Evidence row 295818

What was observed

{
  "count": 4,
  "tools": [
    {
      "description": "Return rating dimensions, explanations, penalties, and boosts for one tool_id.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "additionalProperties": false,
        "properties": {
          "tool_id": {
            "description": "Stable Agent Radar tool identifier, usually obtained from search_tools results.",
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "tool_id"
        ],
        "type": "object"
      },
      "name": "explain_rating"
    },
    {
      "description": "Return one Tool Card and its Rating Result by stable tool_id.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "additionalProperties": false,
        "properties": {
          "tool_id": {
            "description": "Stable Agent Radar tool identifier, usually obtained from search_tools results.",
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "tool_id"
        ],
        "type": "object"
      },
      "name": "get_tool_card"
    },
    {
      "description": "Recommend known tools for a task using request-scoped LLM credentials when required.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "additionalProperties": false,
        "properties": {
          "allowed_permissions": {
            "description": "Permission scope names the caller is willing to allow; candidates outside these boundaries are treated conservatively.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "budget": {
            "description": "Natural-language cost constraint, such as free, free_or_low_cost, or a project-specific budget.",
            "type": "string"
          },
          "environment": {
            "description": "Execution contexts such as local development, CI, browser, cloud, IDE, or production.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "existing_tools": {
            "description": "Tools already available to the project or agent, used as compatibility and duplication context.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "language_or_stack": {
            "description": "Programming languages, frameworks, runtimes, or other stack constraints relevant to the task.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "model": {
            "description": "LLM provider model identifier recognized by Agent Radar. Omit it to use the server-configured or registry default model.",
            "minLength": 1,
            "type": "string"
          },
          "output_format": {
            "description": "Preferred presentation format for recommendation content: json or markdown. MCP still returns structured content.",
            "enum": [
              "json",
              "markdown"
            ],
            "type": "string"
          },
          "preferred_tool_types": {
            "description": "Agent Radar tool types to prefer when selecting candidates.",
            "items": {
              "enum": [
                "mcp",
                "skill",
                "agent",
                "framework",
                "cli",
                "prompt",
                "rules",
                "dataset",
                "service"
              ],
              "type": "string"
            },
            "type": "array"
          },
          "risk_tolerance": {
            "description": "Maximum preferred risk tolerance for the recommendation: low, medium, or high.",
            "enum": [
              "low",
              "medium",
              "high"
            ],
            "type": "string"
          },
          "task": {
            "description": "Natural-language development task for which Agent Radar should recommend suitable AI tools.",
            "minLength": 1,
            "type": "string"
          },
          "top_k": {
            "description": "Maximum number of recommended candidates to return, from 1 to 50. Defaults to 5 when omitted.",
            "maximum": 50,
            "minimum": 1,
            "type": "integer"
          }
        },
        "required": [
          "task"
        ],
        "type": "object"
      },
      "name": "recommend_tools"
    },
    {
      "description": "Search Agent Radar Tool Cards by query text and optional type, tag, or risk filters.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "additionalProperties": false,
        "properties": {
          "filters": {
            "additionalProperties": false,
            "description": "Optional exact-match filters applied before search ranking.",
            "properties": {
              "risk_level": {
                "description": "Exact assessed risk level to include: low, medium, high, critical, or unknown.",
                "enum": [
                  "low",
                  "medium",
                  "high",
                  "critical",
                  "unknown"
                ],
                "type": "string"
              },
              "tags": {
                "description": "Exact catalog tags that every returned tool must contain.",
                "items": {
                  "type": "string"
                },
                "type": "array"
              },
              "type": {
                "description": "Exact Agent Radar tool type to include, such as mcp, skill, agent, framework, or cli.",
                "enum": [
                  "mcp",
                  "skill",
                  "agent",
                  "framework",
                  "cli",
                  "prompt",
                  "rules",
                  "dataset",
                  "service"
                ],
                "type": "string"
              }
            },
            "type": "object"
          },
          "query": {
            "default": "",
            "description": "Free-text tool name, capability, or task to search for. Use an empty string to browse the highest-rated matching catalog entries.",
            "type": "string"
          },
          "top_k": {
            "default": 5,
            "description": "Maximum number of results to return, from 1 to 50. Defaults to 5.",
            "maximum": 50,
            "minimum": 1,
            "type": "integer"
          }
        },
        "type": "object"
      },
      "name": "search_tools"
    }
  ]
}

What the leaf hashes

{"body":"c32f963a7e554762457d54d61b2ee3873582b81be6f5352f9fdd584dbd95d52a","id":295818,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793051186782997,"prev":null,"subject":"0986e1a7-90ea-4b93-b3c2-0029149f134c","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-26 (296695 rows).

1. 0x372e826264c79108e281dcb2bb2fc5994a896f929634d612b4651a93e7e6aefa
2. 0xc4704a568cf990efd95e8caaa82de5b3f5e64cd0f3e698d1a5729a4ca0e11521
3. 0xd287c7b33d820b44182b0716d4fd731a37b25e54c2e46dbfbc3d0d2f605887fe
4. 0xc7a71229ac9f7d2983e1e08aec1b12e0036287bd9681b74469573f4310f0ea6c
5. 0x3f2d7c850e4a2b2cf55aa129e4c0ad19af31fa78872dc2e58cdff2b9e947b0c4
6. 0x4ac8b6a48181c0007d1349eb7e38968fc0756471b919e6bc5a1d2a071e81a5a4
7. 0x3df80040a1fbbac2cfbe93e624095694d4e8aceb9f5f8eafa8ad80a98eea2d98
8. 0xd160a10c573b9a4c5e85fde3cd927fa2eb48ea3a119c783f7946aad17df52eb8
9. 0xccde764a9dad94e012262c23cb26faf6ab3ad6398eb6de621d7359132d68c0af
10. 0x8681b8a904a71605e6e13e7c5c8ece78e783c5629075c54dc57234a15410c0d8
11. 0x92b5da9306740c6310c9590169e789efc810ad3fa152b365777b6395fec4767f
12. 0x8b23d34f6cd746f123b1e4d09449280800a81e00febe1fd9224b6802057238d5
13. 0xdfc7a920beab71e6882e75c57bf136a5354c4ea989a35108a69046bc62059090
14. 0x4338355a835617a26aa0bfcc971e84eb030d84e2aa840e3c8af958921dd080c7
15. 0x610738e0c43687aded83c613c73de8b5226c73c375858a167436461c522ea213
16. 0x06d71bfa7d8b41d62c8836efc2c601e1cc3772d9453596b2ae4950887f3310ca
17. 0x8c756542e482157df0c36464d75c937b845e4423dbbcfad08f0cf8d5a16b9a13
18. 0x20704208aa6923d6b2450ef74ede87a6e0265d46447926038fe9506e9e154b2d
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae