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Precisely MCP. Turn fuzzy addresses into actionable coordinates.

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Precisely MCP on Cursor AI Code Editor MCP Client Precisely MCP on Claude Desktop App MCP Integration Precisely MCP on OpenAI Agents SDK MCP Compatible Precisely MCP on Visual Studio Code MCP Extension Client Precisely MCP on GitHub Copilot AI Agent MCP Integration Precisely MCP on Google Gemini AI MCP Integration Precisely MCP on Lovable AI Development MCP Client Precisely MCP on Mistral AI Agents MCP Compatible Precisely MCP on Amazon AWS Bedrock MCP Support

Just plug in your AI agents and start using Vinkius.

Precisely gives your AI client accurate location intelligence by connecting it to a global geospatial network. Use it to geocode addresses, verify property boundaries down to the rooftop level, calculate local sales taxes, and score environmental or crime risks for any coordinate set.

What your AI agents can do

Autocomplete address

Provides up to 10 ranked address suggestions as a user types, drawing from a global database of structured addresses.

Enrich crime risk

Calculates the crime risk index for coordinates, returning normalized scores across categories like burglary and theft relative to national averages.

Enrich demographics

Pulls rich socioeconomic profiles for a location, including household income brackets, population density, and education levels.

+ 7 more capabilities included
Resolve Coordinates from Addresses

The agent takes a full or partial address string and returns precise global latitude/longitude coordinates.

Validate and Standardize Addresses

It checks an input address against official postal databases, correcting formatting and confirming if the location is deliverable.

Calculate Local Tax Rates

The agent determines the combined sales tax rate for a specific coordinate, breaking down the contribution from state, county, and city levels.

Analyze Socioeconomic Data

It pulls detailed demographic reports for an area, including household income brackets, population density, and education levels.

Assess Environmental/Crime Risk

The agent reads specific risk scores—like FEMA flood zones or normalized crime indices—for a given location.

Supported MCP Clients

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients
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AI Agent

Precisely MCP Server: 10 Tools for Location Intelligence

These tools give your AI the ability to read structured location data—from tax rates to demographics—using coordinates as their input.

autocomplete019d75f9

autocomplete address

Provides up to 10 ranked address suggestions as a user types, drawing from a global database of structured addresses.

enrich019d75f9

enrich crime risk

Calculates the crime risk index for coordinates, returning normalized scores across categories like burglary and theft relative to national averages.

enrich019d75f9

enrich demographics

Pulls rich socioeconomic profiles for a location, including household income brackets, population density, and education levels.

enrich019d75f9

enrich flood risk

Returns flood zone classification (A, AE, X, V) and risk index by evaluating coordinates against FEMA data.

geocode019d75f9

geocode address

Converts a full or partial address string into precise global latitude/longitude coordinates, identifying the match depth (e.g., rooftop vs. street).

get019d75f9

get local tax

Determines the combined sales tax rate and its individual components (state, county, city) for a given coordinate.

get019d75f9

get property info

Retrieves comprehensive property data for US addresses, including lot size, building square footage, assessed value, and year built.

get019d75f9

get timezone

Resolves the IANA timezone identifier and current UTC offset for any geographic coordinate, accounting for DST changes.

reverse019d75f9

reverse geocode

Converts coordinates back into a structured postal address, identifying the nearest rooftop-level street components.

verify019d75f9

verify address

Validates and standardizes any input postal address against authoritative datasets, confirming deliverability status.

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What you can do with this MCP connector

You gotta feed your agent the Precisely intelligence network so it knows what's actually at a location. This connection lets your client pull accurate geospatial data—everything from tax rates to flood risks—without needing a dozen different map APIs. It gives you real-world context for any coordinate set.

Resolving Coordinates and Addresses

When you have an address, even if it's partial, the agent doesn't guess; it resolves precise global coordinates using **geocode_address**. You feed it a full or partial street name, and it spits out latitude/longitude. It also tells you how accurate that match is—did it nail the rooftop level, or did it just hit the general street segment? If you're still typing an address, use **autocomplete_address**; this gives up to ten ranked suggestions from a massive global database of structured addresses right as your user types.

But if you start with coordinates instead of text, you can go backward. The **reverse_geocode** tool takes those lat/long points and converts them back into a complete, structured postal address, confirming the nearest rooftop-level street components. For full validation, use **verify_address**. This checks any input postal address against official databases, correcting formatting issues and telling you straight up if the location is actually deliverable.

