---
title: "Mireye Land Search API: MCP, Pricing and Screening"
canonical: https://wavect.io/blog/mireye-land-search-enrich-api-mcp/
language: en
description: "Use Mireye Search, Enrich and free Fetch for U.S. parcel screening. See MCP setup, per-page costs, field limits, reranking and source checks."
image: "https://wavect.io/img/blog/headers/header_mireye-land-search-enrich-api-mcp.png"
author: ["Kevin Riedl"]
published: "2026-10-23"
updated: "2026-10-23"
reviewed: "2026-10-23"
---

# Mireye Land Search and Enrich API: MCP, Pricing and Parcel Screening

[**Back**](/blog/overview/)

[![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/)

[Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt

11 min read · 23 Oct 2026 Last reviewed October 23, 2026

[**Next**](/blog/ai-agent-cost-per-action-2026/)

TL;DR

Mireye Search finds U.S. parcels; Enrich investigates a selected property; standard authenticated Fetch supplies eligible public location fields for free. Search costs $0.20 per completed page and Enrich $0.02 per matched parcel with returned fields on the Free plan. Five pages plus 20 chargeable enrichments therefore cost $1.40 before model and review costs. Use current field catalogs, verify parcel identity, preserve unknowns and rank candidates with explicit criteria.

Header illustration: © 2026 Wavect.

A useful land-search agent should return a shortlist someone can investigate: identifiable parcels, explicit selection criteria, traceable evidence and unanswered questions. Mireye's new Land Search and Enrich API provides building blocks for that workflow through REST and the Model Context Protocol (MCP).

In its [launch announcement](https://www.linkedin.com/posts/ansh-chokshi_introducing-land-search-and-enrich-api-activity-7519083978218180608-9C2E), Mireye reports more than **160 million U.S. parcels, 550 available data fields, billions of indexed data points and four million historical Fetch calls**. Those are vendor figures. The practical news is the combination of parcel discovery, selected-property enrichment and free access to supported public location fields.

**Checked on 23 October 2026:** we reviewed primary documentation and retrieved the three public field catalogs. We did not run authenticated parcel searches, benchmark latency or independently audit nationwide coverage. The examples below are proposed workflows and derived costs.

**Use Search to discover parcels, Enrich to investigate a selected parcel, and Fetch for supported public facts at a known location.** The endpoint determines the available evidence as well as the bill.

| Operation | Best starting point | What you receive | Usage price |
| --- | --- | --- | --- |
| Search | State, acreage and other supported requirements | Matching parcels and selected field values | $0.20 per completed page |
| Enrich | One U.S. address or coordinate | A parcel match and selected fields with source, confidence and interpretation hints | $0.02 per matched parcel when at least one field is returned |
| Fetch | A known location and eligible public fields | Structured public data with field-level provenance and status | Free through standard authenticated access |
| Ask | A natural-language location question | A generated answer with citations | $0.10 per completed answer |

The [current pricing page](https://www.mireye.com/pricing) lists a one-time **$10 signup balance without expiry**. Paid plans start at $49 per month with included usage and lower Search and Enrich rates. The Free plan's name does not make its parcel operations free.

Free Fetch excludes licensed parcel records, including parcel identity, ownership, parcel boundaries and parcel zoning, plus calculations that depend on those records. Authentication and capacity limits still apply. The separate API-key-free [x402 payment route](https://docs.mireye.ai/pricing) costs $0.001 USDC per successful Fetch call. “Free Fetch” therefore needs the access-method and field-scope qualification.

## How much does a Mireye parcel shortlist cost?

**At Free-plan rates, five completed Search pages plus 20 chargeable Enrich calls cost $1.40 in Mireye usage.** This is an illustrative budget, not the measured cost of finding 20 suitable properties.

| Step | Assumption | Calculation |
| --- | --- | --- |
| Discover candidates | Five paid Search pages | 5 × $0.20 = $1.00 |
| Investigate finalists | 20 Enrich operations returning at least one field each | 20 × $0.02 = $0.40 |
| Additional public location checks | Eligible standard authenticated Fetch calls | $0.00 in Fetch usage |
| Mireye subtotal | These operations only | **$1.40** |

Search includes the output fields requested on that page. Returning a field through Search does not automatically add a separate Enrich charge. A page can return fewer parcels than requested, including zero; the example does not assume five full pages or an exhaustive search. Model tokens, orchestration, paid driving calculations and human investigation are outside this subtotal.

