Evidence row 291580

What was observed

{
  "count": 15,
  "tools": [
    {
      "description": "Analyzes combined multimodal prompts spanning text, image descriptors, audio transcripts, and video temporal events using Gemini Omni Flash. Use when correlating information across multiple media modalities in a single reasoning pass; for running pure text-only reasoning or coding prompts, use generate_gemini_completion instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'input' ('modality_focus' defaults to 'cross_modal_synthesis'), supports '?demo=true' or L402 (100 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "input": {
            "description": "Multimodal prompt or media scene description to analyze.",
            "type": "string"
          },
          "modality_focus": {
            "description": "Primary analytical lens applied to the multimodal input; defaults to 'cross_modal_synthesis' when omitted.",
            "enum": [
              "cross_modal_synthesis",
              "visual_ocr_extraction",
              "temporal_event_timeline"
            ],
            "type": "string"
          }
        },
        "required": [
          "input"
        ],
        "type": "object"
      },
      "name": "analyze_omni_multimodal_input"
    },
    {
      "description": "Compiles a structured JSON research dossier ('key_findings', 'synthesis_quality_score', 'sources_cited') settled via Base EVM Machine Payments Protocol (MPP). Use when generating a strict-JSON cited intelligence brief paid via Base USDC MPP session headers; for running long-form narrative research reports via L402 Lightning, use conduct_deep_research_investigation instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'research_topic' ('output_density' defaults to 'concise_5_points'), supports '?demo=true' or MPP (300 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "output_density": {
            "description": "Granularity of the key_findings array; defaults to 'executive_10_points' when omitted.",
            "enum": [
              "concise_5_points",
              "executive_10_points",
              "comprehensive_20_points"
            ],
            "type": "string"
          },
          "query": {
            "description": "Research topic or technical question to investigate and structure into JSON.",
            "type": "string"
          },
          "research_topic": {
            "description": "Alias for query: research topic or technical question to investigate.",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "compile_mpp_research_dossier"
    },
    {
      "description": "Composes studio-grade instrumental music tracks, ambient loops, and multi-instrument arrangements from genre, tempo, and mood prompts using Google Lyria 3. Use when generating musical scores, soundtrack stems, or rhythmic audio loops; for converting written text into spoken human voice narration (TTS), use synthesize_gemini_speech_audio instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('genre_preset' defaults to 'cinematic_synth'), supports '?demo=true' or L402 (150 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "genre_preset": {
            "description": "Primary acoustic or electronic genre; defaults to 'cinematic_synth' when omitted.",
            "enum": [
              "cinematic_synth",
              "lo_fi_hiphop",
              "orchestral_ambient",
              "industrial_techno"
            ],
            "type": "string"
          },
          "prompt": {
            "description": "Musical style, instrumentation, BPM tempo, and harmonic mood description.",
            "type": "string"
          }
        },
        "required": [
          "prompt"
        ],
        "type": "object"
      },
      "name": "compose_lyria_music_track"
    },
    {
      "description": "Runs an iterative, multi-hop investigative research session via Gemini Deep Research Pro, cross-examining primary sources to produce a cited technical report. Use when investigating complex scientific, architectural, or market questions requiring deep multi-hop synthesis; for running a fast single-turn Google Search grounded fact lookup, use search_gemini_grounded_web instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'input' ('depth_tier' defaults to 'quick_synthesis'), supports '?demo=true' or L402 (300 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "depth_tier": {
            "description": "Research breadth and citation density; defaults to 'exhaustive_report' when omitted.",
            "enum": [
              "quick_synthesis",
              "standard_brief",
              "exhaustive_report"
            ],
            "type": "string"
          },
          "input": {
            "description": "Investigative research question, scope, and specific aspects to analyze.",
            "type": "string"
          }
        },
        "required": [
