MCP serving¶

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How-to guide — This page gives steps to serve your runtime over MCP, with the API reference for details and Ontology design for what to expose.

MCP is a serving surface over the same ontology runtime, not a second authorization model. The server exposes introspection, guarded reads, actions, and Functions using the consumer and providers supplied at construction. The underlying ontology handlers are bound once; a request does not register a fresh copy of the domain.

Quickstart step: serve one consumer¶

For a single-agent or local deployment, build a server with the operator-selected Consumer:

from ontary import build_mcp_server

server = build_mcp_server(ontology, store, consumer)
server.run()  # stdio is the default transport

Alternatively, serve the ontology from the command line:

ontary serve your_app.ontology:ontology --dev --store ./dev.sqlite --port 8000

The dev server binds to 127.0.0.1 on port 8000. It requires the --dev flag. The dev consumer sees unscoped rows only, unless the ontology declares matching dev scopes.

This is the short form used by the README quickstart. The agent sees the same scope, sensitivity, row-visibility, and min-N decisions as a direct OntologyClient. Use this shape when one process intentionally represents one asserted identity.

Tools, bounded queries, and safety annotations¶

The twelve tools are registered with these MCP safety hints:

Tools	Annotation
`list_object_types`, `list_link_types`, `list_action_types`, `list_functions`	`readOnlyHint=True`
`get_declarations`, single-object reads, `query_objects`, `count_objects`, `traverse_links`, `call_function`	`readOnlyHint=True`
`aggregate_objects`	`readOnlyHint=True`
`execute_action`	`destructiveHint=True`

list_functions publishes each Function's declared parameters alongside its descriptions. Typed Functions list each parameter's name, type, choices, structured fields, required status, and referenced ontology type. A Function with no inputs publishes parameters: []. It never publishes parameters: null.

query_objects accepts where, order_by, limit, and after. Its where grammar uses bare scalars for equality and gt/gte/lt/lte/in/ne/contains operator mappings, validated against declared property types; several operators in one mapping are AND-ed, so {"gte": a, "lt": b} is a range. Unknown operators return UNKNOWN_OPERATOR; incompatible operators or operands return OPERATOR_TYPE_MISMATCH; unknown keys return the existing UNKNOWN_FIELD error. The MCP server applies a default limit of 100 and refuses an explicit limit above the hard maximum of 1000 with INVALID_LIMIT. count_objects accepts the same where grammar and returns the number of rows visible to the invoking consumer. It is not min-N-gated: it reveals only what query_objects already lists. For a min-N-released count, use aggregate_objects(func="count"), which needs no value_field.

count_objects and aggregate_objects are unbounded operations on this untrusted surface: each can scan the full underlying type to apply its matching, scope, row-visibility, and (for aggregation) release gates. They have no cap, so callers should treat them as potentially expensive on large types; query_objects caps the rows it returns, but an order_by page sorts the whole type and costs the same as count_objects.

The successful response preserves the existing rows under result and adds an opaque next_cursor alongside them:

{
  "result": [{"payload": {"id": "book-1"}, "lineage": {}}],
  "next_cursor": "opaque-page-token"
}

Pass that cursor back with the same explicit limit to fetch the next page. A null cursor means the visible result set is exhausted. after without an explicit limit returns AFTER_WITHOUT_LIMIT, and limit < 1 continues to use the underlying INVALID_LIMIT validation.

traverse_links is intentionally not paged here: its underlying OntologyClient.traverse/GuardedQuery.traverse API returns an unpaged list and does not expose limit, after, or a cursor to delegate. Adding a cap to that tool would require a new core traversal surface rather than a trivial MCP adaptation.

Serve many proven identities¶

build_multi_consumer_mcp_server is the HTTP shape for a process that serves more than one identity. It does not take a fixed consumer. Instead, each invocation receives the MCP transport's already-verified AccessToken, calls your resolve_consumer callback, and creates a cheap consumer view over the shared runtime.

Here is a complete server you can run as it stands. It declares a one-type ontology, seeds one ticket, and serves it to one authenticated agent:

from mcp.server.auth.provider import AccessToken
from mcp.server.auth.settings import AuthSettings
from pydantic import AnyHttpUrl

from ontary import (
    Consumer, DirectProperty, ObjectStore, Ontology, OntologyObject, Source, prop,
)
from ontary.mcp_server import build_multi_consumer_mcp_server

ontology = Ontology(name="support", scope_levels=["queue"])


@ontology.object(
    layer="L0", scope=[DirectProperty(level="queue", property_name="queue_id")]
)
class Ticket(OntologyObject):
    id: str = prop(primary_key=True)
    subject: str
    queue_id: str = prop(scope_level="queue")


ontology.validate()
store = ObjectStore(ontology.registry)  # SQLite, in memory
store.insert(
    "Ticket",
    {"id": "ticket-1", "subject": "Invoice mismatch", "queue_id": "billing"},
    Source(source_system="demo"),
)


class StaticTokenVerifier:
    """A stand-in for your identity provider's verifier: a fixed token map."""

