ambiguous, provide disambiguators such as business category, known neighborhood, website, or phone number. This reduces false matches and prevents the agent from guessing.
Handle common failure modes
Common blockers include inactive Google Maps authorization, skipped tool discovery, incomplete required fields, ambiguous place names, and mismatched expectations about distance type. If a run fails, ask the agent to show which tool schema it discovered, which required fields were missing, and whether the connection status was active. That diagnostic trail is more useful than simply retrying the same prompt.
Iterate after the first output
Treat the first result as a validation pass. Ask for ambiguous rows, missing fields, duplicate candidates, or low-confidence matches to be separated from clean results. For place search, refine category, radius, open-hours requirement, or ranking criteria. For directions, compare alternatives only after confirming the origin, destination, and travel mode were interpreted correctly.
Extend it for Workflow Automation
For google_maps-automation for Workflow Automation, pair the skill with explicit downstream requirements: “return JSON keyed by store_id,” “include an error field per record,” or “make results safe to import into a CRM.” If you repeatedly run the same process, save a house prompt that requires RUBE_SEARCH_TOOLS first, validates connection status, normalizes output fields, and reports assumptions separately from tool-returned facts.