          "input"
        ],
        "type": "object"
      },
      "name": "conduct_deep_research_investigation"
    },
    {
      "description": "Dispatches a complex engineering or operational task to Google's Antigravity autonomous agent for multi-step planning, tool orchestration, and verified execution. Use when solving multi-stage software engineering or workflow tasks that require autonomous planning; for compiling exhaustive multi-source literature and market research dossiers, use conduct_deep_research_investigation instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'input' ('execution_mode' defaults to 'autonomous_verified'), supports '?demo=true' or L402 (150 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "execution_mode": {
            "description": "Autonomy policy for subtask execution; defaults to 'autonomous_verified' when omitted.",
            "enum": [
              "autonomous_verified",
              "plan_only",
              "strict_sandbox"
            ],
            "type": "string"
          },
          "input": {
            "description": "High-level task objective and constraints for the Antigravity autonomous agent.",
            "type": "string"
          }
        },
        "required": [
          "input"
        ],
        "type": "object"
      },
      "name": "execute_antigravity_agent"
    },
    {
      "description": "Generates text completions and structured reasoning across all 45 Google Gemini foundation models (Gemini 4 Argon, 3.1 Pro, 3.8 Flash) with zero Google account or API key required. Use when running single-turn reasoning, coding, or analytical prompts on a specific Gemini model; for delegating multi-step complex tasks that require remote tool execution, use execute_antigravity_agent instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('model' defaults to 'gemini-4-argon'), supports '?demo=true' or L402 (50 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "model": {
            "description": "Target Gemini foundation model identifier; defaults to 'gemini-4-argon' when omitted.",
            "enum": [
              "gemini-4-argon",
              "gemini-3.1-pro",
              "gemini-3.8-flash",
              "gemini-2.5-pro"
            ],
            "type": "string"
          },
          "prompt": {
            "description": "Primary user instruction or reasoning prompt to send to the selected Gemini model.",
            "type": "string"
          }
        },
        "required": [
          "prompt"
        ],
        "type": "object"
      },
      "name": "generate_gemini_completion"
    },
    {
      "description": "Generates normalized 3,072-dimensional or 768-dimensional semantic vector embeddings via Google Gemini Embedding 2 for RAG indexing and cosine-similarity search. Use when embedding text passages or queries via L402 Lightning with configurable Matryoshka dimensions; for running live Google Search grounded factual queries, use search_gemini_grounded_web instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('model' defaults to 'gemini-embedding-2'), supports '?demo=true' or L402 (10 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "model": {
            "description": "Gemini embedding model version; defaults to 'gemini-embedding-2' when omitted.",
            "enum": [
              "gemini-embedding-2",
              "gemini-embedding-1"
            ],
            "type": "string"
          },
          "text": {
            "description": "Input text passage, code snippet, or search query to embed into a float vector.",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "type": "object"
      },
      "name": "generate_gemini_embeddings"
    },
    {
      "description": "Generates photorealistic illustrations, UI mockups, and technical diagrams from text prompts using the Nano Banana Pro (Gemini 2.5 Flash Image) model family. Use when creating static visual assets, icons, or architecture diagrams from text descriptions; for synthesizing animated multi-second video clips with motion, use generate_veo_cinematic_video instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('aspect_ratio' defaults to '1:1'), supports '?demo=true' or L402 (75 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "aspect_ratio": {
            "description": "Output image aspect ratio; defaults to '16:9' when omitted.",
            "enum": [
              "1:1",
              "16:9",
              "9:16",
              "4:3"
            ],
            "type": "string"
          },
          "prompt": {
            "description": "Detailed visual scene, composition, lighting, and typography description to render.",
            "type": "string"
          }
        },
        "required": [
          "prompt"
        ],
        "type": "object"
      },
      "name": "generate_nano_banana_image"
    },
    {
