Mireye: Federal-Grade Geospatial MCP for AI Agents 2026
Mireye MCP gives AI agents federal-grade geospatial ground truth for any US coordinate: terrain, land cover, buildings, water, and risk data. YC S26 startup.
Mireye is a 2026 MCP server that plugs AI agents into federal-grade geospatial data for any US coordinate, returning sourced terrain, land cover, buildings, water, and risk information. Built by a 2-person YC S26 team in San Francisco, it targets agent builders who need reliable physical-world context. Pricing is not listed publicly; contact the team at mireye.com.
Mireye is an MCP server and API that gives AI agents cited geospatial ground truth for any US coordinate. Founded in 2026 (YC S26), it indexes 5 federal data layers: terrain, land cover, buildings, water, and risk. Agents query a location and get sourced answers instead of model guesses. Pricing is not publicly listed; contact mireye.com.
Maker: Mireye · Protocol: MCP · Auth: api key
Compatible agents: Claude Desktop, Cursor, Windsurf, Any MCP-compatible client
Required runtime: Node.js >= 18 (for MCP client), Valid Mireye API key (request at mireye.com)
About Mireye
Mireye is geospatial infrastructure built for AI agents that need to reason about real-world locations. Founded in 2026 by Ansh Chokshi and Shashwat Kapoor as a Y Combinator S26 company in San Francisco, the 2-person team addresses a concrete gap: when AI agents answer questions about specific physical places, the underlying models guess. Mireye fixes that by indexing federal-grade data for every US coordinate and returning sourced, cited answers instead of hallucinations. The product runs as both a Model Context Protocol (MCP) server and a REST API. Agents connect to the MCP server through their standard configuration file, then call Mireye tools to pull terrain, land cover, building footprint, water body, and natural-hazard risk data for any latitude/longitude pair in the United States. Each answer comes with source attribution, so an agent can cite what it found rather than assert it. Mireye works with any MCP-compatible agent runtime, including Claude Desktop, Cursor, Windsurf, and custom agent frameworks that implement the Model Context Protocol. Agent builders who need physical-world context for site analysis, infrastructure planning, emergency routing, or environmental reporting can add Mireye to their agent config and immediately gain access to 5 layers of federal geospatial data. Pricing is not publicly listed as of June 2026. Mireye is early-stage and backed by Y Combinator (S26), so developers should contact the team through mireye.com to request API access and pricing details. A free tier or developer sandbox is common for YC-backed API products at this stage, though nothing is confirmed. Mireye's 2026 focus is building out coverage depth for US coordinates. The team's stated long-term mission is to index every inch of the earth and make it as queryable as the web, with global expansion planned after the US data layer is complete.
Key Features
- Federal-grade geospatial data: Mireye sources terrain, land cover, building, water, and risk data from US federal datasets, so every answer includes a citation rather than a model inference.
- 5 data layers per coordinate: A single coordinate query returns up to 5 structured data layers: terrain/elevation, land cover classification, building footprints, water bodies, and natural hazard risk scores.
- MCP + REST API interface: Mireye runs as a Model Context Protocol (MCP) server for agent runtimes like Claude Desktop and Cursor, and also exposes a REST API for direct HTTP integration in any codebase.
- Cited answers, not guesses: Every data point returned by Mireye includes the source dataset (e.g., USGS National Elevation Dataset, NLCD land cover), giving AI agents provenance for every claim about a location.
- US coordinate coverage: Mireye covers all US coordinates as of 2026, with the roadmap targeting global coverage as the team scales its data indexing infrastructure.
Use Cases
- Site suitability analysis: An agent queries terrain slope, land cover type, and flood risk for a proposed construction site and returns a structured report with source citations from federal datasets.
- Emergency routing context: A first-responder AI agent checks water and risk data for any US coordinate in real time to inform routing decisions around flood zones or wildfire areas.
- Environmental due diligence: A real-estate AI agent pulls building footprint, land cover, and risk scores for any US parcel to flag environmental liabilities before a transaction closes.
Requirements
- Request API access at mireye.com (no public self-serve signup as of June 2026)
- Node.js >= 18 installed if using the MCP server integration
- Add Mireye to your MCP client config file with your API key
Actions
Query Location
Returns all available geospatial data layers (terrain, land cover, buildings, water, and risk) for a given US coordinate, each with source attribution from federal datasets.
