claude-tools.json

[
  {
    "name": "delegate_task",
    "description": "Delegate a task to a specialized sub-agent within the Network-AI swarm. The swarm uses a shared blackboard for coordination. Use this to route work to agents like data_analyst, strategy_advisor, or risk_assessor.",
    "input_schema": {
      "type": "object",
      "properties": {
        "targetAgent": {
          "type": "string",
          "description": "The identifier of the target agent to receive the task (e.g. data_analyst, strategy_advisor, risk_assessor)"
        },
        "taskPayload": {
          "type": "object",
          "description": "The task context, instructions, and any required data"
        },
        "priority": {
          "type": "string",
          "enum": ["low", "normal", "high", "critical"],
          "description": "Task priority level for queue ordering. Defaults to normal."
        },
        "timeout": {
          "type": "number",
          "description": "Maximum execution time in milliseconds. Defaults to 30000."
        },
        "requiresAuth": {
          "type": "boolean",
          "description": "Whether this task requires AuthGuardian permission grant before execution."
        }
      },
      "required": ["targetAgent", "taskPayload"]
    }
  },
  {
    "name": "query_swarm_state",
    "description": "Query the current state of the agent swarm — active tasks, blackboard contents, agent availability, and permission grants.",
    "input_schema": {
      "type": "object",
      "properties": {
        "scope": {
          "type": "string",
          "enum": ["all", "agents", "tasks", "blackboard", "permissions"],
          "description": "The scope of state information to retrieve. Defaults to all."
        },
        "agentFilter": {
          "type": "array",
          "items": { "type": "string" },
          "description": "Optional list of agent IDs to filter results"
        },
        "includeHistory": {
          "type": "boolean",
          "description": "Include historical task execution data. Defaults to false."
        }
      }
    }
  },
  {
    "name": "spawn_parallel_agents",
    "description": "Spawn multiple sub-agents in parallel for complex task decomposition. Results are combined using a synthesis strategy.",
    "input_schema": {
      "type": "object",
      "properties": {
        "tasks": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "agentType": { "type": "string" },
              "taskPayload": { "type": "object" }
            },
            "required": ["agentType", "taskPayload"]
          },
          "description": "Array of parallel tasks to execute simultaneously"
        },
        "synthesisStrategy": {
          "type": "string",
          "enum": ["merge", "vote", "chain", "first-success"],
          "description": "How to combine results from parallel agents. Defaults to merge."
        }
      },
      "required": ["tasks"]
    }
  },
  {
    "name": "request_permission",
    "description": "Request permission from AuthGuardian before accessing a protected resource type. Always call this before DATABASE, PAYMENTS, EMAIL, or FILE_EXPORT operations.",
    "input_schema": {
      "type": "object",
      "properties": {
        "resourceType": {
          "type": "string",
          "enum": ["DATABASE", "PAYMENTS", "EMAIL", "FILE_EXPORT"],
          "description": "The type of protected resource being requested"
        },
        "justification": {
          "type": "string",
          "description": "Clear reason for requesting access — must be specific to the current task"
        },
        "scope": {
          "type": "string",
          "description": "Specific scope of access needed (e.g. read:invoices, read:revenue)"
        }
      },
      "required": ["resourceType", "justification"]
    }
  },
  {
    "name": "update_blackboard",
    "description": "Write or update an entry on the shared blackboard for cross-agent coordination. The blackboard persists state between agents.",
    "input_schema": {
      "type": "object",
      "properties": {
        "key": {
          "type": "string",
          "description": "The blackboard entry key (e.g. task:001:data_analyst)"
        },
        "value": {
          "description": "The data to store — can be any JSON-serializable value"
        },
        "ttl": {
          "type": "number",
          "description": "Time-to-live in seconds. Entry expires and returns null after this duration."
        }
      },
      "required": ["key", "value"]
    }
  },
  {
    "name": "inject_context",
    "description": "Read the persistent project context (Layer 3 memory) and return a formatted markdown block for injection into an agent system prompt. Contains goals, tech stack, architecture decisions, milestones, and banned approaches that persist across all sessions.",
    "input_schema": {
      "type": "object",
      "properties": {}
    }
  },
  {
    "name": "update_context",
    "description": "Persist a decision, milestone, stack entry, goal, or banned approach to the project context file (Layer 3 memory). Use this to record information that every agent in every future session should know.",
    "input_schema": {
      "type": "object",
      "properties": {
        "section": {
          "type": "string",
          "enum": ["decisions", "milestones", "stack", "goals", "banned", "project"],
          "description": "The context section to update"
        },
        "add": {
          "description": "Item to append — JSON object for decisions/milestones, plain string for goals/banned"
        },
        "set": {
          "type": "object",
          "description": "Key-value pairs to merge (use for stack and project sections)"
        },
        "complete": {
          "type": "string",
          "description": "Milestone name to mark as completed (milestones section only)"
        }
      },
      "required": ["section"]
    }
  },
  {
    "name": "run_qa_harness",
    "description": "Run a batch of blackboard entries through the QA quality gate and return aggregate results including pass rate, contradictions between agents, and a regression snapshot.",
    "input_schema": {
      "type": "object",
      "properties": {
        "scenarios": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "id": { "type": "string", "description": "Unique scenario identifier" },
              "key": { "type": "string", "description": "Blackboard key" },
              "value": { "description": "The value to gate — any JSON-serializable data" },
              "sourceAgent": { "type": "string", "description": "Agent that produced the value" },
              "minScore": { "type": "number", "description": "Per-scenario quality threshold override (0-1)" }
            },
            "required": ["id", "key", "value", "sourceAgent"]
          },
          "description": "Array of scenarios to run through quality gates"
        },
        "qualityThreshold": {
          "type": "number",
          "description": "Global minimum quality score (0-1). Defaults to 0.7."
        }
      },
      "required": ["scenarios"]
    }
  }
]