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AI Title Examiner

Use your AI agent to examine Texas oil and gas records, then review the source evidence behind each proposed owner and title issue.

AI Title Examiner reads a prepared packet of deeds, leases, patents, and probate records. It follows cited predecessor records and produces a chronological runsheet, a proposed ownership opinion, and reports for examiner review.

You use the examiner through an AI agent with Model Context Protocol (MCP) support. Desktop apps, editors, CLI agents, and hosted applications can use the same tools when their MCP integration supports the connection. The examination runs on the owner’s server; your client does not need model API keys or a copy of the engine.

Choose your starting point

You want toStart with
Connect an AI agentConnect your agent
Connect a client that launches local stdio serversLocal bridge setup for Windows, macOS, and Linux
Run a prepared caseRun your first examination
Review an existing jobReview examination results
Check a tool argument or responseMCP tool reference
Integrate or change the engineDevelop and verify changes

For an agent that can fetch documentation, provide /llms.txt. It links to the same guides as this book. /llms-full.txt contains every chapter.

From records to a review package

  1. The project owner prepares a case’s PDF records on the server.
  2. You select an exact survey name returned by list_surveys.
  3. After agreeing on the case and spend, your agent starts one job and saves its job_id.
  4. Your agent checks that job’s status and retrieves the available reports.
  5. You compare the reports with the original records and return corrections with evidence.
flowchart LR
    accTitle: Examination workflow
    accDescr: Choose a survey, start one job, save its ID, poll until it finishes, then retrieve available reports and review the records. Send failed jobs to the operator.
    A["Choose survey"] --> B["Start once<br/>Save job ID"]
    B --> C["Poll saved ID"]
    C -->|Active| C
    C -->|Finished| D["Read summary<br/>and reports"]
    D --> E["Review records"]
    C -->|Failed| F["Send error<br/>to operator"]

The job continues if your agent disconnects. Reconnect and use the saved ID. A new start creates another examination rather than resuming the first one.

What you receive

ReportYour review task
Lease Run Sheet (LRS)Check instrument order, parties, dates, acreage, and record references.
Letter of Opinion on Title (LOR)Trace proposed interests to conveyances and inspect title notes and curative requirements.
Buying summaryCheck net mineral acres, royalty burdens, and unresolved conditions.
Review sheetRecord corrections beside the relevant runsheet rows.
Faithfulness reportInspect extracted values that lack support in the available OCR text.
Validation report, when requestedReview an additional advisory audit.

See Review examination results for fictional reports and a worked ownership example.

Before you start

Access: the owner supplies a private HTTPS endpoint and a shared pilot key. The key permits access to registered cases and billable examinations. Keep it in your client’s protected configuration.

Cost: listing surveys and the two structured arithmetic tools do not invoke title models. A full examination uses paid model credits, even when it reads local PDFs. Agree on the case and spend before starting.

Documents: the owner prepares new case packets. The MCP interface has no upload tool. Arrange access to original scans separately so you can review the reports.

Review: complete describes the workflow’s completion criteria. It does not establish legal title or approve a mineral purchase. Accuracy and known limits explains the measured weaknesses and review requirements.

The public diagrams and report downloads use fictional cases. Keep actual records and owner details in the agreed private review channel.