Pelias Geocoder MCP. Convert addresses, reverse coordinates, and map POIs.
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Pelias Geocoder handles all your map data needs in one place. Use it to turn human-readable addresses into precise GPS coordinates, or run the reverse process to find out where a set of Lat/Long points actually is.
It also lets you autocomplete POIs and search specific areas using bounding boxes.
What your AI agents can do
Lookup place id
Extracts rich location details from a given place ID, giving you more than just the basic address.
Reverse distance limit
Checks how far out from a point Pelias should search when performing a reverse lookup to find alternatives.
Reverse geocode
Takes Lat/Long coordinates and returns the corresponding human-readable address data for that spot.
Input a full street address, and it returns precise latitude/longitude boundaries for that location.
Provide raw GPS coordinates (Lat/Long), and the tool figures out the corresponding real-world place name or address.
Autocomplete suggestions as you type, pulling live POI data like businesses or landmarks.
Define a specific box on the map using bounding coordinates and pull all features that fall inside it.
Limit search results to only match addresses or POIs within a specific ISO 3166 country boundary.
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Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Pelias Geocoder MCP Server: 10 Tools for Geospatial Data Retrieval
Use these ten tools to run complex map queries. They let you convert addresses into coordinates, find locations from raw Lat/Long data, and filter POIs by specific boundaries or countries.
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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Pelias Geocoder on Vinkius019d75f0lookup place id
Extracts rich location details from a given place ID, giving you more than just the basic address.
019d75f0reverse distance limit
Checks how far out from a point Pelias should search when performing a reverse lookup to find alternatives.
019d75f0reverse geocode
Takes Lat/Long coordinates and returns the corresponding human-readable address data for that spot.
019d75f0search autocomplete
Provides live suggestions as you type, pulling explicit POI names like businesses or landmarks.
019d75f0search bounding box
Filters and returns all map features that fall strictly within a defined rectangular coordinate box.
019d75f0search country filter
Limits searches to specific geographic areas by matching ISO 3166 country codes, ignoring foreign domains.
019d75f0search focus bias
Adjusts search results to prioritize locations that are physically closest to a given GPS trace point.
019d75f0search geocode
Identifies and pulls structured location data from the headless Pelias Maps service based on input criteria.
019d75f0search layer filter
Allows you to query and export active GIS datasets by specifying which map layer rules you want to see.
019d75f0structured geocoding
Isolates location terms using specific address parts (e.g., region=Y) to find precise, native location arrays.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Pelias Geocoder. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Finding location boundaries shouldn't require multiple lookups.
Right now, if you have an address like '45 Oak St', you usually run it through one API to get the basic Lat/Long. Then, if you need to know what POIs are nearby or verify that location against a map layer, you have to call a second API with those coordinates. It's slow, and you lose context between calls.
With Pelias Geocoder, you run it once. The agent handles the initial geocoding, but then uses that structured data to immediately query nearby POIs or check for features within a small bounding box using `search_bounding_box`. You get one clean flow, and all your necessary location context hits the endpoint.
Pelias Geocoder MCP Server: Get precise coordinates in seconds.
Manually determining if a piece of data is valid—is it really an address? Is that Lat/Long pair even real?—takes time and introduces error. You have to cross-reference multiple services just to confirm the boundaries are correct.
This server lets you run those checks in one go. The agent executes `structured_geocoding` or `reverse_geocode`, providing a definitive, validated output that confirms data integrity every time. It's immediate and reliable.
What you can do with this MCP connector
Need to figure out where something is on a map? This server handles all your geospatial data needs in one spot. You don't just run simple lookups; you execute structured checks against massive map datasets.
Want to turn a street address into precise GPS coordinates? Use search_geocode to identify and pull structured location data from the Pelias Maps service based on whatever criteria you throw at it. If you start with raw Lat/Long points, run reverse_geocode; that tells you what real-world place name or address is sitting at those exact coordinates.
When you're hunting for Points of Interest (POIs), don't just guess. You can get live suggestions as you type using search_autocomplete, which pulls explicit POI names like local businesses or landmarks. If the area is huge, use search_bounding_box to define a specific rectangular coordinate box and pull every single map feature that falls inside it.
