Atomadic MCP

Atomadic MCP: 68 deterministic tools for agent identity, escrow, SLA, EU AI Act compliance.

Sollte ich dies verwenden

Qualität und Sicherheit

B
Qualität der Beschreibung
70%
Vollständigkeit des Schemas
73%
Qualität der Benennung
81%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Befunde (45)

  • LOWTool 'agents_contradiction' description lacks action verbin agents_contradiction
  • LOWTool 'agents_intent_classify' description lacks action verbin agents_intent_classify
  • LOWTool 'agents_plan' description lacks action verbin agents_plan
  • LOWTool 'agents_register' description lacks action verbin agents_register
  • LOWTool 'agents_reputation' description lacks action verbin agents_reputation
  • LOWTool 'agents_semantic_diff' description lacks action verbin agents_semantic_diff
  • LOWTool 'compliance_check' description lacks action verbin compliance_check
  • LOWTool 'compliance_eu_ai_act' description lacks action verbin compliance_eu_ai_act
  • LOWTool 'compliance_explain' description lacks action verbin compliance_explain
  • LOWTool 'compliance_fairness' description lacks action verbin compliance_fairness

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~4,701Tokens (Tool-Definitionen)
~490 BTypische Antwortgröße
Erhebliche Auswirkung auf die Aufmerksamkeit (3.67% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

{
  "mcpServers": {
    "atomadic": {
      "url": "https://mcp.atomadic.tech/mcp"
    }
  }
}

Remote-Endpunkte

https://mcp.atomadic.tech/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (68)

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⚪murmuration(agent_id)

START HERE (free, no key). Initialize your Atomadic control surface in one call: returns a verbose, step-by-step setup (SDK install, MCP connect, your unlocked tools), a RUNNABLE polyglot demo that proves the superpowers, recon links, and a one-command bootstrap. Idempotent — re-call anytime to re-check.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string",
      "description": "Optional stable id to track your onboarding state across calls."
    }
  }
}
⚪agents_contradiction(statements)

[agents] Detect contradictions in agent outputs

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "statements": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "example": [
        "The model accuracy is 95%",
        "The model fails 20% of the time"
      ]
    }
  },
  "required": [
    "statements"
  ]
}
⚪agents_intent_classify(text)

[agents] Agent intent classification

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "example": "I need to fetch user data and store it in the database"
    }
  },
  "required": [
    "text"
  ]
}
⚪agents_plan(goal, constraints)

[agents] Autonomous agent goal planning

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "goal": {
      "type": "string",
      "example": "Deploy a web service with zero downtime"
    },
    "constraints": {
      "type": "object",
      "description": "Optional constraints for the plan (e.g., time limits, resource caps)",
      "additionalProperties": true
    }
  },
  "required": [
    "goal"
  ]
}
⚪agents_register(agent_id, capabilities)

[agents] Register an agent

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string",
      "example": "my-agent-001"
    },
    "capabilities": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "example": [
        "inference",
        "planning",
        "code-generation"
      ]
    }
  },
  "required": [
    "agent_id",
    "capabilities"
  ]
}
⚪agents_reputation(agent_id)

[agents] Agent reputation and trust score

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string",
      "example": "my-agent-001"
    }
  },
  "required": [
    "agent_id"
  ]
}
⚪agents_semantic_diff(output_a, output_b, context)

[agents] Semantic diff between agent outputs

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "output_a": {
      "type": "string"
    },
    "output_b": {
      "type": "string"
    },
    "context": {
      "type": "string"
    }
  },
  "required": [
    "output_a",
    "output_b"
  ]
}
⚪agents_topology

[agents] Live swarm topology

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
🟢compliance_check(data_type, jurisdiction, operation)

[security] GDPR/CCPA compliance gate

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "data_type": {
      "type": "string",
      "example": "user_pii"
    },
    "jurisdiction": {
      "type": "string",
      "example": "EU"
    },
    "operation": {
      "type": "string",
      "example": "store"
    }
  },
  "required": [
    "data_type",
    "jurisdiction",
    "operation"
  ]
}
⚪compliance_eu_ai_act(system_id, risk_category, intended_purpose)

[security] EU AI Act compliance certificate

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "system_id": {
      "type": "string"
    },
    "risk_category": {
      "type": "string",
      "enum": [
        "minimal",
        "limited",
        "high",
        "unacceptable"
      ]
    },
    "intended_purpose": {
      "type": "string"
    }
  },
  "required": [
    "system_id",
    "risk_category",
    "intended_purpose"
  ]
}
🟢compliance_explain(model_id, decision_id, subject_context)