      "description": "Synthesizes high-definition cinematic video sequences with temporally coherent camera motion and physics using Google Veo 3. Use when generating moving video clips, product B-roll, or animated scene renders from text prompts; for rendering static single-frame images or diagrams at lower cost, use generate_nano_banana_image instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('motion_style' defaults to 'static_tripod'), supports '?demo=true' or L402 (650 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "motion_style": {
            "description": "Camera movement preset; defaults to 'smooth_tracking_shot' when omitted.",
            "enum": [
              "static_tripod",
              "smooth_tracking_shot",
              "drone_fpv_flyover",
              "macro_slow_motion"
            ],
            "type": "string"
          },
          "prompt": {
            "description": "Cinematic scene description including subject motion, camera trajectory, and lighting.",
            "type": "string"
          }
        },
        "required": [
          "prompt"
        ],
        "type": "object"
      },
      "name": "generate_veo_cinematic_video"
    },
    {
      "description": "Plans and executes OS-level desktop, browser DOM, and GUI coordinate actions (click, type, scroll, wait) using the Gemini Computer Use agent. Use when automating interactive web UIs or desktop workflows that lack a direct REST API; for running code-first API and terminal automation tasks, use execute_antigravity_agent instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'input' ('viewport_preset' defaults to 'desktop_1080p'), supports '?demo=true' or L402 (200 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "input": {
            "description": "UI automation goal and target webpage or application context.",
            "type": "string"
          },
          "viewport_preset": {
            "description": "Target screen resolution for coordinate grounding; defaults to 'desktop_1080p' when omitted.",
            "enum": [
              "desktop_1080p",
              "laptop_720p",
              "mobile_viewport"
            ],
            "type": "string"
          }
        },
        "required": [
          "input"
        ],
        "type": "object"
      },
      "name": "operate_computer_use_agent"
    },
    {
      "description": "Computes 3D spatial affordances, obstacle-avoidance waypoints, and manipulator grasp sequences using Gemini Robotics ER 2 embodied reasoning. Use when planning physical robot arm kinematics, mobile navigation waypoints, or spatial scene task orders; for interacting with 2D desktop or browser GUI screens, use operate_computer_use_agent instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('coordinate_frame' defaults to 'cartesian_xyz_meters'), supports '?demo=true' or L402 (150 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "coordinate_frame": {
            "description": "Reference coordinate convention; defaults to 'cartesian_xyz_meters' when omitted.",
            "enum": [
              "cartesian_xyz_meters",
              "joint_angles_radians",
              "normalized_voxel_grid"
            ],
            "type": "string"
          },
          "prompt": {
            "description": "Spatial environment description, obstacle coordinates, and physical manipulation goal.",
            "type": "string"
          }
        },
        "required": [
          "prompt"
        ],
        "type": "object"
      },
      "name": "plan_robotics_spatial_trajectory"
    },
    {
      "description": "Processes real-time conversational dialog turns with native acoustic emotion, interruption handling, and paralinguistic cue comprehension via Gemini 3.8 Live. Use when handling interactive voice-agent dialog turns that require tone and turn-taking awareness; for performing one-way text-to-speech readout of a static text string, use synthesize_gemini_speech_audio instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'input' ('response_tone' defaults to 'direct_peer'), supports '?demo=true' or L402 (75 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "input": {
            "description": "Conversational audio transcript or dialog turn payload to process.",
            "type": "string"
          },
          "response_tone": {
            "description": "Target paralinguistic delivery style; defaults to 'direct_peer' when omitted.",
            "enum": [
              "direct_peer",
              "concise_tactical",
              "empathetic_support"
            ],
            "type": "string"
          }
        },
        "required": [
          "input"
        ],
        "type": "object"
      },
      "name": "process_live_audio_dialogue"
    },
    {
