Onto MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Onto MCP Serverlist available realms"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Onto MCP Server
FastMCP server for Onto platform access via a configured Onto API key.
Runtime Contract
Required environment variables:
ONTO_API_BASEONTO_API_KEY
Optional environment variables:
ONTO_API_KEY_HEADERdefault:X-API-KeyONTO_API_KEY_PASSTHROUGH_HEADERdefault:X-Onto-Api-KeyMCP_TRANSPORTvalues:stdio,httpPORTSESSION_STATE_API_BASESESSION_STATE_API_KEY
The server no longer supports login/password, OAuth code exchange, or manual user token flows.
In HTTP mode, Onto backend authentication can come from either:
server-side
ONTO_API_KEYincoming request header
X-Onto-Api-Key(or the value ofONTO_API_KEY_PASSTHROUGH_HEADER)
SESSION_STATE_API_KEY remains optional unless you use the session-state helper tools.
Related MCP server: agntcy-mcp-server
Tools
how_to_use_onto_mcp(question="", safety_mode="read_only")list_available_realms()list_realm_agents(realm_id)get_realm_agent(realm_id, slug)preflight_realm_agent_governance_proposal(realm_id, proposal_artifact_id)admit_realm_agent(realm_id, candidate)about_onto(focus="")search_templates(name_part, realm_id=None, include_children=False, include_parents=False)search_relation_templates(realm_id, relation_type_name="", meta_ids=None)search_entities_by_relations(realm_id, searched_meta_ids, predicates=None, include_descendants=True, first=0, offset=100, sort=None)search_agent_memory(realm_id, target_kind, target_id, memory_kind="", status="", reality="", author_id="", source_ref="", branch_id="", query="", first=0, offset=100)get_agent_memory_record(realm_id, record_id)create_memory_artifact_draft(realm_id, artifact_path, artifact_kind, write_mode, body, summary, source_ref, source_context=None, review_destination=None, agent_principal="", targets=None)get_memory_artifact(realm_id, artifact_id)get_memory_artifact_by_path(realm_id, artifact_path)get_own_memory_artifact_draft_by_path(realm_id, artifact_path, agent_principal)search_memory_artifacts(realm_id, artifact_kind="", write_mode="", artifact_path="", review_destination="", target_kind="", target_id="", query="", first=0, offset=100)update_memory_artifact_draft(realm_id, artifact_id, body=None, summary=None, review_destination=None, agent_principal="", targets=None)append_memory_artifact(realm_id, artifact_id, body, source_ref, summary="", source_context=None, agent_principal="")submit_memory_artifact(realm_id, artifact_id)accept_memory_artifact(realm_id, artifact_id)revoke_memory_artifact(realm_id, artifact_id)supersede_memory_artifact(realm_id, artifact_id, artifact_path, artifact_kind, write_mode, body, summary, source_ref, source_context=None, review_destination=None, agent_principal="", targets=None)search_objects(realm_id=None, name_filter="", template_uuid="", comment_filter="", load_all=False, first=0, offset=100)create_realm(name, comment="")update_realm(realm_id, name, comment="")delete_realm(realm_id)save_template(realm_id, name, comment="", template_id="")create_template(realm_id, name, comment="")get_template(realm_id, template_id, include_children=False, include_parents=False, name="")delete_template(realm_id, template_id)link_template_to_parents(realm_id, child_template_id, parent_template_ids)unlink_template_from_parents(realm_id, child_template_id, parent_template_ids)save_entity(realm_id, name, comment="", entity_id="", meta_entity_id="")save_entities_batch(realm_id, entities)create_entities_batch(realm_id, entities)get_entity(realm_id, entity_id, related_diagrams=False, related_entities=False, with_empty_stickers=False, name="")get_node_chat_messages(realm_id, node_id)create_node_chat_message(realm_id, node_id, text)search_entities(realm_id=None, name_filter="", meta_entity_id="", comment_filter="", include_inherited=False, first=0, offset=100)search_entities_by_fields(realm_id, field_filters, meta_entity_id="", name_filter="", comment_filter="", first=0, offset=100)search_entities_with_related_meta(realm_id=None, name_filter="", meta_entity_id="", comment_filter="", include_inherited=False, first=0, offset=100)delete_entity(realm_id, entity_ids, name="")save_entity_fields(realm_id, entity_id, fields)delete_entity_fields(realm_id, entity_id, field_ids)save_template_fields(realm_id, template_id, fields)delete_template_fields(realm_id, template_id, field_ids)search_diagrams(realm_id, name_part="", tag_ids=None, first=0, offset=100)search_context_tags(realm_id, name_part="", first=0, offset=100)create_context_tag_from_object(realm_id, entity_id)add_diagram_tag(realm_id, diagram_id, tag_id)remove_diagram_tag(realm_id, diagram_id, tag_id)add_existing_nodes_to_diagram(realm_id, diagram_id, nodes)create_diagram(realm_id, name, comment="")get_diagram(realm_id, diagram_id)update_diagram(realm_id, diagram_id, name="", comment="", tag_ids=None)delete_diagram(realm_id, diagram_id)create_relation(realm_id, start_entity_id, end_entity_id, relation_type_name, start_role="", end_role="", additional_properties=None)update_relation(realm_id, start_entity_id, end_entity_id, relation_type_name, start_role="", end_role="", additional_properties=None)delete_relation(realm_id, start_entity_id, end_entity_id, relation_type_name, name="")create_meta_relation(realm_id, start_meta_id, end_meta_id, relation_type_name, start_min=0, start_max=1, end_min=0, end_max=1, equal=False)update_meta_relation(realm_id, start_meta_id, end_meta_id, relation_type_name, start_min=0, start_max=1, end_min=0, end_max=1, equal=False)delete_meta_relation(realm_id, start_meta_id, end_meta_id, relation_type_name)saveOntoAIThreadID(thread_external_id, ctx)getOntoAIThreadID(ctx)
Agent operational guidance is defined by the canonical machine-readable contract
in onto_mcp/agent_contract.json, exposed at runtime through
how_to_use_onto_mcp, and summarized for humans in docs/AGENT_ENTRY_GUIDE.md.
Realm-agent Constitution, charter, and registry proposals use the read-only preflight twice:
after submit and before any approval sheet or position, then again immediately
before accept. The proposal must carry the submit-time registry id in
source_context.realm_agent_governance_submit. A pass validates structure,
exact body bytes, predecessor and frozen-electorate evidence only; it does not
count votes or authorize acceptance.
Owner-driven v2 resident admission uses exactly one high-risk tool:
admit_realm_agent(realm_id, candidate). It sends the bare recursively closed
candidate to POST /realm/{realmId}/agent-population/admissions with the
ambient owner credential. The tool has no admission preflight, confirm
argument, client-supplied fingerprint, generic MemoryArtifact lifecycle chain,
or alternate route. If a sent request has no valid received response, the tool
returns outcome_unknown; recovery is the same exact admission call with the
same candidate.
Resources
onto://spacesonto://user/info
Configuration Example
{
"mcpServers": {
"onto-mcp-server": {
"command": "python",
"args": ["-m", "onto_mcp.server"],
"cwd": "/path/to/repo",
"env": {
"ONTO_API_BASE": "https://app.ontonet.ru/api/v2/core",
"ONTO_API_KEY": "replace-with-onto-api-key"
}
}
}
}Running
python -m pip install -r requirements.txt
python -m onto_mcp.serverHTTP mode:
MCP_TRANSPORT=http PORT=8080 python -m onto_mcp.serverIf you use HTTP mode or session-state helpers, also configure SESSION_STATE_API_KEY.
This server cannot be deployed
Maintenance
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Render, verify, describe, and safely edit Mermaid diagrams through MCP.
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MCP tools for TON Sites, TON DNS and TON Storage.
Agent knowledge, private memory and coordination. Connect with MCP OAuth or an agent key.
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