[security] Explainability certificate (XPL-100-ExplainBound)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "model_id": {
      "type": "string"
    },
    "decision_id": {
      "type": "string"
    },
    "subject_context": {
      "type": "object"
    }
  },
  "required": [
    "model_id",
    "decision_id",
    "subject_context"
  ]
}
⚪compliance_fairness(model_id, dataset_id, protected_attrs, metric)

[security] Fairness proof (FNS-100-FairnessBound)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "model_id": {
      "type": "string"
    },
    "dataset_id": {
      "type": "string"
    },
    "protected_attrs": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "metric": {
      "type": "string",
      "example": "demographic_parity"
    }
  },
  "required": [
    "model_id",
    "dataset_id",
    "protected_attrs",
    "metric"
  ]
}
⚪compliance_incident(system_id, severity, description, affected_users)

[security] Report compliance incident

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "system_id": {
      "type": "string"
    },
    "severity": {
      "type": "string",
      "enum": [
        "low",
        "medium",
        "high",
        "critical"
      ]
    },
    "description": {
      "type": "string"
    },
    "affected_users": {
      "type": "integer"
    }
  },
  "required": [
    "system_id",
    "severity",
    "description",
    "affected_users"
  ]
}
⚪compliance_incidents(system_id)

[security] Incident registry

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "system_id": {
      "type": "string"
    }
  },
  "required": [
    "system_id"
  ]
}
⚪compliance_lineage(model_id, dataset_stages)

[security] Data lineage (LIN-100-ChainIntegrity)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "model_id": {
      "type": "string"
    },
    "dataset_stages": {
      "type": "array",
      "items": {
        "type": "object"
      }
    }
  },
  "required": [
    "model_id",
    "dataset_stages"
  ]
}
⚪compliance_oversight(system_id, reviewer_id, decision, notes)

[security] Log oversight event

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "system_id": {
      "type": "string"
    },
    "reviewer_id": {
      "type": "string"
    },
    "decision": {
      "type": "string"
    },
    "notes": {
      "type": "string"
    }
  },
  "required": [
    "system_id",
    "reviewer_id",
    "decision",
    "notes"
  ]
}
⚪compliance_oversight_history(system_id)

[security] Oversight history

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "system_id": {
      "type": "string"
    }
  },
  "required": [
    "system_id"
  ]
}
⚪compliance_transparency(system_id, period, include)

[security] Transparency report

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "system_id": {
      "type": "string"
    },
    "period": {
      "type": "string",
      "example": "2026-Q1"
    },
    "include": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "example": [
        "incidents",
        "oversight",
        "fairness"
      ]
    }
  },
  "required": [
    "system_id",
    "period",
    "include"
  ]
}
⚪defi_bridge_verify(bridge_id, source_chain, dest_chain, amount, token)

[defi] Bridge Proof (BRP-100-BridgeIntegrity)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "bridge_id": {
      "type": "string"
    },
    "source_chain": {
      "type": "string"
    },
    "dest_chain": {
      "type": "string"
    },
    "amount": {
      "type": "number"
    },
    "token": {
      "type": "string"
    }
  },
  "required": [
    "bridge_id",
    "source_chain",
    "dest_chain",
    "amount",
    "token"
  ]
}
⚪defi_contract_audit(contract_id, contract_type)

[defi] Contract Verify (CVR-100-AuditBound)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "contract_id": {
      "type": "string"
    },
    "contract_type": {
      "type": "string",
      "example": "erc20"
    }
  },
  "required": [
    "contract_id",
    "contract_type"
  ]
}
🟢defi_liquidation_check(position_id, collateral_value, debt_value, collateral_factor)

[defi] Liquid Shield (LQS-100-LiquidationBound)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "position_id": {
      "type": "string"
    },
    "collateral_value": {
      "type": "number"
    },
    "debt_value": {
      "type": "number"
    },
    "collateral_factor": {
      "type": "number",
      "format": "float"
    }
  },
  "required": [
    "position_id",
    "collateral_value",
    "debt_value",
    "collateral_factor"
  ]
}
⚪defi_optimize(protocol, position, risk_tolerance)

