Mireye
PaidProvenance-tagged geospatial data for any US coordinate, built for AI agents and underwriters.
By Tanmay Verma, Founder · Last verified 03 Jul 2026
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Added 6d ago
77/100Safe BetIn short
Mireye — Provenance-tagged geospatial data for any US coordinate, built for AI agents and underwriters. Best for Insurance underwriters needing auditable geospatial data, Lenders assessing property risk with federal-source citations, AI agents requiring provenance-tagged geospatial context. Paid pricing.
Is Mireye actually worth it?
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Editorial Verdict
Best for
Insurance underwriters needing auditable geospatial dataLenders assessing property risk with federal-source citationsAI agents requiring provenance-tagged geospatial contextGIS developers building MCP-native geospatial toolsSite selection analysts evaluating terrain, utilities, and hazards
Not ideal for
Users needing global coverage outside the US and territoriesProjects requiring real-time streaming data or historical time seriesTeams without technical ability to integrate REST APIs or MCP serversUsers expecting proprietary risk scores or AI-derived values
Mireye Earth fills a critical niche for verifiable geospatial data, especially for regulated industries like insurance and lending. Its MCP-native design is forward-thinking for agent workflows, but the US-only coverage and absence of proprietary scores limit its appeal for global or predictive analytics.
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Last verified: July 2026
What independent users actually report about Mireye
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
39% positive61% critical
Recurring strengths
- +Provenance-tagged data with exact federal source citations.
- +Dual API endpoints: LLM-synthesized answers and raw field values.
- +MCP server integrates directly with Claude Desktop and Cursor.
- +207 fields across 7 layers covering terrain, land cover, climate, etc.
- +Partial failure reporting with retryable flag for unavailable fields.
Recurring frustrations
- −No community reviews or testimonials to verify claims.
- −Pricing is opaque – no free tier or transparent cost breakdown.
- −Documentation is sparse and lacks troubleshooting examples.
- −Support channels are not publicly visible or easily accessible.
- −Retry limits on partial failures can block critical workflows.
Patterns worth knowing
Provenance tagging is praised but data reliability concerns persist due to lack of independent validation.
Seen on Reddit, Hacker News, Product Hunt
Documentation and pricing transparency are insufficient for a paid API.
Seen on Bluesky, Reddit, Stack Overflow
MCP server integration is a standout feature for agent workflows.
Seen on GitHub, Reddit
Learning curve
beginnerProductive in ~A few hours
Hidden costs people mention
- • No free tier means you must commit payment before testing data quality.
- • Retry failures may incur additional costs if charged per request.
- • Overage or rate-limit penalties are unstated.
Viability Score
77/100
Safe BetHow likely is Mireye to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Provenance-tagged geospatial data for any US coordinate
- Two HTTP endpoints: /v1/ask (LLM-synthesized answers) and /v1/fetch (raw field values)
- MCP server for native integration with Claude Desktop, Cursor, and MCP agents
- Per-field citations with source, source_url, fetched_at, and confidence bucket
- 207 fields across 7 layers: terrain, land cover, built environment, utilities, parcels, climate, hazards
- 14 use-case presets: terrain, flood_risk, wildfire_underwrite, land_cover, site_selection, building_lookup, utilities, boundaries, solar_siting, wind_siting, storage_siting, data_center_siting, grid_interconnect, natural_hazard
- Partial failure reporting with retryable flag for unavailable fields
- Public field catalog at GET /v1/meta/fields
- Authentication via bearer token, dashboard API token, device-flow login, or MCP OAuth
About Mireye
PaidIntermediateAPI availableAPI · Plugin
Mireye Earth provides provenance-tagged geospatial data for any US coordinate, answering questions with federal-source citations. It offers two HTTP endpoints and an MCP server that share the same field catalog and provenance model. The POST /v1/ask endpoint uses a planner LLM to select relevant fields, fetches them in parallel, and returns a synthesized answer with inline citations. The POST /v1/fetch endpoint allows developers to request raw per-field values with provenance and partial failure reporting. The MCP server exposes both operations as tools for Claude Desktop, Cursor, and custom MCP agents. Mireye is designed for insurance underwriters, lenders, and AI agents that need an audit trail, not just an answer. It covers 207 fields across 7 layers (terrain, land cover, built environment, utilities, parcels, climate, hazards) from federal sources only, with no proprietary risk scores or third-party brokers.
Behind the Verdict
Mireye Earth is a niche but powerful tool for professionals who need verifiable, federal-source geospatial data. It excels in insurance underwriting, lending, and regulatory compliance where audit trails are mandatory. The MCP-native integration is a smart bet for AI agent ecosystems, but the lack of a free tier and global coverage means it won't replace broader GIS platforms. For teams already using Claude Desktop or Cursor, the MCP server reduces friction significantly. However, if you need proprietary risk scoring, historical data, or international coverage, you'll need to supplement Mireye with other tools. Overall, it's a solid choice for US-focused, compliance-heavy workflows.
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Use Cases
- Underwrite property insurance by verifying flood zone, wildfire fuel load, and nearest power plant with federal citations.
- Assess mortgage risk by fetching parcel zoning, elevation, and proximity to utilities for a given coordinate.
- Site a solar farm using the solar_siting preset which bundles irradiance, land cover, and terrain data.
- Evaluate a data center location by pulling grid interconnect, flood risk, and wildfire hazard data.
- Build an AI agent that answers geospatial questions about any US address with verifiable sources.
- Automate compliance reporting by extracting and citing federal-source geospatial attributes for a portfolio of properties.
Limitations
- Data requests are authenticated with a bearer token; free tier is not mentioned.
- Out-of-bounds coordinates return a 400 error.
- The service covers only the US including territories, with no global data.
Integrations
Claude DesktopCursor
Resources & Guides
Frequently Asked Questions
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