Mireye

Mireye is a cited geospatial data API and MCP (Model Context Protocol) server designed to connect AI agents to physical-world context. It exposes a single API surface with endpoints such as /v1/ask (natural-language questions about a place), /v1/geocode (resolving any string into a place), /v1/lookup (aggregating the full data stack from one input string), /v1/fetch (retrieving cited facts at a location), /v1/proximity (computing measurements the raw data cannot express, such as distance to the nearest transmission line), and /v1/field-requests (requesting data fields that do not yet exist in the catalog). The service positions itself as infrastructure for agents rather than a human-facing map product. It is intended to be called by assistants and agent frameworks such as Claude, ChatGPT, Gemini, Cursor, Grok, Copilot, DeepSeek, or custom agents, which query it through the API or MCP tools and receive structured responses containing values, sources, fetch timestamps, and confidence indicators. Mireye emphasizes provenance: the site states that every value is cited, listing source organizations including USGS, FEMA, NOAA, USDA, EPA, EIA, FCC, Census, NREL, USFWS, USFS, FHWA, FAA, BTS, USACE, BLM, HUD, FHFA, BLS, NSIDC, Overture, Regrid, JRC, and Sentinel-2. Example outputs include elevation from USGS 3DEP with a datum, source URL, fetch date, and confidence level. The vendor markets the product around sector workflows rather than generic mapping, with example desks including data center siting (slope, FEMA flood zone, nearest 345 kV line, substation voltage and status, county interconnection queue), renewable energy siting (slope, aspect, irradiance, interconnect distance, soil drainage class), and insurance underwriting (flood zone, base flood elevation, design wind speed, coastal distance). Mireye is backed by Y Combinator. Pricing details are not stated in the supplied homepage content.

Category: AI API
Pricing: Unknown

Problem solved

General-purpose language models have no reliable, current, or sourced access to physical-world facts about a specific location, so they answer geospatial questions with estimates, missing units, and no provenance. Mireye provides one API and MCP interface that returns cited, timestamped location data, enrichment, signals, and computed proximity measures, so AI agents can make auditable decisions about real places.

Use cases

Answering natural-language questions about a specific place or coordinate with cited sources
Geocoding or resolving arbitrary text inputs into canonical place identifiers
Screening candidate sites for data centers, including slope, flood zone, and nearby transmission infrastructure
Screening renewable energy sites using slope, aspect, irradiance, interconnect distance, and soil drainage
Underwriting insurance exposure with flood zone, base flood elevation, and design wind speed data
Enriching a record or address with a single lookup across multiple federal and third-party datasets
Computing proximity measures such as distance to nearest substation, coastline, or facility
Requesting a data field that is not yet in the catalog so it can be built and made available to all callers

Audience

Developers
ML Engineers
Data Scientists
Enterprise
Startups

Pros

Single API surface spanning data, enrichment, tools, and signals
Every returned value is cited with source, fetch timestamp, and confidence
MCP support makes integration with common AI agents and assistants straightforward
Purpose-built for agentic and physical-world decision workflows rather than human map browsing
Field-request mechanism allows gaps in the catalog to be filled over time

Cons

Source data appears heavily weighted toward United States federal datasets, limiting global coverage
Pricing and rate limits are not published in the supplied homepage content
No public repository, SDK list, or technical documentation details were available for review
Accuracy and coverage depend on third-party upstream sources and their update cadence

Official website: https://www.mireye.com/

Canonical LambdaBase page: https://www.lambdabase.com/tools/mireye

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Mireye

Unknown

• Updated Sep 15, 2026, 03:19 PM

Mireye is a cited geospatial data API and MCP (Model Context Protocol) server designed to connect AI agents to physical-world context.

Use Cases ▼

Answering natural-language questions about a specific place or coordinate with cited sources

Geocoding or resolving arbitrary text inputs into canonical place identifiers

Screening candidate sites for data centers, including slope, flood zone, and nearby transmission infrastructure

Screening renewable energy sites using slope, aspect, irradiance, interconnect distance, and soil drainage

Underwriting insurance exposure with flood zone, base flood elevation, and design wind speed data

Enriching a record or address with a single lookup across multiple federal and third-party datasets

Computing proximity measures such as distance to nearest substation, coastline, or facility

Requesting a data field that is not yet in the catalog so it can be built and made available to all callers

What Problem It Solves ▼

Key Features ▼

✓

Single API surface spanning data, enrichment, tools, and signals

✓

Every returned value is cited with source, fetch timestamp, and confidence

✓

MCP support makes integration with common AI agents and assistants straightforward

✓

Purpose-built for agentic and physical-world decision workflows rather than human map browsing

✓

Field-request mechanism allows gaps in the catalog to be filled over time

Overview

Full Description ▼

The service positions itself as infrastructure for agents rather than a human-facing map product. It is intended to be called by assistants and agent frameworks such as Claude, ChatGPT, Gemini, Cursor, Grok, Copilot, DeepSeek, or custom agents, which query it through the API or MCP tools and receive structured responses containing values, sources, fetch timestamps, and confidence indicators.

Mireye emphasizes provenance: the site states that every value is cited, listing source organizations including USGS, FEMA, NOAA, USDA, EPA, EIA, FCC, Census, NREL, USFWS, USFS, FHWA, FAA, BTS, USACE, BLM, HUD, FHFA, BLS, NSIDC, Overture, Regrid, JRC, and Sentinel-2. Example outputs include elevation from USGS 3DEP with a datum, source URL, fetch date, and confidence level.

The vendor markets the product around sector workflows rather than generic mapping, with example desks including data center siting (slope, FEMA flood zone, nearest 345 kV line, substation voltage and status, county interconnection queue), renewable energy siting (slope, aspect, irradiance, interconnect distance, soil drainage class), and insurance underwriting (flood zone, base flood elevation, design wind speed, coastal distance). Mireye is backed by Y Combinator. Pricing details are not stated in the supplied homepage content.