Your first examination

Start one examination of a prepared case, keep its job ID, and collect the reports for examiner review.

This tutorial uses fixture sourcing: the examiner retrieves records from the server’s prepared case folder. Browser sourcing and purchases stay off. Extraction and analysis still consume model credits.

Prerequisites

• Complete Connect your agent and its read-only connection check.
• Agree the case, starting scope, and model spend with the operator.
• Arrange private access to the source scans for review. The MCP tools return report text, not downloadable PDFs.
• Keep private case notes where you can save the job ID.

Historical planning estimates are roughly $150 for a standard run and $300–450 for some live open-loop deals. These are not quotes or hard spending limits. Confirm the expected spend with the operator before you start.

1. Choose a case and scope

Call list_surveys and choose the exact returned survey value. An examination starts from records on the server, rather than a filename or survey name guessed from these examples.

For a first test, use the prepared folder. The hosted service uses bfs, which walks the guarded sourcing worklist. The experimental local agent driver is unavailable over hosted HTTP.

Check before continuing: you have an exact registered survey, an agreed scope, and permission for the model spend.

2. Start once and save the job ID

After that agreement, ask your agent:

Start one AI Title Examiner job for <exact survey returned by list_surveys>.
Use provider fixture, driver bfs, max_docs 50, validate false,
allow_browser false, and allow_purchase false. Leave seed_ref empty.
Save the returned job_id in our case notes. If the connection slows or I
reconnect, check this job instead of starting another one.

The equivalent start_title_exam arguments are below. Replace the survey placeholder before use:

{
  "survey_name": "<exact survey returned by list_surveys>",
  "provider": "fixture",
  "driver": "bfs",
  "max_docs": 50,
  "validate": false,
  "allow_browser": false,
  "allow_purchase": false
}

The response includes a generated job ID, for example:

{
  "job_id": "<generated UUID>",
  "state": "queued",
  "status_tool": "get_title_exam_status",
  "results_tool": "get_title_exam_results"
}

Save the UUID in your private notes. The worker runs independently of the client connection; closing your client does not stop it. The service has no job-list or cancellation tool.

The default deployment admits one queued or running examination. A concurrency rejection means that slot is occupied; the rejected request does not enter a waiting queue.

3. Check the existing job

Call get_title_exam_status with your saved UUID:

{
  "job_id": "<saved UUID>"
}

Check every 30–60 seconds while the job is active. Continue using that same ID after reconnecting.

Status does not expose a reliable percentage, current analysis phase, or actual spend. docs_read may be empty during work. Ask your agent to report the returned state, rather than infer progress.

4. Read the summary before reports

Call get_title_exam_results with artifact set to summary:

{
  "job_id": "<saved UUID>",
  "artifact": "summary"
}

Check summary.reports_ready. When it is true, the current LRS and LOR exist for the final sourced set, and other report reads are allowed. Reports may be ready even when coverage remains incomplete.

Also check coverage_complete, coverage_limited, analysis_complete, qa_review_failed, and review_flags. Use Read and review the results to interpret those fields and sourcing termination reasons. A complete state indicates the service’s criteria are met, not independent legal approval.

5. Collect the review package

Ask:

For job <saved UUID>, read the summary first. If reports_ready is true,
retrieve lrs, lor, buying, review_sheet, and faithfulness. Retrieve validation
only if the summary shows it is available. Identify missing or truncated
outputs. Keep coverage and analysis warnings alongside the reports.

Each artifact read uses the same ID and one artifact name:

{
  "job_id": "<saved UUID>",
  "artifact": "lrs"
}

Artifact text stops at 100,000 bytes. A [truncated] marker means the response is incomplete. Ask the operator for the full private file before judging the entire report.

6. Review the evidence and record feedback

Compare reported facts with the scans before accepting ownership or acreage. Check subject-tract acreage, ambiguous filing dates, name continuity, source citations, and cursive records. Review probate shares, community property, life estates, and mineral-versus-royalty reservations.

Use the result review checklist and accuracy limits. A mineral fee total of one proves an arithmetic invariant, not that the correct chain was supplied. Active held-by-production (HBP) lease status and lien/release searches require external examiner work.

For each issue, record the job ID, artifact, row or Vol/Pg, reported value, source evidence, and practical consequence. Send feedback privately to the operator using Pilot testing and feedback.

Correct/Notes is a review deliverable. The service has no correction-saving or upload tool. The operator handles revised records and any rerun; starting again creates a new billable job.

You have finished this tutorial when: you saved one job ID, checked its final state, and recorded evidence-backed findings. If reports are available, collect them in full before reviewing them. If the job is incomplete or failed, preserve its blocker and job ID for the operator.

If a step fails

Use Troubleshooting for connection, concurrency, job-state, or missing-report problems. Keep the existing job ID while investigating. A slow response is not authorization to start a second examination.

Next step

Use Prompts and worked examples for a one-instrument trace or focused review. Discuss external sourcing with the operator before enabling a web provider or purchases.