Calculating Financial and Property Data

Need to know what people are paying in taxes? The agent determines the combined sales tax rate for a specific coordinate using **get_local_tax**. It doesn't just give you one number; it breaks down exactly where that money goes—the state contribution, the county split, and the city portion. For property-specific numbers on US addresses, **get_property_info** retrieves comprehensive records.

You get lot size measurements, building square footage counts, assessed values, and even the year the structure was built.

If you just need to know what time zone it is right now, use **get_timezone**. It resolves the IANA timezone identifier and gives you the current UTC offset for any coordinate, making sure it handles Daylight Saving Time shifts correctly.

Analyzing Risk and Demographics

The agent reads specific risk scores tied to location. If flood concerns come up, **enrich_flood_risk** evaluates coordinates against FEMA data, returning the official flood zone classification (like A, AE, X, or V) plus a calculated risk index. For crime analysis, **enrich_crime_risk** calculates the specific crime risk index for those coordinates.

It normalizes scores across categories like burglary and theft relative to national averages.

Want to know who lives there? **enrich_demographics** pulls detailed socioeconomic profiles. This includes household income brackets, population density counts, and education level data for the area. You're getting deep insights into the community surrounding those coordinates.

How Precisely MCP Works

  1. 1 First, the AI client sends an address string to geocode_address (or autocomplete_address) so it can get precise coordinates and validate the input.
  2. 2 Next, the agent decides what context is needed—maybe tax rates or flood data. It then passes those coordinates into a specific tool like get_local_tax or enrich_flood_risk.
  3. 3 Finally, the response comes back as structured JSON containing the required data points (e.g., 8.625% sales tax rate and its components).

The bottom line is: you give your agent an address, and it runs a pipeline of specialized tools to return multiple layers of verifiable context.

Who Is Precisely MCP For?

This is for the data engineer who can't rely on simple regex validation for addresses. It’s for the real estate analyst who needs flood scores and demographics in one prompt. And it’s for e-commerce operations teams that need to know tax zones before they start shipping products.

Data Engineer

Cleans, standardizes, and verifies raw address fragments from messy data sources into structured, actionable coordinates without writing cURL scripts.

Real Estate Analyst

Asks for property dimensions, flood scores (enrich_flood_risk), tax rates (get_local_tax), and socioeconomic stats in natural language to build reports on a specific area.

E-commerce Operations Manager

Validates the deliverability and checks the exact rooftop tax zone for US addresses during development, preventing checkout errors.

What Changes When You Connect

  • Stop guessing tax rates. Use get_local_tax to instantly know the exact combined state, county, and city sales tax rate for any delivery ZIP code.
  • Minimize shipping risk with verify_address. It standardizes messy input formats (USPS/Royal Mail) and confirms if an address is actually deliverable before you write a single line of validation logic.
  • Go beyond simple maps. Use enrich_demographics to pull population density, income brackets, and age distribution for a coordinate, allowing your agent to tailor marketing copy instantly.
  • Assess risk upfront. Running enrich_flood_risk or enrich_crime_risk on coordinates lets you factor environmental or security metrics into your underwriting process, not just the mailing address.
  • Handle ambiguity with geocode_address. Instead of failing on partial input, it resolves candidates and gives you a match confidence score, telling you how close to perfect the data is.

Real-World Use Cases

01

New E-commerce Checkout Flow

A customer enters an address. Your agent first runs verify_address to clean and confirm the format. Then, it uses geocode_address for coordinates. Finally, it calls get_local_tax to calculate the exact tax total before showing the final price, preventing checkout errors.

02

Real Estate Due Diligence

An analyst needs a full picture of a property. They feed coordinates into the agent which then runs get_property_info (for square footage), enrich_flood_risk (for risk scores), and enrich_demographics (for local wealth indicators) in one conversational turn.

03

Logistics Route Planning

A logistics worker needs to check a delivery point. The agent uses reverse_geocode on the GPS coordinates to confirm the nearest street address, then calls get_timezone so the tracking status updates correctly for that locale.

04

Fraud Detection and Risk Scoring

A payments system flags an order. The agent queries both enrich_crime_risk and enrich_demographics using the coordinates to see if the location shows unusually high risk or a demographic profile inconsistent with typical buyers.

The Tradeoffs

Assuming street data is enough

Trying to calculate local taxes or property size just by using basic string parsing on the address input, which fails when jurisdiction lines overlap.

Always use geocode_address first for coordinates. Then, pass those coordinates into specialized tools like get_local_tax or get_property_info. Don't try to calculate it; let the tool do the math.

Using generic address validation

Relying on simple postal code checks that don't account for multi-state jurisdiction rules or specific property boundaries.

