Salesforce AgentForce CKG
Salesforce AgentForce knowledge graph — 40 nodes, Einstein Trust Layer, 11x fewer tokens than RAG.
¿Debería usar esto?
Calidad y seguridad
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"ckg-agentforce": {
"command": "uvx",
"args": [
"ckg-agentforce"
]
}
}
}Paquetes ejecutables
0.10.3stdioPuntos de conexión remotos
https://ckg-agentforce.onrender.com/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (10)
🟢list_concepts
List all 40 AgentForce concepts in this knowledge graph.
Esquema de entrada
{
"type": "object",
"properties": {},
"title": "list_conceptsArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "list_conceptsOutput"
}🟢search_concepts(query)
Find AgentForce concepts by keyword. Args: query: Search term — e.g. 'resolution', 'trust', 'grounding', 'action', 'NIM'.
Esquema de entrada
{
"type": "object",
"properties": {
"query": {
"title": "Query",
"type": "string"
}
},
"required": [
"query"
],
"title": "search_conceptsArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "search_conceptsOutput"
}🟢query_ckg(concept, depth)
Traverse 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 — e.g. 'Autonomous Resolution', 'Einstein Trust Layer', 'Service Agent', 'Grounding', 'NVIDIA NIM'. depth: Traversal depth 1–5 (default 3).
Esquema de entrada
{
"type": "object",
"properties": {
"concept": {
"title": "Concept",
"type": "string"
},
"depth": {
"default": 3,
"title": "Depth",
"type": "integer"
}
},
"required": [
"concept"
],
"title": "query_ckgArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "query_ckgOutput"
}🟢get_prerequisites(concept)
Return the full ordered prerequisite chain for an AgentForce concept. Shows everything the concept depends on — the complete upstream path. Args: concept: Target concept — e.g. 'Autonomous Resolution', 'Multi-LoRA Serving', 'Custom Actions', 'Semantic Retrieval'.
Esquema de entrada
{
"type": "object",
"properties": {
"concept": {
"title": "Concept",
"type": "string"
}
},
"required": [
"concept"
],
"title": "get_prerequisitesArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "get_prerequisitesOutput"
}⚪resolution_path
Trace the exact path that determines an AgentForce autonomous resolution event. This is the $2/resolution billing path — what the agent must traverse correctly to resolve autonomously without human handoff.
Esquema de entrada
{
"type": "object",
"properties": {},
"title": "resolution_pathArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "resolution_pathOutput"
}🟢route_query(question)
Route 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 → Data Cloud → NVIDIA NIM → Resolution Criteria) have typed hops that signal reasoning complexity deterministically. No heuristic: the graph decides. Routing table: hop_depth 1 → haiku · direct (simple concept lookup) hop_depth 2 → sonnet · generic_cot (moderate chain) hop_depth 3+ → opus · sparql_cot (deep dependency, structured reasoning) Args: question: Concept name or natural language question about Salesforce AgentForce. Returns: model_tier + reasoning_approach + why + context subgraph to inject before LLM call.
Esquema de entrada
{
"type": "object",
"properties": {
"question": {
"title": "Question",
"type": "string"
}
},
"required": [
"question"
],
"title": "route_queryArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "route_queryOutput"
}⚪verify_source(concept, receipt)
Return the source URL and content hash for an AgentForce concept node. Audit chain: edge answer → graph commit → source_hash → 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 (experimental-interop-v0.1) binding concept_label + source_url + source_hash + timestamp to this server's ed25519 session key.
Esquema de entrada
{
"type": "object",
"properties": {
"concept": {
"title": "Concept",
"type": "string"
},
"receipt": {
"default": false,
"title": "Receipt",
"type": "boolean"
}
},
"required": [
"concept"
],
"title": "verify_sourceArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "verify_sourceOutput"
}⚪export_benchmark_receipt
Generate 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 · 4× over RAG baseline (0.123).
Esquema de entrada
{
"type": "object",
"properties": {},
"title": "export_benchmark_receiptArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "export_benchmark_receiptOutput"
}⚪evaluate_trust_chain
Return 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 → SHA-256 of source docs (what was known at extraction time) graph_hash → SHA-256 of the installed graph file (what is declared now) signer_key_id → Ed25519 public key fingerprint (who is attesting) benchmark_receipt → BoundaryAttest-signed F1 claim (cryptographic performance proof) scitt_status → SCITT ledger submission status (public audit anchor) ROI gate: if total_value_per_call_usd > payment_option.price_per_call_usd → autonomous_pay = true. Returns: JSON string (schema: ckg-trust-chain-v1).
Esquema de entrada
{
"type": "object",
"properties": {},
"title": "evaluate_trust_chainArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "evaluate_trust_chainOutput"
}🟡query_intersect(branches, depth, direction, mode, limit)
Answer 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 — "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 intersection is exact. A concept appears only if a declared path reaches it from each anchor. A relation missing from the graph produces an empty result, never a guess. Args: branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes everything within `depth` hops, or an anchor plus an explicit relation path using '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the frontier. '*' matches any relation. Mix both forms freely. depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths. direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default). mode: 'AND' (default) intersects branches; 'OR' unions them. limit: Max concepts listed, 1-200 (default 40). The true count is always shown. Returns: Markdown with the query plan and its per-step set sizes, then the answer set with taxonomy tags. Reports which branch was empty when the intersection is empty.
Esquema de entrada
{
"type": "object",
"properties": {
"branches": {
"items": {
"type": "string"
},
"title": "Branches",
"type": "array"
},
"depth": {
"default": 2,
"title": "Depth",
"type": "integer"
},
"direction": {
"default": "both",
"title": "Direction",
"type": "string"
},
"mode": {
"default": "AND",
"title": "Mode",
"type": "string"
},
"limit": {
"default": 40,
"title": "Limit",
"type": "integer"
}
},
"required": [
"branches"
],
"title": "query_intersectArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "query_intersectOutput"
}Comunidad
Evidencia