_ registry / mcp streamable-http · checked 58m ago

ZizkaDB

https://db.zizka.ai

Registry code: 14e9ae7247f01c16

api record

ZizkaDB is causal memory and a tamper-evident audit log for AI agents. Log each action with log_event and pass the returned event_id as parent_id on the next event, so why() can trace decisions later.

endpoint
https://db.zizka.ai/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is ZizkaDB live?
Yes — it answered the hub's last check (checked 58m ago). It answered 100% of checks over the last 30 days.
Is ZizkaDB free to use?
Not measured yet.
What tools does ZizkaDB have?
12 tools: memory_diff, log_event, search_memory, verify_chain, get_context, why, query_events, time_travel, ….
Is ZizkaDB safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
108ms

last good check

priced tools
0

of 12 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 12 tools
12 never probed 0 of 12 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • memory_diff unknown never probed

    Summarise what happened in a session. Args: session_id: The session to analyse Returns: summary, event_types, total_events, has_errors, new_event_types

    mcp-tool

    {
  "type": "object",
  "title": "memory_diffArguments",
  "required": [
    "session_id"
  ],
  "properties": {
    "session_id": {
      "type": "string",
      "title": "Session Id"
    }
  }
}
    arguments 13 lines
  • log_event unknown never probed

    Log an event to ZizkaDB. Use this every time your agent takes an action: a tool call, a decision, a user message, or an agent response. Logging builds the causal graph that powers why(), search_memory(), and get_context(). Pass the event_id this returns as parent_id on the next event: the hosted server does not chain events across calls by itself. Args: agent: Unique identifier for your agent (e.g. "support-bot"): letters, numbers, dots, dashes and underscores, starting with a letter or number event: What happened (e.g. "tool_call", "user_message", "decision") data: Any dict of relevant data (tool name, query, result, etc.) session_id: Groups related events into a session (optional) parent_id: event_id of the event that caused this one -- enables causal lineage (optional) Returns: event_id, timestamp, sequence_no, checksum

    mcp-tool

    {
  "type": "object",
  "title": "log_eventArguments",
  "required": [
    "agent",
    "event",
    "data"
  ],
  "properties": {
    "data": {
      "type": "object",
      "title": "Data",
      "additionalProperties": true
    },
    "agent": {
      "type": "string",
      "title": "Agent"
    },
    "event": {
      "type": "string",
      "title": "Event"
    },
    "parent_id": {
      "type": "string",
      "title": "Parent Id",
      "default": ""
    },
    "session_id": {
      "type": "string",
      "title": "Session Id",
      "default": ""
    }
  }
}
    arguments 34 lines
  • search_memory unknown never probed

    Search over all logged agent events. Finds past decisions, tool calls, conversations, or any event. Keyword search, or keyword + meaning (hybrid) when embeddings are on. Useful before a new task to see what happened in similar situations. Args: query: Natural language description of what you're looking for agent: Filter to a specific agent (optional -- leave blank to search all) limit: Number of results (default 10, max 50) mode: Leave blank for the server default, or "keyword", "semantic", "hybrid" Returns: List of matching events with relevance scores

    mcp-tool

    {
  "type": "object",
  "title": "search_memoryArguments",
  "required": [
    "query"
  ],
  "properties": {
    "mode": {
      "type": "string",
      "title": "Mode",
      "default": ""
    },
    "agent": {
      "type": "string",
      "title": "Agent",
      "default": ""
    },
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 10
    },
    "query": {
      "type": "string",
      "title": "Query"
    }
  }
}
    arguments 28 lines
  • verify_chain unknown never probed

    Prove an agent's history is untouched. Rechecks every event's checksum and its link in the agent's hash chain. "intact": true means no event was edited, removed or reordered (GDPR deletions show up as tombstones, not breaks). Keep "chain_head" as proof of the history at this moment. Args: agent: The agent whose history to check Returns: intact, a summary, any problems found, and chain_head

    mcp-tool

    {
  "type": "object",
  "title": "verify_chainArguments",
  "required": [
    "agent"
  ],
  "properties": {
    "agent": {
      "type": "string",
      "title": "Agent"
    }
  }
}
    arguments 13 lines
  • get_context unknown never probed

    Get a formatted memory context block ready to inject into a system prompt. Combines recent events with relevant past events, fits within a token budget, and formats as plain text. Args: agent: Your agent identifier task: What the agent is about to do (guides retrieval) max_tokens: Token budget for the context block (default 2000) session_id: Current session ID to exclude from context (optional) Returns: Formatted context string -- paste directly into your system prompt

    mcp-tool

    {
  "type": "object",
  "title": "get_contextArguments",
  "required": [
    "agent",
    "task"
  ],
  "properties": {
    "task": {
      "type": "string",
      "title": "Task"
    },
    "agent": {
      "type": "string",
      "title": "Agent"
    },
    "max_tokens": {
      "type": "integer",
      "title": "Max Tokens",
      "default": 2000
    },
    "session_id": {
      "type": "string",
      "title": "Session Id",
      "default": ""
    }
  }
}
    arguments 28 lines
  • why unknown never probed