Use verify_address to get authoritative, standardized formatting. If you need tax details, follow up with get_local_tax using the validated coordinates.

Ignoring coordinate order

Calling reverse_geocode and passing (latitude, longitude) instead of the required (longitude, latitude) format.

Remember the tool signature: for reverse_geocode, always pass the coordinates as x = longitude and y = latitude. It's a common trap.

When It Fits, When It Doesn't

Use this server if your workflow requires verifiable, multi-layered context tied to precise physical location. You need data points that go beyond simple street naming—think tax jurisdiction boundaries, specific census blocks, or FEMA flood zones. If you are building a system where the outcome depends on where something is (e.g., billing, zoning, logistics), this is essential.

Don't use it if all you need is to clean up obvious typos or standardize a simple mailing address for a database field—basic validation might suffice. If your problem is general data cleaning without location dependency, look at specialized data transformation APIs instead of a geo-intelligence stack.

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Precisely. 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.

Available Capabilities

autocomplete_address enrich_crime_risk enrich_demographics enrich_flood_risk geocode_address get_local_tax get_property_info get_timezone reverse_geocode verify_address

Manually checking tax and risk zones is a nightmare process.

Right now, if you're setting up an e-commerce checkout or running compliance checks, your team spends time cross-referencing state tax maps with county boundaries. If the address changes by just one block, you have to re-run complex logic—it’s a multi-step process that breaks every time.

With this MCP server, the agent handles it all. You give it the coordinates for '1 Market Street.' It runs `get_local_tax` and immediately spits out the combined rate (State: 6%, County: 0.25%...). The complexity disappears into a single API call.

Use get_property_info to pull structural details, not just addresses.

Before, getting property data meant calling the county assessor's website and dealing with fragmented databases. You'd manually collect lot size from one source, square footage from another, and assessed value from a third—it was tedious copy-pasting across three tabs.

Now, just ask the agent for 'property attributes.' It runs `get_property_info` and returns everything in structured JSON: lot size, building area, year built, and ownership data. You get the full record instantly.

Common Questions About Precisely MCP

How do I use geocode_address to find coordinates for a partial address? +

Pass the most complete string you have into geocode_address. The API returns candidates and includes a match confidence score. This helps your agent decide which coordinate set is the best fit, even if the input was vague.

What's the difference between verify_address and autocomplete_address? +

Autocomplete suggests completions as you type (like a predictive text tool). verify_address takes an already completed address and checks it against authoritative datasets to confirm its formal standard and deliverability status.

Can I use get_local_tax for tax calculations in non-US areas? +

No. The get_local_tax tool is explicitly designed to resolve complex, overlapping US sales tax jurisdictions down to the rooftop level; it only covers US territories and states.

Which tool should I use for finding a city's timezone? +

Use get_timezone. It takes coordinates and returns the precise IANA identifier (like America/New_York), which is necessary for accurate time-based logic, especially when accounting for DST.

If I run many addresses through `geocode_address` in a short period, how does the system handle rate limits? +

The server enforces standard API rate limiting. For high-volume tasks (e.g., processing thousands of records), you must implement client-side throttling and use exponential backoff logic to prevent hitting usage caps.

Can I run `get_property_info` if the US address doesn't exist in county assessor databases? +

No. This tool requires an active, verifiable US physical address tied to official records. If the data isn't present in the source county database, the call will fail because it cannot retrieve lot size or assessed value.

Before I run `enrich_demographics`, do I always have to use `geocode_address` first? +

Yes. The demographic tools rely strictly on precise latitude and longitude coordinates, not text addresses. Always pass the output of a successful geocoding call into your enrichment process.

If I use `reverse_geocode` on coordinates that are in an open area (like a field), what information do I receive? +

It returns the nearest structured address record. If no nearby building exists, it provides the general location type (e.g., 'Rural Area') but will not populate specific street names or postal codes.

Can the AI find out if a specific property is inside a flood zone? +

Yes. First the AI uses geocode_address to find the exact Latitude/Longitude of the building's rooftop. Then it feeds those coordinates into enrich_flood_risk to extract the strict FEMA designation naturally.

How accurately does verify_address standardize locations? +

Very accurately. It connects to native postal services (like USPS and Royal Mail) to autocorrect misspellings, adjust zip codes, and flag impossible addresses before they reach your logistics layer.

Do I need two separate keys to use this? +

Yes. Precisely Developer APIs use robust OAuth workflows. You must supply both the individual API Key (Consumer Key) and the API Secret. The MCP server handles token exchanging for you internally.

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