[defi] LP parameter optimization (DFP-100-OptimalTick)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "protocol": {
      "type": "string",
      "example": "uniswap_v3"
    },
    "position": {
      "type": "object",
      "properties": {
        "token_a": {
          "type": "string",
          "example": "ETH"
        },
        "token_b": {
          "type": "string",
          "example": "USDC"
        },
        "amount_usd": {
          "type": "number",
          "example": 10000
        }
      }
    },
    "risk_tolerance": {
      "type": "number",
      "format": "float",
      "example": 0.5
    }
  },
  "required": [
    "protocol",
    "position",
    "risk_tolerance"
  ]
}
⚪defi_oracle_verify(oracle_address, price_feed, reference_price, asset, tvl_at_risk)

[defi] Oracle guard (OGD-100-ManipulationBound)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "oracle_address": {
      "type": "string"
    },
    "price_feed": {
      "type": "string"
    },
    "reference_price": {
      "type": "number"
    },
    "asset": {
      "type": "string"
    },
    "tvl_at_risk": {
      "type": "number"
    }
  },
  "required": [
    "oracle_address",
    "price_feed",
    "reference_price",
    "asset",
    "tvl_at_risk"
  ]
}
⚪defi_risk_score(protocol, position, market_conditions)

[defi] DeFi risk scoring (DFI-101-RiskScoreBound)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "protocol": {
      "type": "string"
    },
    "position": {
      "type": "object"
    },
    "market_conditions": {
      "type": "object"
    }
  },
  "required": [
    "protocol",
    "position",
    "market_conditions"
  ]
}
⚪defi_yield_optimize(principal_usd, risk_tolerance)

[defi] Yield Oracle (YLD-100-YieldConvergence)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "principal_usd": {
      "type": "number",
      "example": 10000
    },
    "risk_tolerance": {
      "type": "number",
      "format": "float",
      "example": 0.5
    }
  },
  "required": [
    "principal_usd",
    "risk_tolerance"
  ]
}
⚪drift_certificate(id)

[trust] Drift certificate

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string"
    }
  },
  "required": [
    "id"
  ]
}
🟢drift_check(model_id, reference_window, current_window)

[trust] DriftGuard check (DRG-100-DriftBound)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "model_id": {
      "type": "string"
    },
    "reference_window": {
      "type": "object"
    },
    "current_window": {
      "type": "object"
    }
  },
  "required": [
    "model_id",
    "reference_window",
    "current_window"
  ]
}
🟡escrow_create(payer_id, payee_id, amount, currency, conditions)

[defi] Create agent-to-agent escrow

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "payer_id": {
      "type": "string"
    },
    "payee_id": {
      "type": "string"
    },
    "amount": {
      "type": "number"
    },
    "currency": {
      "type": "string"
    },
    "conditions": {
      "type": "object"
    }
  },
  "required": [
    "payer_id",
    "payee_id",
    "amount",
    "currency"
  ]
}
⚪escrow_dispute(escrow_id, reason, evidence)

[defi] Open escrow dispute

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "escrow_id": {
      "type": "string"
    },
    "reason": {
      "type": "string"
    },
    "evidence": {
      "type": "object"
    }
  },
  "required": [
    "escrow_id",
    "reason"
  ]
}
⚪escrow_release(escrow_id)

[defi] Release escrowed funds

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "escrow_id": {
      "type": "string"
    }
  },
  "required": [
    "escrow_id"
  ]
}
⚪forge_badge_{model}

[fuse] Model quality badge

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
🟡forge_delta_submit(model_id, delta, description)

[fuse] Submit a model delta

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "model_id": {
      "type": "string"
    },
    "delta": {
      "type": "object"
    },
    "description": {
      "type": "string"
    }
  },
  "required": [
    "model_id",
    "delta"
  ]
}
⚪forge_leaderboard

[fuse] Model leaderboard

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪forge_quarantine

[fuse] Quarantined models

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪forge_verify(model_id, verification_type)

[fuse] Verify a model submission

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "model_id": {
      "type": "string"
    },
    "verification_type": {
      "type": "string"
    }
  },
  "required": [
    "model_id"
  ]
}
🟢identity_sybil_check

[identity] Sybil-resistance check

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪identity_verify(agent_id)

[identity] Topological identity verification

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string",
      "example": "my-agent-001"
    }
  },
  "required": [
    "agent_id"
  ]
}
⚪inference(messages)

[inference] Chat inference with hallucination guard

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "messages": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "role",
          "content"
        ],
        "properties": {
          "role": {
            "type": "string",
            "enum": [
              "user",
              "assistant",
              "system"
            ],
            "description": "Message role"
          },
          "content": {
            "type": "string",
            "description": "Message content"
          }
        }
      },
      "example": [
        {
          "role": "user",
          "content": "What is formal verification?"
        }
      ]
    }
  },
  "required": [
    "messages"
  ]
}
⚪inference_stream(messages)