    def __init__(self, tokens: dict[str, AccessToken]) -> None:
        self._tokens = tokens

    async def verify_token(self, token: str) -> AccessToken | None:
        return self._tokens.get(token)  # None = not authenticated (HTTP 401)


verifier = StaticTokenVerifier({
    "token-alice": AccessToken(
        token="token-alice", client_id="support-agent", scopes=[], subject="alice"
    ),
})

consumers = {
    "alice": Consumer(
        actor_id="alice", role="Agent", scope_level="queue", scope_id="billing",
        kind="human",
    ),
}


def resolve_consumer(token: AccessToken) -> Consumer | None:
    return consumers.get(token.subject or "")


server = build_multi_consumer_mcp_server(
    ontology,
    store,
    resolve_consumer=resolve_consumer,
    token_verifier=verifier,
    auth=AuthSettings(
        issuer_url=AnyHttpUrl("https://auth.example.com"),
        resource_server_url=AnyHttpUrl("http://localhost:8000"),
    ),
)

if __name__ == "__main__":
    server.run(transport="streamable-http", stateless_http=True, json_response=True)

StaticTokenVerifier is a stand-in that only makes the example run offline. A deployment replaces it with its identity provider's verifier, for example JWT validation against the provider's JWKS or OAuth token introspection. The verifier must be an async def verify_token that returns an AccessToken, or None to refuse the token. AuthSettings names the issuer that minted the token and this server's own URL. Running the file serves http://localhost:8000/mcp.

An agent calls a tool with its bearer token:

POST /mcp HTTP/1.1
Host: localhost:8000
Authorization: Bearer token-alice
Accept: application/json, text/event-stream
Content-Type: application/json

{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "query_objects", "arguments": {"obj_type": "Ticket"}}}

The server answers 200 OK with the rows that alice's billing queue scope allows. content carries the same result as text for clients that do not read structuredContent; valid_from is the time the row was inserted:

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "content": [{"type": "text", "text": "<the structuredContent below, as JSON text>"}],
    "isError": false,
    "structuredContent": {
      "result": [
        {
          "payload": {"id": "ticket-1", "subject": "Invoice mismatch", "queue_id": "billing"},
          "lineage": {
            "object_type": "Ticket",
            "object_id": "ticket-1",
            "valid_from": "<insert time>",
            "valid_to": null,
            "source_system": "demo",
            "source_id": null,
            "extracted_at": null
          }
        }
      ],
      "next_cursor": null
    }
  }
}

The same request without the Authorization header, or with a token the verifier does not know, is refused with 401 Unauthorized. The MCP transport refuses it before ontary resolves a consumer, so no resolve_consumer call and no audit entry happens:

HTTP/1.1 401 Unauthorized
Content-Type: application/json
WWW-Authenticate: Bearer error="invalid_token", error_description="Authentication required", resource_metadata="http://localhost:8000/.well-known/oauth-protected-resource"

{"error": "invalid_token", "error_description": "Authentication required"}

The 200 body above is what json_response=True returns. Without it, the server streams the same JSON-RPC message as a server-sent event. The 401 is the same in both modes.

The resolver is application code. It may look up a database row or an organization directory, but it is not a token verifier and it is not a security proxy supplied by ontary. Once the resolver returns, the server stamps Consumer.principal from the verified token, so a resolver cannot forge the principal that the transport proved.

Resolution happens for every invocation and is never cached. A revoked or re-scoped token therefore cannot reuse an earlier consumer binding, and concurrent requests for different identities share the immutable runtime machinery without sharing per-request consumer state.

Authentication and failure direction¶

The SDK verifies no token and issues none. Pass the deployer's token_verifier and AuthSettings into the builder so MCPServer can install its authentication pipeline. The callback receives an AccessToken that MCP has already verified.

If a request has no verified token, the server raises PermissionDenied with code UNAUTHENTICATED. With token_verifier and AuthSettings configured, a missing or unknown bearer token never gets this far: the transport answers with the 401 shown above. UNAUTHENTICATED is what a server built without them, or run over stdio, returns. If a verified principal has no mapped Consumer, the server raises PermissionDenied with code CONSUMER_UNRESOLVED. These are distinct recovery paths: configure authentication for the first and map the principal for the second. Both happen before an OntologyClient exists, so neither is audited as an ontology action or Function.

Once a request resolves, audit entries retain both identities: principal records who the transport proved and actor/role records the consumer the resolver selected. That makes a resolver which maps every principal to one privileged actor visible to an auditor.

Transport requirements¶

The multi-consumer builder returns an MCPServer but does not choose a transport mode. On mcp 2.x, transport options (stateless_http, json_response, transport_security, host) are keyword arguments of run() and streamable_http_app(), and port of run(). Each request resolves its own token in stateful sessions as well, and the SDK's test suite pins both modes at the ASGI boundary.

server.run(transport="streamable-http", stateless_http=True, json_response=True)

stdio carries no bearer-token context. A multi-consumer server run over stdio therefore refuses calls with UNAUTHENTICATED; use the single-consumer build_mcp_server(ontology, store, consumer) for a zero-infrastructure stdio process. The SDK does not provide its own HTTP entrypoint: the deployer runs the MCPServer instance or wraps its ASGI application in the host's process.

See the API reference MCP section for the builder signatures and tool behavior, and its Declarations paragraph for the runtime's declared identity contract.

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