About dVeracity Semantic API
dVeracity Semantic API is an MCP server for agents working with emissions data. It provides natural-language access to a verified-emissions knowledge graph, Open Footprint model tools, and standards validation.
Key features
- 01Query verified-emissions data in natural language
- 02Browse Open Footprint entities and sectors
- 03Validate data against models and standards
- 04Inspect query templates and credit balance
- 05Optional KERI-verified agent identity
Use cases
- 01Ask questions about emissions data
- 02Check payload structure and published sector rules
- 03Explore Open Footprint fields when building an application
MCP support
The server exposes semantic queries, model exploration, validation, credit and standards tools to MCP clients. Optional KERI mode attaches a verified agent identity to API calls.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://api.dveracity.com/mcpclaude mcp add --transport http com-dveracity-semantic https://api.dveracity.com/mcp{"mcpServers":{"com-dveracity-semantic":{"type":"http","url":"https://api.dveracity.com/mcp"}}}Open in Cursor{"servers":{"com-dveracity-semantic":{"type":"http","url":"https://api.dveracity.com/mcp"}}}Open in VS Code{"mcpServers":{"com-dveracity-semantic":{"type":"http","url":"https://api.dveracity.com/mcp"}}}{"mcpServers":{"com-dveracity-semantic":{"serverUrl":"https://api.dveracity.com/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEdVeracity Semantic MCP server
Gives any MCP-capable AI agent (Claude Code, Cursor, custom agents) metered access to the dVeracity Semantic API — natural-language queries over the verified-emissions knowledge graph (Open Footprint / PPDM / OGMP-methane) — and VaaS standards validation.
Prerequisites
- An api-tier subscription: https://dveracity.com/pricing
- An API key (
dvrc_…):POST /api/v1/api-keys(or the dashboard) - API credits for metered calls:
POST /api/v1/vaas/credits/purchase
The machine-readable service contract lives at GET /api/v1/semantic/manifest
(public, no auth).
Install
From this directory: npm install
Claude Code
claude mcp add dveracity \
-e DVERACITY_API_KEY=dvrc_yourkey \
-- node /path/to/dVE/mcp/semantic-mcp/index.js
Generic MCP JSON config (Cursor, etc.)
{
"mcpServers": {
"dveracity": {
"command": "node",
"args": ["/path/to/dVE/mcp/semantic-mcp/index.js"],
"env": { "DVERACITY_API_KEY": "dvrc_yourkey" }
}
}
}
Optional: DVERACITY_API_URL overrides the API base URL (defaults to prod).
KERI mode — verified agent identity (optional)
If the agent holds a dVeracity Agent Authorization credential (an ACDC issued
by its Legal Entity, chained to the Legal Entity's vLEI — see
elm/docs/VLEI_AGENT_TOKENS_DESIGN.md), set:
DVERACITY_KERI_AID=<the agent's AID (credential issuee)>
DVERACITY_KERI_PRESENTATION=/path/to/agent-credential.cesr # self-contained CESR
The server then authenticates the agent by verifiable presentation
(challenge → exchange → 1-hour session, refreshed transparently) and attaches
X-Keri-Session to every call: the API key keeps carrying billing, the KERI
session adds verified identity — every metered call is attributed to the
agent AID and Legal Entity LEI in dVeracity's audit trail. The keri_identity
tool (free) shows the active identity. Scope denials (a credential that doesn't
carry e.g. semantic:query) surface as actionable errors naming the carried
scopes. Signify-based nonce signing is a planned enhancement.
Tools
| Tool | Cost | What it does |
|---|---|---|
semantic_query | credits | Natural-language question over the verified-emissions knowledge graph |
semantic_templates | free | Catalog of supported query templates |
credits_balance | free | Remaining credit balance |
list_standards | free | Standards VaaS can validate against |
validate_data | credits | Validate a payload against a supported standard |
keri_identity | free | This agent's verified vLEI identity, when KERI mode is configured |
Open Footprint canonical model
Design-time tools for building an application on the Open Footprint standard. Reading the model is free; only the check at the end is metered.
| Tool | Cost | What it does |
|---|---|---|
ofp_models | free | The model domains (nine as of openfootprint b60a24e), and which database dialects have published DDL |
ofp_search_entities | free | Search the canonical entities (262 entries as of b60a24e) by name, description or field |
ofp_entity | free | One entity in full: fields, types, keys, relationships, physical table |
ofp_sectors | free | Industry sectors, each with a status |
ofp_sector | free | One sector, with its reference artifacts |
ofp_policies | free | A sector's Rego guardrails, or an explicit "none published" |
compliance_policies | paid plan | Approved regulatory compliance policies (rules extracted from a regulation, admin-approved); never drafts |
compliance_policy | paid plan | One approved policy: Rego, extracted rules, inputs, provenance |
ofp_validate | credits | Check a payload against the model and, optionally, sector guardrails |
ofp_semantics | free | O-DEF semantic codes, for aligning another system's fields onto the model |
ofp_semantic_code | free | Which canonical fields carry one code — the reverse lookup a connector needs |
ofp_model_provenance | free | Which snapshot of the standard this deployment serves |
Two behaviours are deliberate and worth knowing before you build against them.
Ambiguous entity names fail rather than resolve. dozens of entity names (the ofp_models summary reports the count as ambiguousNameCount)
are defined in more than one domain — Country is in four. ofp_entity without
a domain returns an error listing the candidates instead of picking one. Pass
domain whenever you know it.