To make those searches smarter, you can limit results by geography. Use search_country_filter to restrict everything to only match addresses or POIs within a specific ISO 3166 country boundary. You can also use search_focus_bias if you need the search results prioritized toward locations physically closest to a given starting GPS point.
For serious data work, precision is everything. If you want to isolate location terms using only specific address parts—say, just finding things in 'Region=Y'—you hit it with structured_geocoding. You can also use the detailed information from a known place ID; calling lookup_place_id extracts rich location details that go way beyond just the basic street address.
If you're doing complex map analysis, you might need to know how far out Pelias should look when running a reverse search. That’s where reverse_distance_limit comes in; it checks how far out from your point Pelias should search for alternatives. To narrow down what kind of data you pull—like active GIS datasets—you use search_layer_filter, allowing you to specify exactly which map layer rules you wanna see.
Finally, if a simple coordinate check isn't enough, and you need to know the precise latitude/longitude boundaries for an entire address string, that’s what general geocoding handles. You can also run search_geocode using various input criteria to nail down exactly what you need from the headless Pelias Maps service.
019d75f0-c815-7363-9fb7-0fd926c9ce17 
How Pelias Geocoder MCP Works
- 1 First, you link your own hosted Pelias parameters. You'll need to enter the base URL and your valid API Token.
- 2 Next, you tell your agent what kind of check you need—is it a reverse lookup? Are you searching by bounding box? The tool uses that context to route the request.
- 3 Finally, the server returns structured JSON data with precise coordinates or full addresses, ready for your AI client to read and process.
The bottom line is: You point it at a location problem, and it gives you clean, verifiable geospatial data back.
Who Is Pelias Geocoder MCP For?
Anyone dealing with physical location data. Think GIS developers who need to validate map inputs or Data Ops teams that handle large batches of addresses. If your job involves turning 'Where is this?' into a JSON object, you need this.
Runs batch checks on lists of coordinates to verify if they map to valid human addresses or pull structured data for cleansing.
Needs to validate complex spatial arrays, ensuring that custom bounding boxes (rect) correctly isolate specific geographic features before rendering a map layer.
Queries POI data or runs structured searches to understand the distribution of services or points of interest within defined city boundaries.
What Changes When You Connect
- Stop guessing where a location is. Use
reverse_geocodeto instantly convert any Lat/Long pair into a specific street address. You get the full context without needing a second API call. - Need to find everything inside a city block? The
search_bounding_boxtool lets you define a coordinate rectangle and pull every single feature that falls within it, making map analysis precise. - Searching globally is slow. Use
search_country_filterfirst to narrow your scope down by country code (ISO 3166). This drastically reduces the dataset size before you even start querying POIs. - Autocomplete needs to be accurate. Call
search_autocompleteto get immediate, suggested POI names as a user types, improving data quality right at the point of entry. - Don't treat all addresses the same. Use
structured_geocodingwhen you know parts of an address—like a region or state—to isolate terms and pull only hyper-specific location arrays.
Real-World Use Cases
Validating batch data for logistics
A logistics team has 500 coordinates from GPS trackers. Instead of manually checking each one, they run the list through reverse_geocode. The agent processes the entire batch and returns a structured JSON report showing the corresponding human address for every single point.
Finding POIs near a custom boundary
A city planner needs to know how many schools are within a new development zone. They use search_bounding_box to define the exact property lines and then query for 'school' layers, getting a count and list of all relevant POIs.
Improving user input forms
Building an address submission form? As the user types 'St. Louis', the agent calls search_autocomplete. This immediately suggests the full, correct location names, preventing typos and ensuring data integrity before submission.
Debugging map layers in development
A dev team suspects a specific area is missing points of interest. They use search_layer_filter to enumerate all attached structured rules for that region, confirming exactly which GIS datasets should be visible and if they are even active.
The Tradeoffs
Searching without filtering
Trying to find a local shop using only the general search_geocode tool. The results dump thousands of global, irrelevant records, and you can't tell what's relevant.