// MCP tool call
{
"tool": "query_location",
"params": {
"latitude": 37.7749,
"longitude": -122.4194
}
}latitude(number) — required: Decimal degrees latitude of the target US coordinate (e.g. 37.7749).longitude(number) — required: Decimal degrees longitude of the target US coordinate (e.g. -122.4194).layers(array): Subset of data layers to return: terrain, land_cover, buildings, water, risk. Returns all layers if omitted.
Get Terrain
Returns terrain and elevation data (elevation in meters, slope gradient, and aspect direction) for a US coordinate with federal dataset source attribution.
// MCP tool call
{
"tool": "get_terrain",
"params": {
"latitude": 40.7128,
"longitude": -74.0060
}
}latitude(number) — required: Decimal degrees latitude.longitude(number) — required: Decimal degrees longitude.
Get Risk Data
Returns natural hazard risk scores for a US coordinate, covering flood, wildfire, wind, and earthquake risk with federal source citations for each score.
// MCP tool call
{
"tool": "get_risk_data",
"params": {
"latitude": 34.0522,
"longitude": -118.2437
}
}latitude(number) — required: Decimal degrees latitude.longitude(number) — required: Decimal degrees longitude.
How to Invoke
Exposed as MCP tools registered in the agent's config file; the agent calls Mireye's location query functions by name once the server is active. Also available as a REST API for direct HTTP integration with an API key in the Authorization header.
Pricing
Pricing not publicly listed as of June 2026. Mireye is an early-stage YC S26 startup; request API access and pricing at mireye.com.
Strengths
- Returns sourced, cited geospatial answers rather than model guesses, which is critical for any agent that needs to assert facts about physical locations.
- Dual interface (MCP server and REST API) means it works with any MCP-compatible agent out of the box and also with custom HTTP-based agent pipelines.
- Backed by Y Combinator (S26), which signals active development and a team responsive to early adopters and feedback.
Weaknesses
- US-only coverage as of June 2026; agents that need international coordinates must wait for the global expansion roadmap.
- Pricing is not publicly listed, which creates friction for developers who want to evaluate cost before requesting access.
- 2-person team means feature velocity and support capacity are limited compared to larger geospatial API providers.
Frequently Asked Questions
What is Mireye and what does it do?
Mireye is a geospatial infrastructure product for AI agents, launched in 2026 by Ansh Chokshi and Shashwat Kapoor as a Y Combinator S26 company based in San Francisco. It provides an MCP (Model Context Protocol) server and REST API that gives AI agents cited, sourced answers about any US location. The core problem it solves is that AI models guess when asked specific questions about physical places, because geospatial data is complex, fragmented, and unstructured across dozens of federal agencies. Mireye indexes those federal geospatial datasets and returns structured, cited answers covering 5 data layers: terrain, land cover, buildings, water, and natural hazard risk. Each answer includes a source attribution, so an agent can report where the data came from rather than assert a hallucinated fact. With a team of 2 and YC backing, the product is in active early development as of June 2026, with the stated mission of indexing every inch of the earth and making it as queryable as the web.
How much does Mireye cost in 2026?
Mireye does not publicly list pricing as of June 2026. The company is an early-stage YC S26 startup with a team of 2, and pricing is available by contacting the team directly through mireye.com. This is typical for developer API products at the seed stage, where pricing is often negotiated with early design partners rather than published on a pricing page. There is no confirmed free tier, but YC-backed API startups commonly offer a developer sandbox or trial credits to encourage adoption. Developers who want to evaluate cost before requesting access may find this friction point a consideration when comparing Mireye to federal APIs that are free at the source. As the company scales, a public pricing page with per-call or subscription tiers is likely to appear. The best approach for now is to contact the founding team directly to discuss needs and pricing.
What geospatial data layers does Mireye provide?