Semantic codes vary in usefulness. Most canonical fields carry an O-DEF code,
but a code shared by many fields is a generic classification, and aligning to one
asserts almost nothing. The ofp_models summary reports the real shape under
precision — preciseFieldThreshold (the fieldCount at or below which a code
is precise rather than generic), fieldsOnPreciseCodes and
preciseShareOfAnnotated — and those follow the vendored snapshot rather than
this document. Every code is returned with its fieldCount: check it before
aligning to one, and pass maxFieldCount at that threshold to ofp_semantics to
see only the precise ones.
"Nothing published" is an answer, not an error. Most sectors are named in the
taxonomy but have no reference implementation, and only seven publish policy
guardrails. ofp_policies on such a sector returns published: false with a
reason, and ofp_validate reports policy.ran: false. Both mean no rules are
published, never there are no constraints — a payload checked for structure
alone is not a compliant one, and should not be described as one.
A fourth outcome, unevaluable, means the sector's rules ran but every rule that came back false reads an input the payload does not carry (policy.missingInputs, e.g. co2e_kg, direction, counterparty_industry). Those are e-ledger record fields, not canonical Open Footprint field names — ofp_policies lists them per policy under inputs. Unevaluable is neither a pass nor a breach, and valid is null.
What ofp_validate checks, and what it does not
The response is a contract, not a verdict. Every call reports the check
catalogue in two lists: checked (what ran) and checks_not_run (what did not,
each with a reason and usually a detail). Read both before describing a
payload as anything.
| Check | Status | Reason reported when it does not run |
|---|---|---|
schema — presence, primary key, types, declared constraints | runs | entity_has_no_fields |
value_range | runs where the model declares a bound: a quantity outside the declared minimum or maximum is rejected. Which fields declare a bound moves with the vendored snapshot, so it fires on some entities and not others — read checked and schema.coverage.constraintsDeclared per call | no_range_declared, no_numeric_fields |
unit_coherence | not run | not_implemented |
calculation_reproducibility | runs where the record carries all three of a quantity, an emission factor value and a stated CO2e result: quantity × factor = result is reproduced within 1%, and a record that contradicts itself is rejected | no_calculation_inputs, calculation_inputs_incomplete, ambiguous_calculation_inputs, calculation_inputs_unparseable |
temporal_consistency | runs: a validity period whose end precedes its start is rejected | no_validity_pair, validity_pair_incomplete, validity_values_unparseable |
enum_membership | runs where the vocabulary publishes its members: a well-formed key for a referent that does not exist is rejected | no_members_published, no_reference_field_supplied, reference_ids_unparseable |
referential_integrity | not run: keys are pattern- and member-checked, never resolved against live records | no_data_plane |
factor_provenance | not run | not_implemented |
materiality | not run | not_implemented |
sector_policy | runs when a sector with published guardrails covers the record type | no_sector_supplied, no_policy_published, policy_source_missing, policy_package_unparseable, not_applicable, unevaluable |
Any check can also report schema_not_run, which means the structural check it
builds on could not run at all.
calculation_reproducibility is self-referential and nothing more. It resolves
its three inputs from the model — the field carrying the emission-measure role, a
… Factor Value (never a … Factor ID, which is a reference to a factor rather
than a number to multiply by) and a … CO2e … result — or, for a record in the
vocabulary the guardrails read, from quantity_value / emission_factor_value /
co2e_kg. A slot the record fills more than once is ambiguous_calculation_inputs
and the check declines rather than picks. The 1% tolerance is the one
CalculationAccuracy in the verification engine and calc_tolerance in the
ISO 14064 quantification policy use, so the identity has one tolerance on every
surface. What it catches is a record whose own numbers do not multiply out. It
does not say the factor is the right one for this source and period
(factor_provenance, still not run) or that the quantity came from the meter it
claims (the data plane, still not run) — a reproduced calculation is a
self-consistent record, not a verified one.
schemaValidis the structural verdict (nullif the structural check could not run).assuranceLevelnames the depth earned:schema-only,schema-and-value,schema-value-and-policyorfull. Each level needs every check beneath it, so guardrails without a value check is stillschema-only. The ladder is gated onvalue_range— a bound the model itself declares — so a record that reproduced its own arithmetic without one still readsschema-only;checkedis where that shows up, and it is the more precise of the two.- Every violation and warning carries
severity(error|warning). Rule ids are stable:required_field_missing,pattern_mismatch,format_mismatch,type_mismatch,primary_key_missing,enum_violation,value_range(withbound:minimum|maximum),temporal_consistency,enum_membership,calculation_reproducibility(withexpected,received,toleranceand the threeinputsit read), …unknown_fieldis a per-field warning and stays one. unknown_fieldwarnings carrydidYouMean: up to three canonical candidates, each with a confidence and the O-DEF code that field carries. The list is empty when nothing in the model is a plausible match — a key that belongs to another system stays a key that belongs to another system.schema.normalisationsseparately discloses keys that resolved through case and separator folding.advisoriescarries findings that are true of the payload but do not bear on its validity. Today that isdeprecated_field: a field the model has retired, severitywarning, carryingmodelDescriptionverbatim and asuccessorwhen the model names one. Where the model names no replacement the key is absent rather than guessed. An advisory never changesschemaValid, and the same entries also appear inschema.warnings.validis deprecated (seedeprecationsin the response). It keeps its 0.5.0 meaning through the 0.5.x line and is removed no earlier than 0.6.0. ReadschemaValidinstead.
Billing behavior (for agents)
Metered calls return an HTTP 402 when the account is out of credits. The server surfaces this as a tool error that tells the agent to ask its human operator to purchase credits or upgrade — agents should relay that message and stop, not retry.
Test
npm test (no network; the HTTP layer is stubbed).
Where it is listed
- Smithery — one-click add for Smithery toolbox users
- Official MCP Registry —
com.dveracity/semantic-mcp - npm —
@dveracity/semantic-mcp
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.