The $10 welcome balance corresponds arithmetically to 50 Search pages or 500 chargeable Enrich operations at these rates, if spent entirely on one operation. For production economics, measure [cost per accepted agent outcome](/blog/ai-agent-cost-per-action-2026/): a cheap API call has little value if its parcel match or evidence must be repaired.

## Are all 550 advertised fields searchable?

**Do not treat the launch total as a filter schema.** Our public-catalog check returned version `0.18.0` with these endpoint-specific inventories:

- [Search catalog](https://api.mireye.com/v1/search/fields): 529 returnable entries, of which 510 had at least one filter operator.
- [Enrich catalog](https://api.mireye.com/v1/enrich/fields): 525 fields.
- [Fetch catalog](https://api.mireye.com/v1/meta/fields): 351 fields.

These are different sets, so comparing their totals does not establish that the launch figure is wrong. More usefully, the [Search discovery contract](https://docs.mireye.ai/api-reference/get-search-fields) distinguishes `filter_ops` from `returnable`. In the retrieved catalog, `parcel_flood_sfha_fraction` could be returned but had no filter operators. An agent should request that result and evaluate it afterwards, rather than invent a server-side filter.

Catalog discovery is unauthenticated and free. Through MCP, use `mireye_list_fields` with the relevant `endpoint` and an optional search term. Check the catalog when constructing a workflow; avoid permanently embedding launch-era field counts in application logic.

## How do you connect Mireye to Claude Code through MCP?

The [supported MCP connection](https://docs.mireye.ai/mcp/installation) is hosted at `https://api.mireye.com/mcp`, using Streamable HTTP and OAuth. For Claude Code:

```
claude mcp add --transport http --scope user mireye-earth https://api.mireye.com/mcp
```

Then open `/mcp` and complete authorization. Other clients need support for the remote transport and authorization flow. The older local adapter and device-login instructions are retired.

[Search and Enrich authorization](https://docs.mireye.ai/authentication) also depends on current terms and licensed-data permissions. Existing connections may need reauthorization; an old OAuth grant does not acquire the new permissions automatically. Direct HTTP API keys and hosted MCP OAuth tokens have different roles.

## A bounded MCP workflow for U.S. land screening

### 1. Turn the brief into explicit constraints

For an illustrative solar-screening brief, start with a named county, an acreage range and the evidence needed for a first review. Separate hard requirements from preferences. “Between 100 and 1,000 acres” is an explicit filter; “good grid access” needs a defined measure and further investigation. Keep U.S. acreage units in requests even when the interface is localized.

The [hosted tool contract](https://docs.mireye.ai/mcp/tools) puts paid request bodies inside `req`, with `idempotency_key` beside it. This example is a request to `mireye_search`, not a recorded API result:

```
{
  "req": {
    "filters": [
      {"field": "state2", "op": "eq", "value": "AZ"},
      {"field": "county", "op": "eq", "value": "04013"},
      {"field": "pvd_gisacre", "op": "between", "value": [100, 1000]}
    ],
    "fields": ["owner", "zoning", "parcel_flood_sfha_fraction"],
    "limit": 10,
    "max_cost_usd": "0.20"
  },
  "idempotency_key": "az-parcel-screen-example-page-001"
}
```

The county FIPS code remains a string so its leading zero survives. The flood fraction is requested as output. Replace the illustrative operation key for a new search; preserve it when retrying the same request.

### 2. Track completion and spending separately

The [Search reference](https://docs.mireye.ai/api-reference/search) specifies a required state filter, a default page size of 10 and a maximum of 100. It returns first matches, so an agent must apply its own ranking.