Mireye provides 5 data layers for any US coordinate: terrain (elevation, slope, and aspect from sources like the USGS National Elevation Dataset), land cover (classification of vegetation, urban area, water, and other surface types from the National Land Cover Database), buildings (footprints, height, and use data), water bodies (rivers, lakes, flood zones, and watersheds), and natural hazard risk (flood, wildfire, wind, and seismic risk scores sourced from FEMA and other federal agencies). Each layer returns structured data with a source citation, so the agent knows which federal dataset the answer came from. Coverage as of 2026 is limited to US coordinates, with global expansion on the roadmap. The data sourcing from US federal agencies gives it a high authority level for applications that require defensible, cited answers in regulated industries like real estate, insurance, and emergency management.
Is Mireye free to use?
Mireye does not publicly offer a free tier as of June 2026. The company is in its early stage as a YC S26 startup, and API access requires contacting the team at mireye.com rather than signing up through a self-serve flow. It is possible that a developer sandbox or trial access exists for early adopters, as this is common among YC-backed API companies trying to grow their developer base. There is no confirmed open-source version or self-hosted option; the product appears to be a managed service. Developers who need to test the integration before committing to a paid plan should reach out directly to the team to ask about trial options. If a free tier or low-cost entry point is a hard requirement, it is worth confirming the tier structure before starting integration work, since publicly available information does not confirm one.
What are the best alternatives to Mireye?
The closest alternatives to Mireye for AI agents needing geospatial context are Esri's ArcGIS REST API, Mapbox's APIs, and the raw US federal dataset APIs (USGS, FEMA, NLCD) accessed directly. Esri ArcGIS offers comprehensive geospatial data but is priced for enterprise GIS workflows and requires more integration work to connect to an MCP-based agent pipeline. Mapbox provides excellent mapping and geocoding APIs but focuses on visualization and routing rather than physical-world ground truth with federal source citations. The raw federal APIs (USGS Elevation API, FEMA Flood Map API) provide the same underlying data for free but are fragmented across multiple endpoints, use inconsistent response formats, and require separate integration work without any MCP support. Mireye's value is aggregating those federal datasets into a single MCP-native interface with per-answer source attribution, which none of the alternatives provide out of the box for agent developers.
Who is Mireye best for?
Mireye is best for agent engineers building AI systems that need to reason about US physical locations, including site analysis for construction or real estate, emergency routing with hazard context, environmental due diligence, and infrastructure planning. It is also a strong fit for LLM developers who are seeing their models hallucinate when asked about specific addresses or coordinates and want a drop-in MCP server to ground those answers in federal data. Product teams building vertical AI agents for insurance, logistics, utilities, or government applications will find the 5 data layers (terrain, land cover, buildings, water, risk) particularly relevant to their domain. Mireye is not the right fit for international use cases (US-only as of 2026), for teams that need a free tier with immediate self-serve access, or for visualization-focused applications where a mapping SDK like Mapbox is a better match.
How do you get started with Mireye?
Getting started with Mireye requires requesting API access through the team at mireye.com, since there is no self-serve signup as of June 2026. Once you have an API key, you add Mireye to your MCP client configuration file: for Claude Desktop this is at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%/Claude/claude_desktop_config.json on Windows. The config entry registers the Mireye MCP server so your agent can call its location query tools by name. Alternatively, you can integrate the REST API directly into any codebase using standard HTTP requests with your API key in the Authorization header. Node.js 18 or later is required if you are using the MCP server integration path. The team is reachable for onboarding support given the early-stage product and the hands-on nature typical of YC S26 companies.
How does Mireye compare to raw US federal geospatial APIs in 2026?
Mireye wraps US federal geospatial datasets (USGS, FEMA, NLCD, and others) into a single MCP-native interface, while raw federal APIs require hitting each agency's endpoint separately, parsing inconsistent response formats, and stitching the results together in your agent pipeline. The USGS Elevation Point Query Service, FEMA Flood Map API, and National Land Cover Database each have distinct authentication schemes, rate limits, and data formats, making it time-consuming to build a unified agent integration from scratch. Mireye handles that aggregation and normalization, returning a single structured response per coordinate with source citations included in every answer. The raw federal APIs are free, while Mireye charges for the integration layer (pricing not publicly listed). For a production agent that queries many locations across multiple data types, the developer time saved by using Mireye instead of building and maintaining a multi-API federal data pipeline is the primary trade-off to evaluate against the cost of Mireye's service.