API and MCP data layer giving AI agents cited physical-world and location context.
Mireye is infrastructure for AI agents that need reliable facts about physical places. Its current surface combines a REST API and MCP tools for natural-language location questions, geocoding, structured field retrieval, parcel/location lookup, proximity analysis and requests for new data fields.1
The product differentiates itself through provenance. Returned values are designed to carry a source identifier and URL, fetch timestamp and confidence metadata, letting downstream agents expose where a physical-world fact came from rather than presenting an unsupported model estimate.1 Y Combinator independently corroborates the core API + MCP positioning and the company's focus on location-aware agents.2
The catalog is primarily US-focused, with drive-time analysis extending to Canada. Current use cases span data-center and renewable-energy siting, property diligence, insurance underwriting, mortgage/title analysis and other workflows where location data materially changes a decision.1
Mireye publishes unusually clear credit-based pricing. The Free plan includes 5,000 credits per month, Build costs $19/month for 25,000 credits, Growth costs $99/month for 120,000 credits, and Enterprise is custom-priced.3 Usage stops when the allowance is exhausted rather than generating automatic overage charges.
The pricing page also publishes per-operation credit costs and rate limits, which makes small pilots comparatively easy to budget. The trade-off is that workloads with expensive parcel lookups or high-volume field enrichment still need workload-specific cost modelling.3
The strongest public evidence is the product's own detailed API surface and provenance model, corroborated at the product-category level by Y Combinator and independently by APIs.io, which describes the REST and MCP delivery model and per-value provenance.124
Independent evidence about production reliability, customer outcomes and enterprise security is much thinner. Mireye is a 2026 company, and broad exact-product professional-review or user-rating coverage is not yet available. That absence is not negative evidence, but it materially limits how strongly the product can be validated from independent public evidence today.
For production use, buyers should separately verify contractual data rights, security controls, availability requirements and whether the specific fields they depend on have the necessary geographic coverage and update cadence.
Mireye is best suited to developers and domain teams building AI agents that must reason about places: site-selection systems, energy and infrastructure workflows, property diligence, underwriting, lending and other location-sensitive decision support.1
It is less suitable when global coverage is mandatory, when a team needs a mature long-term operating record, or when procurement requires extensive independently documented enterprise-security and reliability evidence before a pilot.
the combined API/MCP interface, explicit source provenance, broad physical-world task coverage, transparent entry pricing and a design that can return typed failures rather than forcing an agent to invent a value when a location or field cannot be resolved.13
early-stage maturity, limited independent validation, primarily US catalog coverage and the need to assess field-by-field data licensing, freshness and production guarantees for higher-stakes workflows. Mireye is therefore compelling as an agent data layer, but production adoption should follow a controlled coverage and reliability evaluation rather than vendor claims alone.
Products matched by shared tasks, audience, workflow, product type and capabilities.