    Trace the causal chain that led to an event. Given any event ID, walks back through parent_id links to reconstruct the full decision tree: user message -> tool call -> result -> response. Args: event_id: The event you want to explain depth: How many levels back to trace (default 10) Returns: Ordered list of events from root cause to the specified event

    mcp-tool

    {
  "type": "object",
  "title": "whyArguments",
  "required": [
    "event_id"
  ],
  "properties": {
    "depth": {
      "type": "integer",
      "title": "Depth",
      "default": 10
    },
    "event_id": {
      "type": "string",
      "title": "Event Id"
    }
  }
}
    arguments 18 lines
  • query_events unknown never probed

    List recent events for an agent. Args: agent: Agent identifier to query limit: Number of events to return (default 20) event_type: Filter by event type, e.g. "tool_call" (optional) Returns: List of events ordered by time (most recent first)

    mcp-tool

    {
  "type": "object",
  "title": "query_eventsArguments",
  "required": [
    "agent"
  ],
  "properties": {
    "agent": {
      "type": "string",
      "title": "Agent"
    },
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 20
    },
    "event_type": {
      "type": "string",
      "title": "Event Type",
      "default": ""
    }
  }
}
    arguments 23 lines
  • time_travel unknown never probed

    Replay the exact state of an agent at a specific point in time. Args: agent: Agent identifier timestamp: ISO 8601 datetime, e.g. "2026-05-10T15:00:00Z" Returns: Agent state snapshot at that moment (events, summary, last_event)

    mcp-tool

    {
  "type": "object",
  "title": "time_travelArguments",
  "required": [
    "agent",
    "timestamp"
  ],
  "properties": {
    "agent": {
      "type": "string",
      "title": "Agent"
    },
    "timestamp": {
      "type": "string",
      "title": "Timestamp"
    }
  }
}
    arguments 18 lines
  • get_baseline unknown never probed

    Behavioral baseline and drift score for an agent.

    mcp-tool

    {
  "type": "object",
  "title": "get_baselineArguments",
  "required": [
    "agent"
  ],
  "properties": {
    "agent": {
      "type": "string",
      "title": "Agent"
    },
    "window": {
      "type": "string",
      "title": "Window",
      "default": ""
    },
    "recent_window": {
      "type": "integer",
      "title": "Recent Window",
      "default": 50
    }
  }
}
    arguments 23 lines
  • get_token_usage unknown never probed

    Token usage and cost report for an agent over a date range.

    mcp-tool

    {
  "type": "object",
  "title": "get_token_usageArguments",
  "required": [
    "agent",
    "from_",
    "to"
  ],
  "properties": {
    "to": {
      "type": "string",
      "title": "To"
    },
    "agent": {
      "type": "string",
      "title": "Agent"
    },
    "from_": {
      "type": "string",
      "title": "From"
    },
    "granularity": {
      "type": "string",
      "title": "Granularity",
      "default": ""
    }
  }
}
    arguments 28 lines
  • get_token_optimization unknown never probed

    Deterministic token optimization suggestions from real usage events.

    mcp-tool

    {
  "type": "object",
  "title": "get_token_optimizationArguments",
  "required": [
    "agent"
  ],
  "properties": {
    "to": {
      "type": "string",
      "title": "To",
      "default": ""
    },
    "agent": {
      "type": "string",
      "title": "Agent"
    },
    "from_": {
      "type": "string",
      "title": "From",
      "default": ""
    },
    "granularity": {
      "type": "string",
      "title": "Granularity",
      "default": ""
    }
  }
}
    arguments 28 lines
  • forget unknown never probed

    Delete all events matching a filter (GDPR right to erasure). Args: filter_key: Field in event data to match (e.g. "user_id", "email", "session_id") filter_value: Value to match (e.g. "user_123", "[email protected]") Returns: deleted_events count

    mcp-tool

    {
  "type": "object",
  "title": "forgetArguments",
  "required": [
    "filter_key",
    "filter_value"
  ],
  "properties": {
    "filter_key": {
      "type": "string",
      "title": "Filter Key"
    },
    "filter_value": {
      "type": "string",
      "title": "Filter Value"
    }
  }
}
    arguments 18 lines
_ try it through the hub, ceiling 0

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_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
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settled without a human
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earned
0 USDC
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upheld
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reviews
paid reviews
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positive
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score
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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.