[inference] Streaming inference with chain-of-thought

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "messages": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "role",
          "content"
        ],
        "properties": {
          "role": {
            "type": "string",
            "enum": [
              "user",
              "assistant",
              "system"
            ],
            "description": "Message role"
          },
          "content": {
            "type": "string",
            "description": "Message content"
          }
        }
      },
      "example": [
        {
          "role": "user",
          "content": "Explain formal verification step by step."
        }
      ]
    }
  },
  "required": [
    "messages"
  ]
}
⚪oracle_entropy

[trust] Entropy epoch oracle

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪oracle_hallucination(claim, context)

[trust] Hallucination oracle

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "claim": {
      "type": "string",
      "example": "Water boils at 100°C at sea level"
    },
    "context": {
      "type": "string",
      "example": "physics"
    }
  },
  "required": [
    "claim"
  ]
}
⚪rag_augment

[inference] Trusted RAG augmentation

Eingabe-Schema

{
  "type": "object"
}
⚪ratchet_advance(session_id)

[identity] RatchetGate — advance session

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "session_id": {
      "type": "string",
      "example": "sess-001"
    }
  },
  "required": [
    "session_id"
  ]
}
⚪ratchet_probe

[identity] RatchetGate — health probe

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪ratchet_register(session_id)

[identity] RatchetGate — register session

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "session_id": {
      "type": "string",
      "example": "sess-001"
    }
  },
  "required": [
    "session_id"
  ]
}
⚪reputation_record(agent_id, interaction_type, outcome, counterparty_id)

[agents] Record agent interaction

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string"
    },
    "interaction_type": {
      "type": "string"
    },
    "outcome": {
      "type": "string",
      "enum": [
        "positive",
        "negative",
        "neutral"
      ]
    },
    "counterparty_id": {
      "type": "string"
    }
  },
  "required": [
    "agent_id",
    "interaction_type",
    "outcome"
  ]
}
⚪rng_quantum

[inference] Quantum random number with proof

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪rng_verify

[inference] Verify current epoch random value

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪security_zero_day(target, context)

[security] Zero-day threat assessment

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "target": {
      "type": "string"
    },
    "context": {
      "type": "object"
    }
  },
  "required": [
    "target"
  ]
}
⚪sla_register(provider_id, consumer_id, terms)

[agents] Register an SLA

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "provider_id": {
      "type": "string"
    },
    "consumer_id": {
      "type": "string"
    },
    "terms": {
      "type": "object"
    }
  },
  "required": [
    "provider_id",
    "consumer_id",
    "terms"
  ]
}
⚪sla_report(sla_id, violation_type, description)

[agents] Report SLA violation

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "sla_id": {
      "type": "string"
    },
    "violation_type": {
      "type": "string"
    },
    "description": {
      "type": "string"
    }
  },
  "required": [
    "sla_id",
    "violation_type"
  ]
}
⚪swarm_inbox

[agents] Poll swarm inbox

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪swarm_relay(target_agent_id, message, ttl)

[agents] Relay message through swarm

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "target_agent_id": {
      "type": "string"
    },
    "message": {
      "type": "object"
    },
    "ttl": {
      "type": "integer",
      "description": "Hop limit for relay"
    }
  },
  "required": [
    "target_agent_id",
    "message"
  ]
}
⚪text_keywords(text, max_keywords)

[inference] Keyword extraction

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string"
    },
    "max_keywords": {
      "type": "integer",
      "example": 10
    }
  },
  "required": [
    "text"
  ]
}
⚪text_sentiment(text)

[inference] Sentiment analysis

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "example": "This API is incredibly reliable and easy to use."
    }
  },
  "required": [
    "text"
  ]
}
⚪text_summarize(text)

[inference] Text summarization

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "example": "Formal verification is the act of proving or disproving the correctness of intended algorithms underlying a system with respect to a certain formal specification or property, using formal methods of mathematics."
    }
  },
  "required": [
    "text"
  ]
}
⚪threat_score(agent_id, context)

[security] Multi-vector threat score

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string",
      "example": "my-agent-001"
    },
    "context": {
      "type": "string",
      "example": "multi-agent-transaction"
    }
  },
  "required": [
    "agent_id"
  ]
}
⚪trust_decay

[trust] Current trust ceiling value

Eingabe-Schema

{
  "type": "object",
  "properties": {}
}
⚪trust_history(agent_id)