- Continue while `next_cursor` is non-null, even after a short or empty page, unless the workflow's total budget requires a stop.
- Keep filters and requested fields unchanged across pages. Each new page needs a new operation key; retries reuse the original key and body.
- Read `remarks` for incomplete checks or unavailable fields. A final cursor alone does not erase a completeness warning.
- Allow at least 210 seconds at the client. Cursors expire after one hour, and pages are not a frozen snapshot.

`max_cost_usd` caps an individual operation. Our proposed controller also counts pages, accumulates receipts and stops at a total run budget. If it stops early, the report should say “best of the candidates examined” and record the stop reason. It should not claim to have found the best parcels nationwide.

### 3. Confirm identity before combining Search and Enrich

[Enrich resolves an address or coordinate to a parcel](https://docs.mireye.ai/api-reference/enrich); it does not simply accept the Search APN as a guaranteed join key. Search's stored `analysis_point` can fall outside the boundary, and a new spatial match can therefore identify a different property.

Follow the [documented identity check](https://docs.mireye.ai/guides/search-and-enrich): require a matched result and compare parcel number, address, state and county FIPS before merging evidence. Preserve uncertain matches for review. APNs can repeat across counties, and a plausible address is insufficient proof of identity.

Requesting selected values directly in Search avoids that second match. Use separate Enrich when you need its field-level source, confidence and interpretation hints. Request the identity fields needed for the comparison as well as the decision fields.

## What does a sourced parcel result actually prove?

Keep the endpoint's evidence structure intact. [Fetch distinguishes `ok`, `absent` and `failed`](https://docs.mireye.ai/api-reference/fetch), and exposes source URLs, retrieval times and dataset vintage where available. A recent `fetched_at` does not make an older underlying dataset current. Search returns plain values; standalone Enrich can omit unavailable fields and does not expose Fetch's full status envelope.

Our engineering rule is to preserve unknown, false and zero as different states. Add source links and dates only where the response or a checked source actually supplies them. Do not fabricate a citation URL from a source label.

![A teal sample point lies outside a purple hazard band, while the band overlaps part of the parcel boundary](https://wavect.io/img/blog/mireye-point-vs-parcel.svg)A point result cannot describe every part of a parcel. The purple band overlaps the parcel away from the teal sample point. Schematic illustration, not an actual flood map. Illustration: © 2026 Wavect.

The [field definitions](https://docs.mireye.ai/api-reference/enrich-fields) make spatial scope especially important: `within_floodplain_polygon` describes a point, while `parcel_flood_sfha_fraction` concerns the full boundary. A useful agent preserves that distinction in both its ranking and its prose.

| Observed signal | Reasonable next action |
| --- | --- |
| No mapped hazard at the sampled point | Check parcel-wide overlap and coverage before making a property-wide statement. |
| Missing zoning or ownership field | Request the relevant record; keep the decision unresolved. |
| Nearby substation or transmission line | Investigate the applicable utility, connection process and project requirements. |
| Recent retrieval of an older dataset | Keep both dates and ask whether the decision needs a newer record. |

FEMA's [flood-zone guidance](https://www.floodsmart.gov/flood-zones-and-maps/what-is-my-flood-zone) explains that lower-risk zones still carry risk and undetermined risk means analysis is missing. FERC's [generator-interconnection explainer](https://www.ferc.gov/explainer-interconnection-final-rule) distinguishes preliminary information from connection studies and network upgrades. Our inference for screening is straightforward: proximity alone cannot establish usable grid capacity.

## How should an agent rerank land for solar, storage or data centers?

**Filter clear requirements first, then rank eligible candidates with explicit preferences and a separate evidence-completeness assessment.** Do not reward missing evidence by treating it as a favorable zero.

Consider three hypothetical solar candidates. Parcel A meets the acreage brief and has reviewed hazard evidence. Parcel B looks attractive on terrain but lacks a parcel-wide flood result. Parcel C fails a confirmed acreage requirement. A can enter the evaluated shortlist, B belongs in a review queue, and C is excluded for the recorded reason. A single blended score would hide why those decisions differ.

Keep each shortlist row useful to the next person: parcel identity, evaluated location, satisfied requirements, preference score and its version, supporting sources, unavailable evidence, search-completeness notes and the next verification action. Store permitted evidence and follow the applicable data-use terms when sharing results.