→
Always narrow your scope first. Use search_country_filter if you know the country, or use search_bounding_box if you have coordinates. This keeps the search tight.
Ignoring POI suggestions
Writing a script that takes a user-entered string like 'The Library' and treats it as final data, even if it's ambiguous or misspelled.
→
Before committing the name, run search_autocomplete. This validates the input against real local records. If the suggestion is accurate, you can use its precise ID.
Relying only on addresses
Assuming that because an address exists (e.g., 123 Main St.), there must be a specific POI like a bank or restaurant right at that spot.
→
Use structured_geocoding to get the basic location, but then follow up with a targeted search using search_layer_filter for the exact type of feature you are looking for (e.g., 'bank').
When It Fits, When It Doesn't
Use this server if your job requires converting physical space into structured data points. You need to go from ambiguous human language ('Near the river') or raw coordinates (Lat/Long) to a verifiable, actionable JSON object.
Don't use this if you just need simple text processing or general web scraping—there are better tools for that. Also, don't rely on it for calculating travel time; it only gives location boundaries. If your query is about 'What happens between Point A and Point B?' you need a specialized routing service.
However, if you need to know the exact geographic limits of an address or find out what services are available within a specific area defined by coordinates, this suite is exactly what you need. Use structured_geocoding for controlled inputs, and use search_bounding_box when your query relies on geometry.
Common Questions About Pelias Geocoder MCP
How do I use search_autocomplete to find the right POI? +
Run search_autocomplete with a partial name or starting letters. The tool returns a list of suggested POIs, including their full names and IDs. Pick the one that matches your target.
What is the difference between search_geocode and structured_geocoding? +
search_geocode handles general location identification. structured_geocoding, however, lets you isolate specific parts of an address (like specifying a region=Y) to get much more precise, controlled results.
Can I find out the full address from just Lat/Long coordinates using reverse_geocode? +
Yes. You pass your target latitude and longitude directly into reverse_geocode. The tool processes them and returns the corresponding structured, human-readable street address.
I need to check a large area; should I use search_bounding_box or something else? +
Use search_bounding_box. You define the top-left and bottom-right corners of your desired map rectangle. The tool then pulls only the features that fall inside those specific coordinates, ignoring everything outside.
What is the best way to search for points within a specific radius using `reverse_distance_limit`? +
Use the circle.radius parameter to define your exact search area. This function guarantees results fall strictly within the circular boundary you specify, preventing searches from expanding too far.
How do I ensure my query only uses a specific data source layer with `search_layer_filter`? +
Pass the desired GIS dataset name to search_layer_filter. This limits your search results to one defined set of rules or layers, ignoring potential conflicts from other available data sources.
If I already have a unique place ID, how can I use `lookup_place_id` to get its full schema? +
Simply pass the specific Place ID string to lookup_place_id. This immediately extracts all rich properties associated with that exact ID, bypassing any need for coordinate searching or location matching.
How do I force the results to prioritize locations physically nearest to a point using `search_focus_bias`? +
Use the center coordinates in conjunction with search_focus_bias. This tells Pelias to bias its result set toward points closest to your specified GPS trace, improving relevance for local searches.
Can I use Pelias bounds configuring explicit extraction of local custom data stores? +
Yes. This configuration inherently parses dynamic host architecture. You explicitly bind the native Base URL to point strictly toward your configured self-hosted arrays or Pelias-compatible public limit providers natively globally.
How explicitly strict are the parameter bounds when I invoke bounded reversed logistics natively? +
You map explicit limits using standard decimal notation gracefully parsing constraints natively: lat=40.73 and lon=-73.93. The limits parse efficiently checking the closest explicit street JSON outputs securely returning structured bounded nodes.
Is the structured Autocomplete log bound explicitly evaluating live typing constraints? +
Absolutely structurally globally bound. Command the search_autocomplete natively with partial strings (e.g., '100 Main S'), and the AI extracts arrays modeling how your specific UI limit bounds react dynamically effortlessly.
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