[trust] Trust score history

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string"
    }
  },
  "required": [
    "agent_id"
  ]
}
⚪trust_score(agent_id)

[trust] Bounded trust score

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string"
    }
  },
  "required": [
    "agent_id"
  ]
}
🟢audit_work_claim(inputs, claimed_digest)

[Ledger product, entitlement: ledger] Recompute-audit an agent work claim: re-derive a deterministic SHA-256 digest from the claimed inputs and check it against the claimed digest. Returns GREEN (matches), RED (tampered), or UNVERIFIED (no claim supplied). Replay, not opinion. 3 free calls/day, then entitlement 'ledger'.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "inputs": {
      "type": "object",
      "description": "The work/claim payload to recompute a digest over."
    },
    "claimed_digest": {
      "type": "string",
      "description": "The agent's claimed SHA-256 digest to verify against. Omit for UNVERIFIED + the recomputed digest."
    }
  },
  "required": [
    "inputs"
  ]
}
⚪verify_settlement(inputs, claimed_digest)

[Ledger product, entitlement: ledger] Proof-gated settlement: recompute the work digest and authorize a payout only if the claimed digest verifies GREEN. No proof, no payout. 3 free calls/day, then entitlement 'ledger'.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "inputs": {
      "type": "object",
      "description": "The work payload backing the settlement."
    },
    "claimed_digest": {
      "type": "string",
      "description": "Claimed digest the payout is conditioned on."
    }
  },
  "required": [
    "inputs"
  ]
}
🟢receipt_reputation(receipts)

[Ledger product, entitlement: ledger] Compute an agent's reputation from a ledger of verifiable receipts (verified minus refuted), deterministically - not from vibes or LLM judgment. 3 free calls/day, then entitlement 'ledger'.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "receipts": {
      "type": "array",
      "description": "List of receipt objects, each with an 'ok' boolean and optional 'score'."
    }
  },
  "required": [
    "receipts"
  ]
}
⚪detect_contradiction(claims)

[Ledger product, entitlement: ledger] Detect contradictions across structured claims: the same key asserted with conflicting values flips RED, otherwise GREEN. Deterministic and recomputable. 3 free calls/day, then entitlement 'ledger'.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "claims": {
      "type": "array",
      "description": "List of claim objects, each with a 'key' (or 'subject') and a 'value'."
    }
  },
  "required": [
    "claims"
  ]
}
⚪enforce_sla(inputs, claimed_digest, obligation)

[Ledger product, entitlement: ledger] Enforce an SLA/obligation by recomputation: the obligation is met only if the work verifies GREEN against its claimed digest. 3 free calls/day, then entitlement 'ledger'.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "inputs": {
      "type": "object",
      "description": "The work payload to verify."
    },
    "claimed_digest": {
      "type": "string",
      "description": "Claimed digest the obligation is conditioned on."
    },
    "obligation": {
      "type": "string",
      "description": "Human-readable obligation label."
    }
  },
  "required": [
    "inputs"
  ]
}
⚪route_capability(intent)

[Ledger product, entitlement: ledger] Route a natural-language intent to a deterministic capability/pipeline coordinate. 3 free calls/day, then entitlement 'ledger'.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "intent": {
      "type": "string",
      "description": "The natural-language intent to route."
    }
  },
  "required": [
    "intent"
  ]
}
⚪render_polyglot_roundtrip_pure(source_text, atom_name, language)

[Fuse product · entitlement: fuse · FREE_R2_DEMO] Syntax-as-Data polyglot render head: source -> language-neutral logic IR -> source in target language. Canonical demo examples baked to R2 (4 examples × 12 languages). Live emit of novel inputs via the SDK.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "source_text": {
      "type": "string",
      "description": ""
    },
    "atom_name": {
      "type": "string",
      "description": ""
    },
    "language": {
      "type": "string",
      "description": ""
    }
  },
  "required": [
    "source_text",
    "atom_name"
  ]
}
⚪render_polyglot_all_pure(source_text, atom_name)

[Fuse product · entitlement: fuse · FREE_R2_DEMO] Polyglot emit to all 7 supported targets in one call. Returns {ok, ir, results, languages}. Canonical demo examples baked to R2; novel inputs via SDK.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "source_text": {
      "type": "string",
      "description": ""
    },
    "atom_name": {
      "type": "string",
      "description": ""
    }
  },
  "required": [
    "source_text",
    "atom_name"
  ]
}

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