Solar, battery storage and data centers should use different briefs. A developer should define the project-specific power, land, access and operational requirements before choosing fields or weights. Reusing a solar score for a data center merely because both need grid context gives the agent a misleading objective.

Mireye's own [Texas solar-carport demonstration](https://github.com/Mireye-Labs/solar-carport-siting/blob/main/README.md) illustrates why these checks matter. It searches Texas public parcel files with custom code and then uses Fetch batch calls; this is not a benchmark of the new Search and Enrich endpoints. The project describes screening 1,187 sites and discusses repeated Fetch passes, duplicate opportunities and an unvalidated scoring formula. Some missing ERCOT queue values reflect sites outside ERCOT territory. That is a reason to inspect applicability before penalizing a null. The project is a vendor demonstration, not a Wavect performance result.

## When is Mireye a useful fit?

Mireye is worth evaluating when an agent needs to discover U.S. parcels and assemble structured evidence for a human site-selection or acquisition process. Start with a bounded pilot in the counties and use cases that matter to you. Measure correct parcel matching, required-field availability, unresolved evidence, reviewed shortlist quality and total cost.

The [documented geographic scope](https://docs.mireye.ai/introduction) is U.S.-focused. The drive-time tools' additional Canadian coverage does not imply Canadian parcel coverage. A German, Spanish or Chinese user interface does not extend the parcel dataset to those countries.

For a production integration, connect the data contract, ranking rules, spending limits and review handoff. Wavect's [AI integration service](/services/artificial-intelligence/) covers the surrounding application work; our [enterprise MCP authorization guide](/blog/enterprise-mcp-authorization-architecture/) explains the broader access boundary.

## Frequently asked questions

### Is Mireye Fetch completely free?

Standard authenticated Fetch has no usage charge for eligible public location fields. Licensed parcel data is outside that free scope, and rate and capacity limits remain. The separate API-key-free x402 route costs $0.001 USDC per successful call.

### Does Mireye Search return the best land automatically?

Search returns the first matching parcels, rather than a ranked shortlist. Your application must define ranking preferences, preserve missing evidence and report whether it examined all available pages or stopped at its budget.

### Does the $10 signup credit renew every month?

No. The checked pricing page describes a one-time welcome balance without expiry. At Free-plan rates, $10 equals 50 Search pages or 500 chargeable Enrich operations if used entirely for one of those operations.

### Can every Mireye field be used as a Search filter?

No. Check the Search catalog's filter_ops and returnable attributes. Some fields can be returned but cannot be used as server-side filters. The launch-wide field count is not an API request schema.

### Can I use Mireye for European or Chinese land parcels?

The documented parcel product is U.S.-focused. Additional Canadian drive-time coverage does not establish international parcel coverage. Verify a separate local data source before building a workflow for another market.

### Can an AI land-search result replace due diligence?

It can organize a first screening and its supporting evidence. A shortlist still needs checks appropriate to the proposed project, including identity, source coverage and unresolved site constraints. A nearby power line or a missing hazard value does not establish that a project can be built.

Architecture and platforms

## Read next

Framework, platform and system-design choices that affect delivery over the long term.

[Start here**Smart City Architecture Best Practices: MQTT, LoRaWAN, Kubernetes and Terraform**](/blog/smart-city-architecture-best-practices-2026/)

- [git-ai Review: Concurrent Agent Merges and Verification](/blog/git-ai-concurrent-agents-merge-verification/)
- [PHP2Shell: Upgrade Legacy PHP or Migrate to Node.js?](/blog/php2shell-legacy-php-upgrade-migration/)
- [Enterprise Software: Adding a Specialist Alongside Your System Integrator](/blog/enterprise-software-specialist-system-integrator/)
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[Next article**git-ai Review: Concurrent Agent Merges and Verification**](/blog/git-ai-concurrent-agents-merge-verification/)

[**Back**](/blog/overview/)

[![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/)

[Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt

11 min read · 23 Oct 2026 Last reviewed October 23, 2026

[**Next**](/blog/ai-agent-cost-per-action-2026/)