Salesforce AgentForce CKG
Salesforce AgentForce knowledge graph — 40 nodes, Einstein Trust Layer, 11x fewer tokens than RAG.
Connect it
Settings → Connectors → Add custom connector
URL: https://ckg-agentforce.onrender.com/mcpSettings → Apps & Connectors → Advanced → Developer mode
Create connector with URL: https://ckg-agentforce.onrender.com/mcp{
"mcpServers": {
"ckg-agentforce": {
"url": "https://ckg-agentforce.onrender.com/mcp"
}
}
}claude mcp add --transport http ckg-agentforce https://ckg-agentforce.onrender.com/mcp{
"servers": {
"ckg-agentforce": {
"type": "http",
"url": "https://ckg-agentforce.onrender.com/mcp"
}
}
}{
"mcpServers": {
"ckg-agentforce": {
"command": "uvx",
"args": [
"ckg-agentforce"
]
}
}
}claude mcp add ckg-agentforce -- uvx ckg-agentforce10 tools, verified live
list_conceptsList all 40 AgentForce concepts in this knowledge graph.
search_conceptsFind AgentForce concepts by keyword. Args: query: Search term â e.g. 'resolution', 'trust', 'grounding', 'action', 'NIM'.
query_ckgTraverse the AgentForce knowledge graph from any concept. Returns prerequisites (what this concept needs) and dependents (what it enables). Every relationship traces to an authoritative Salesforce doc URL. Args: concept: Concept name â e.g. 'Autonomous Resolution', 'Einstein Trust Layer', 'Service Agent', 'Grounding', 'NVIDIA NIM'. depth: Traversal depth 1â5 (default 3
get_prerequisitesReturn the full ordered prerequisite chain for an AgentForce concept. Shows everything the concept depends on â the complete upstream path. Args: concept: Target concept â e.g. 'Autonomous Resolution', 'Multi-LoRA Serving', 'Custom Actions', 'Semantic Retrieval'.
resolution_pathTrace the exact path that determines an AgentForce autonomous resolution event. This is the $2/resolution billing path â what the agent must traverse correctly to resolve autonomously without human handoff.
route_queryRoute an AgentForce question to the optimal model and reasoning approach via graph depth. The CKG graph IS the router. AgentForce dependency chains (e.g. Einstein Trust Layer â Data Cloud â NVIDIA NIM â Resolution Criteria) have typed hops that signal reasoning complexity deterministically. No heuristic: the graph decides. Routing table: hop_depth 1 â haiku · direct (simple c
verify_sourceReturn the source URL and content hash for an AgentForce concept node. Audit chain: edge answer â graph commit â source_hash â source_url (fetch hint) Verification: curl -s <source_url> | sha256sum # compare output to source_hash Args: concept: Concept label (partial match supported). receipt: If True, also return a signed BoundaryAttest receipt envelope (
export_benchmark_receiptGenerate a signed BoundaryAttest receipt for the agentforce CKG benchmark result. Produces a `ckg.benchmark_result.exported` claim signed with this server's Ed25519 session key. Any party holding the public key can verify the signature over the canonical JSON claim. Implements GuardrailDecisionV1 · experimental-interop-v0.1. Result: F1 0.471 over 30 queries · ckg-benchmark v0.6.2 · 4à over R
evaluate_trust_chainReturn a machine-readable JSON trust chain + ROI analysis + payment recommendation. Designed for autonomous agent consumption. The agent reads the JSON, evaluates the ROI gate, selects the cheapest positive-ROI payment option, and purchases autonomously if budget is pre-authorized. Trust chain closes the audit gap that RAG cannot: source_hashes â SHA-256 of source docs (what was known at ex
query_intersectAnswer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B). query_ckg walks outward from one concept. This intersects the reachable sets of two or more, which is the shape of most real questions â "the component that satisfies A AND applies to B". Neither anchor alone answers it; the answer lives in the overlap. Every branch is an exact set of declared edges, so the int
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