Evidence row 583551

What was observed

{
  "count": 10,
  "tools": [
    {
      "description": "Add a text document to the authenticated user's knowledge base. The content is chunked, embedded, and stored so it becomes retrievable via rag_search.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "content": {
            "description": "The text content to ingest. It is chunked, embedded, and made retrievable via rag_search.",
            "type": "string"
          },
          "group_key": {
            "description": "Optional group key to scope the document. Pass the same value to rag_search to retrieve only this group; omit it to search the whole corpus.",
            "type": [
              "string",
              "null"
            ]
          },
          "name": {
            "description": "A human-readable name/title for the document.",
            "type": "string"
          }
        },
        "required": [
          "name",
          "content"
        ],
        "type": "object"
      },
      "name": "file_ingest"
    },
    {
      "description": "Return the messages of one of the user's chats, in chronological order.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "chat_id": {
            "description": "The id of the chat (from list_chats) to read messages from.",
            "type": "integer"
          },
          "limit": {
            "default": 50,
            "description": "Maximum number of (most recent) messages to return (1-100).",
            "maximum": 100,
            "minimum": 1,
            "type": "integer"
          }
        },
        "required": [
          "chat_id"
        ],
        "type": "object"
      },
      "name": "get_messages"
    },
    {
      "description": "List assistants the authenticated user may use from a connected app. Each row includes slug, name, description, version and origin.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "list_assistants"
    },
    {
      "description": "List the authenticated user's conversations (most recently updated first).",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "limit": {
            "default": 50,
            "description": "Maximum number of chats to return (1-200).",
            "maximum": 200,
            "minimum": 1,
            "type": "integer"
          }
        },
        "type": "object"
      },
      "name": "list_chats"
    },
    {
      "description": "List the user's available Synaplan task prompts (internal `tools:*` prompts excluded).",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "list_prompts"
    },
    {
      "description": "Store a new long-term memory for the authenticated user — a durable fact or preference that Synaplan will recall in future conversations.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "category": {
            "description": "Category for the memory, e.g. preferences, work, personal, health.",
            "type": "string"
          },
          "key": {
            "description": "Short identifier for the memory (min 3 characters), e.g. tech_stack.",
            "minLength": 3,
            "type": "string"
          },
          "value": {
            "description": "The fact or preference to remember.",
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "category",
          "key",
          "value"
        ],
        "type": "object"
      },
      "name": "memory_add"
    },
    {
      "description": "Search the authenticated user's stored long-term memories (preferences, facts, context) using semantic similarity.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "category": {
            "description": "Optional category filter (e.g. preferences, work, personal).",
            "type": [
              "string",
              "null"
            ]
          },
          "limit": {
            "default": 5,
            "description": "Maximum number of memories to return (1-50).",
            "maximum": 50,
            "minimum": 1,
            "type": "integer"
          },
          "query": {
            "description": "Natural-language query describing the memory to look up.",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "memory_search"
    },
    {
      "description": "Semantic search across the authenticated user's vectorized documents and knowledge base. Returns the most relevant text chunks with similarity scores.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "group_key": {
            "description": "Optional document group key to scope the search to a single document/collection.",
            "type": [
              "string",
              "null"
            ]
          },
          "limit": {
            "default": 10,
            "description": "Maximum number of chunks to return (1-50).",
            "maximum": 50,
            "minimum": 1,
            "type": "integer"
          },
          "min_score": {
            "default": 0.3,
            "description": "Minimum similarity score (0-1) a chunk must reach to be returned.",
            "maximum": 1,
            "minimum": 0,
            "type": "number"
          },
          "query": {
            "description": "Natural-language search query.",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "rag_search"
    },
    {
      "description": "Find document chunks similar to a given source message/document id (the `message_id` returned by rag_search).",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "limit": {
            "default": 10,
            "description": "Maximum number of similar chunks to return (1-50).",
            "maximum": 50,
            "minimum": 1,
            "type": "integer"
          },
          "message_id": {
            "description": "The source document/message id to find similar chunks for (use a `message_id` returned by rag_search).",
            "type": "integer"
          }
        },
        "required": [
          "message_id"
        ],
        "type": "object"
      },
      "name": "rag_similar"
    },
    {
      "description": "Send a message through Synaplan's full AI pipeline — intent classification, web search, RAG over the user's documents, long-term memories, and inference — and return the synthesized answer. This is richer than a raw model call: it uses the authenticated account's knowledge base and memories.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "agentId": {
            "description": "Optional assistant id or slug (see list_assistants). Pins the reply to that assistant; fails when it is not published, not usable by you, or does not allow connected apps.",
            "type": [
              "string",
              "integer",
              "null"
            ]
          },
          "chat_id": {
            "description": "Optional existing chat id (from list_chats) to continue a conversation. Omit to start a new chat.",
            "type": [
              "integer",
              "null"
            ]
          },
          "message": {
            "description": "The message or question to send through Synaplan's full AI pipeline (classification, web search, RAG over the user's documents, memories, and inference).",
            "type": "string"
          }
        },
        "required": [
          "message"
        ],
        "type": "object"
      },
      "name": "synaplan_chat"
    }
  ]
}

What the leaf hashes

{"body":"dcbd4b3956ea404a68a54a497f30a862f42fe96136d860f0fcfb5b72996abc4a","id":583551,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793145672545498,"prev":null,"subject":"6b00068e-f1cb-411b-84e0-6f57b07ac553","subject_kind":"endpoint"}

Proof

Its day has not closed yet. A day closes ten minutes after midnight UTC; the proof appears here then.