Machine-Readable Integration Guide for AI and Crawlers
This guide shows how an automated system or developer can discover GoGuides endpoints, inspect a domain trust record, retrieve verified source text, and independently test the SHA-256 integrity value of a complete record.
The examples are public and crawlable. They demonstrate actual endpoint behavior without implying endorsement by any crawler, search engine, model provider, or AI company.
GoGuides Machine-Readable Endpoint Overview
| Purpose | Endpoint | What to inspect |
|---|---|---|
| Endpoint discovery | /machine-use.json |
Service purpose, public endpoints, crawler guidance, and canonical documentation. |
| Live public signal sample | /signal.json |
A current machine-readable trust signal record. The represented domain may change. |
| Specific domain evaluation | /evaluate.php?domain=goguides.com&format=json |
Matched domain, policy status, current trust state, freshness, observation history, and machine-use fields. |
| Verified-text topic lookup | /verified-text.php?q=gravity&format=json |
Candidate records, source keys, titles, sections, previews, chunk IDs, and direct-record URLs. |
| Complete verified-text record | BOXING record JSON | Full normalized text, stored SHA-256 value, computed integrity result, source metadata, and licensing information. |
Workflow 1: Discover the Public Machine Endpoints
Fetch the discovery document
Begin with the public JSON document that describes GoGuides machine-use resources.
Read the endpoint and guidance fields
Inspect endpoints, crawler_guidance, canonical_documentation, and the declared service purpose.
Choose the endpoint matching the task
Use domain evaluation for a specific website, or Verified Text for source-attributed text retrieval.
curl -sS \ 'https://www.goguides.com/machine-use.json'
Workflow 2: Inspect a Specific Domain Trust Record
Use the evaluation endpoint when the task concerns a named domain. Unlike the general /signal.json sample, this request explicitly selects the domain being evaluated.
curl -sS \ 'https://www.goguides.com/evaluate.php?domain=goguides.com&format=json'
Identity fields
Check matched_domain, matched_url, and lookup_type.
Current state
Inspect status, policy_status, gate, freshness, and current trust-state timing.
Observation evidence
Review first and last observation dates, observation count, span, and crawl status.
Machine guidance
Use the returned machine_use fields as GoGuides guidance—not as proof of external adoption or endorsement.
Workflow 3: Find and Verify a Text Record
Search for a topic
Request a topic such as gravity and inspect the candidate results.
Select the correct record
Compare title, section, source_key, chunk_id, preview text, and license fields. Do not choose only by the first matching word.
Follow the direct record URL
The topic response contains record and JSON URLs. Use the direct JSON response to obtain the complete normalized text.
Recompute the SHA-256 value
Hash the complete verified_text value and compare it with hash_sha256.
Preserve attribution and license details
Store the source key, source title, chunk ID, canonical record URL, and any required attribution with the retrieved text.
Topic request
curl -sS \ 'https://www.goguides.com/verified-text.php?q=gravity&format=json'
Complete direct record request
curl -sS \ 'https://www.goguides.com/verified-text.php?source_key=britannica_1926&chunk_id=1926%3Ag%20t%20b%20boxing%3Aa37e28623831&format=json'
Independent Python hash check
import hashlib
import json
from urllib.request import urlopen
url = (
"https://www.goguides.com/verified-text.php"
"?source_key=britannica_1926"
"&chunk_id=1926%3Ag%20t%20b%20boxing%3Aa37e28623831"
"&format=json"
)
with urlopen(url) as response:
record = json.load(response)
computed_sha256 = hashlib.sha256(
record["verified_text"].encode("utf-8")
).hexdigest()
assert computed_sha256 == record["hash_sha256"]
assert record["integrity"]["hash_check"] == "match"
verified_text_preview. The preview is useful for record selection, but it is not the complete text covered by the full-record SHA-256 value.
Purposeful Verified-Text Test Queries
These examples cover different retrieval conditions without repeating every topic in both long lists.
Basic cross-source topic
Returns records from Britannica and Open English WordNet. Compare sources before selecting a record.
Historical scientific concept
Useful for checking source context and historical terminology.
Highly ambiguous word
Returns many lexical senses. The first result should not be assumed to match the intended meaning.
Multiple meanings
Demonstrates why section, preview, and source metadata matter.
Mixed historical and lexical records
Includes Britannica 1926 and Open English WordNet results.
Large sense set
Shows why automated systems need deliberate sense selection rather than word-only matching.
Response Handling and Troubleshooting
| Condition | What it means | Recommended action |
|---|---|---|
HTTP 200 with status: ok |
The endpoint returned a usable response. | Inspect the fields appropriate to the endpoint and preserve source metadata. |
| Multiple topic results | The topic has several sources, senses, sections, or records. | Select using meaning and source context—not result position alone. |
HTTP 404 or status: not_found |
No matching record was available for the supplied query or identifier. | Check spelling, use a simpler topic, or verify the exact source key and chunk ID. |
integrity.hash_check: mismatch |
The stored and newly computed text hashes differ. | Do not treat the record as integrity-verified. Retry later or preserve the mismatch for audit. |
| Preview shorter than full record | The topic response intentionally limits visible text. | Follow the direct record URL before hashing, citing, or storing the complete record. |
What These Machine Signals Do Not Prove
- A SHA-256 match proves text integrity against the stored normalized record; it does not prove that every statement in the source is current or factually correct.
- A trust or evaluation response is a GoGuides measurement and policy result. It does not prove that another crawler, model provider, or search engine accepts or uses it.
- Source-grounded retrieval can improve auditability and reduce unsupported source selection, but it cannot guarantee that an AI system will interpret or use the record correctly.
- Historical material may contain outdated language, facts, assumptions, or viewpoints.
- Licensed records must retain the attribution and license information returned with the record.