      "description": "Executes geospatial and point-of-interest queries grounded in live Google Maps data, returning verified addresses, coordinates, operating hours, and spatial context. Use when resolving physical locations, venues, transit distances, or regional infrastructure data; for searching general web articles, documentation, and news, use search_gemini_grounded_web instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('distance_units' defaults to 'metric_km'), supports '?demo=true' or L402 (75 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "distance_units": {
            "description": "Preferred unit system for distances; defaults to 'metric_km' when omitted.",
            "enum": [
              "metric_km",
              "imperial_miles"
            ],
            "type": "string"
          },
          "prompt": {
            "description": "Geospatial query, place name, or regional routing question.",
            "type": "string"
          }
        },
        "required": [
          "prompt"
        ],
        "type": "object"
      },
      "name": "search_gemini_grounded_maps"
    },
    {
      "description": "Answers factual queries with live Google Search grounding, returning verified up-to-date answers accompanied by source URLs and grounding metadata. Use when verifying live web facts, documentation updates, or current events in a single low-latency call; for querying geographic places, coordinates, directions, and local business data via Google Maps grounding, use search_gemini_grounded_maps instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('recency_bias' defaults to 'past_24h'), supports '?demo=true' or L402 (75 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "prompt": {
            "description": "Factual question or search query to ground against live Google Search results.",
            "type": "string"
          },
          "recency_bias": {
            "description": "Temporal preference for search snippets; defaults to 'any_time' when omitted.",
            "enum": [
              "past_24h",
              "past_week",
              "past_year",
              "any_time"
            ],
            "type": "string"
          }
        },
        "required": [
          "prompt"
        ],
        "type": "object"
      },
      "name": "search_gemini_grounded_web"
    },
    {
      "description": "Converts written text into natural, prosody-controlled spoken voice audio using Gemini 3.8 Flash Neural Text-to-Speech. Use when rendering voiceover narration or spoken agent replies from a deterministic text script; for conducting bidirectional conversational audio dialog comprehension, use process_live_audio_dialogue instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('voice_persona' defaults to 'neutral_broadcast'), supports '?demo=true' or L402 (50 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "text": {
            "description": "Exact written script or paragraph to synthesize into spoken voice audio.",
            "type": "string"
          },
          "voice_persona": {
            "description": "Expressive vocal timbre preset; defaults to 'neutral_broadcast' when omitted.",
            "enum": [
              "neutral_broadcast",
              "warm_conversational",
              "technical_lecturer",
              "urgent_alert"
            ],
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "type": "object"
      },
      "name": "synthesize_gemini_speech_audio"
    }
  ]
}

What the leaf hashes

{"body":"df582783c355491ad7c4e91f258567252e10b502e712351343c6feb757f1d748","id":291580,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793048185289849,"prev":null,"subject":"877842f1-bf29-4105-8b45-ebbc06b27006","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. 0xa9f3769e27d08a82a45982494deaa2be1130c5ab8f728df5d1b0efc8df915e86
2. 0xb68cbbffd83dbd33dc2eb5917f3ba51171161c1184e2bae697c8498c155d876c
3. 0xb54ee0d778bfd63cf0ccb60d4e483c2811e3268e6cd7c65e5806a4e2c799efc5
4. 0xb4d98e2aa3ca65a94327d4125f32339a46791ece0c3cf4d40a25dc727c817f8d
5. 0xb88bb18676b891a342495ac4495dde4f5891bafb31ccf89bec43a705936b0e33
6. 0x3b494f5e0170886a9c97f326c038578a5933b005953c5181bba72062ff654339
7. 0xc6e628ea5491574784b452f65fefe6fc3ff7fc9c368e271e13535453e4eb3fda
8. 0x8f14ab6c88413edb8a4c4942f3c7022cc8f31824d3dcf1c22f0b35b7f531ee73
9. 0xc580924a5a23f25de4357c17fc84cd3fafa3d3afccfd4e18ea2cb141221341eb
10. 0x382774117c6a99ced4320ca176d4e72cc7226f2fa6e3d8ac0173511fe55d6919
11. 0x55ba80743bf45624f0b04651dfd0a37e2a85f91f45e334ff14efbb74a49dd361
12. 0x09a19b8d31a6e948e5051cb72b98b1d2b2bfe053f62d6d24d61c22b3ed1c3f24
13. 0x4700d6dfdb40e52e2ceb8ffe62c75f3f708d23452f828255fd003d795f5d9d66
14. 0x3b4dba7d874fc323b0a364140a9c60f37bf547fe671897653118a9873fe03420
15. 0x1a74ed3b3756b9cd946da49701bcec120ec88645013da99fa9eea60b39e1a570
16. 0x123ec8bee20ddc55223eeef5a4f4d6c4b6416515adbb911c56759f5cde56e452
17. 0xd0651cb9bc8155eb52b5247d506e74b8a935057dc190ba52091702ac25ed3a40
18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae