compliance_policies
ChatGPTList APPROVED regulatory compliance policies — rules AI-extracted from a regulation (for example EU Green Bonds, CSRD/ESRS) and approved by a dVeracity admin. This is the regulation-derived corpus; the Open Footprint sector guardrails from ofp_policies cite no regulation by design. Paid plans only (VaaS or above): a free account gets an actionable "plan required" error, not data. Requires the compliance:read scope — a connector authorised before that scope existed must be re-authorised. Returns name, framework, domain, effective dates, the source regulation document, rule count and approval time per policy. Drafts and rejected policies are never returned. An empty list is a real answer (published:false with a reason): treat it as "none published", never as "no obligations exist", and do not invent rules to fill the gap.
compliance_policy
ChatGPTOne APPROVED regulatory compliance policy in full: its Rego source, the structured rules the extractor produced, the record types and input fields the Rego reads, and provenance (source regulation document, extraction model and confidence, approval time, policy hash). Paid plans only; requires the compliance:read scope. A draft id — or a row that never went through approval, such as a retired seeded stub — answers "not found" exactly like an unknown id. A policy approved before its source was retained is returned without Rego and says so (rego_unavailable: source_not_retained). Such a policy is NOT loaded into the policy engine; the response says in enforcement whether its structured validation rules are still evaluated. Do not reconstruct the Rego yourself.
credits_balance
ChatGPTShow the remaining dVeracity API credit balance for this account (free).
keri_identity
ChatGPTShow this agent's verified vLEI identity (agent AID, Legal Entity LEI, authorized scopes) if KERI mode is configured — free. In KERI mode every metered call is attributed to this identity in dVeracity's audit trail.
list_standards
ChatGPTList the sustainability data standards dVeracity can validate against (free).
ofp_entity
ChatGPTGet one canonical entity in full (free): fields with types and required flags, primary key, relationships, and the physical database table implementing it. Pass domain whenever you know it — many entity names are defined in more than one domain (Country is in four; the ofp_models summary reports the current count as ambiguousNameCount), and without it the call fails rather than guessing which one you meant.
ofp_model_provenance
ChatGPTShow which snapshot of the Open Footprint standard this deployment serves (free): source commit, date, and entity/sector counts. Use it when you need to know how current the model you are designing against is.
ofp_models
ChatGPTList the Open Footprint canonical model domains and which database dialects have published DDL (free). Start here when building an app on the Open Footprint standard: it tells you what the model covers before you look up individual entities. The response summary carries the served counts (domains, entities, fields); they follow the vendored snapshot, which ofp_model_provenance identifies.
ofp_policies
ChatGPTGet a sector's policy guardrails as Rego source (free), with policies[].inputs listing the record types and input fields each policy reads — e-ledger fields such as co2e_kg and direction, not canonical Open Footprint field names; a payload without them is reported unevaluable by ofp_validate. When a sector has no published guardrails this returns published:false with a reason — that is a real answer, not an error. Treat it as "no rules are published", never as "there are no constraints", and do not invent guardrails to fill the gap.
ofp_search_entities
ChatGPTSearch the canonical Open Footprint entities by concept (free). Matches entity names, descriptions and field names. Use this to find the right entity before fetching its full definition with ofp_entity.
ofp_sector
ChatGPTGet one sector in full (free): its classification, status, reference artifacts and nested industries. Use ofp_sectors first to find the sector id.
ofp_sectors
ChatGPTList industry sectors and their reference implementations (free). Each carries a status: "implemented" means a reference implementation exists, while "planned" or "placeholder" mean the sector is named in the taxonomy but nothing is published for it. Check the status before assuming a sector has content.
ofp_semantic_code
ChatGPTShow which canonical fields carry one O-DEF semantic code (free). This is the reverse lookup a connector needs: given a field in a source system, find where in the Open Footprint model it belongs. Codes carried by more than ten fields are returned with an explicit caution — they are generic classifications, and aligning to one asserts far less than it appears to.
ofp_semantics
ChatGPTList the O-DEF semantic codes carried by the canonical model (free). Use this when mapping fields from another system — SAP, an ERP, a supplier feed — onto Open Footprint: a code is the shared address two systems can align on. Every code comes with fieldCount, the number of canonical fields sharing it. A high count means a generic fallback that asserts almost nothing, so pass maxFieldCount (10 is a good start) to get only codes specific enough to be a real alignment target.
ofp_validate
ChatGPTCheck a data payload against the canonical Open Footprint model and, when a sector is named, that sector's published Rego guardrails. Costs API credits per call. WHAT IT CHECKS: structure (required fields, primary key, declared types); the constraints the model declares (identifier patterns, enums, date formats, lengths); value ranges where the model declares a bound (a quantity outside the declared minimum or maximum is rejected; one field in the model declares a bound today, so this check runs only there — read checked rather than assuming it ran); temporal consistency (a validity period whose end precedes its start is rejected); enum membership (a foreign key into a vocabulary that publishes its members is checked against that member set, so a well-formed identifier for a referent that does not exist is caught); and, with a sector, the guardrails. WHAT IT DOES NOT CHECK in this release: value ranges on fields whose model declares no bound (an out-of-range quantity passes, reported per call as no_range_declared or no_numeric_fields), unit coherence, referential integrity (foreign keys are pattern-checked and, where a vocabulary publishes one, member-checked, but never resolved against live records), factor provenance, or materiality. The response says so per call: checked lists the checks that ran and checks_not_run lists every catalogued check that did not, each with a reason (not_implemented, no_data_plane, no_range_declared, no_numeric_fields, no_validity_pair, no_members_published, no_reference_field_supplied, schema_not_run, no_sector_supplied, no_policy_published, not_applicable, unevaluable). schemaValid is the structural verdict; assuranceLevel (schema-only | schema-and-value | schema-value-and-policy | full) is the depth earned. valid is deprecated (kept through 0.5.x, removed no earlier than 0.6.0) — it only ever covered the checks that ran. Every violation and warning carries severity (error | warning). Unknown fields are warnings, never violations, and each carries didYouMean candidates when the model has a plausible match; schema.normalisations discloses keys that resolved only through case and separator folding. advisories carries findings that are true of the payload but do not bear on its validity: today deprecated_field, severity warning, naming a successor when the model names one — an advisory never changes schemaValid. Guardrails report four distinct outcomes, never conflated: passed, denied, not_applicable (the rules do not cover this record type), or unevaluable (the payload lacks the e-ledger inputs the rules read, e.g. co2e_kg — listed in policy.missingInputs). Never report a payload as compliant unless sector_policy is in checked and policy.outcome is passed.
semantic_query
ChatGPTAsk a natural-language question about verified emissions data (Open Footprint / PPDM / OGMP-methane knowledge graphs). Costs API credits per call; a payment-required error means the human operator must top up credits. Prefer specific questions (a site, a company, a time range) over broad ones.
semantic_templates
ChatGPTList the query templates the semantic layer supports (free). Useful to learn what kinds of questions the knowledge graph can answer before spending credits on semantic_query.
validate_data
ChatGPTValidate a data payload against a supported sustainability standard (Validation-as-a-Service). Costs API credits per call. Use list_standards first to see supported standards and versions.
credits_balance
Claudekeri_identity
Claudelist_standards
Claudeofp_entity
Claudeofp_model_provenance
Claudeofp_models
Claudeofp_policies
Claudeofp_search_entities
Claudeofp_sector
Claudeofp_sectors
Claudeofp_semantic_code
Claudeofp_semantics
Claudeofp_validate
Claudesemantic_query
Claudesemantic_templates
Claudevalidate_data
Claude
