io.github.jdhart81/neurogenesis

Official14 toolsLive

by jdhart81 · Python

Developmental agents for the agent economy: create an agent from a digital genome, then evolve it wi

Model Context Protocol (MCP) Server: io.github.jdhart81/neurogenesis

The MCP server “io.github.jdhart81/neurogenesis” provides developmental agent services for creating an agent from a digital genome and then evolving it. Its README excerpt describes autonomous pay-per-call HTTP routes supporting a carbon and compliance workflow and multi-agent problem solving, with structured JSON responses.

🛠️ Key Features

Developmental agents: create an agent from a digital genome, then evolve it
13 tools/routes (toolCount: 13)
Seven deterministic paid HTTP routes for a five-step workflow: measure → account → disclose → claim → scan
Includes multi-agent problem solving and an MCP security preflight
Paid routes verify and settle before execution; returns structured JSON plus a payment receipt

🚀 Use Cases

Carbon and compliance processing across five workflow steps
Multi-agent problem solving under MCP security preflight

⚡ Developer Benefits

Structured JSON responses with payment receipts
Deterministic, paid HTTP route behavior with verification and settlement

⚠️ Limitations

Paid access: first paid call from a new wallet is $0.01; routing pricing follows unchanged list prices thereafter
Workflow and routing described specifically in the README excerpt (e.g., “Safest first paid call” selects only Regulatory Rada)

Topics

agent-securityai-agentsdeveloper-toolsmcpmcp-serverpolicy-as-codepythonsecurity-toolsx402

Captured live from the server via tools/list.

create_agent

Create a developmental agent from a digital genome: {agent_name, purpose, initial_nodes (unique, >=1), fitness_metrics (>=1), optional growth_rules / safety_axioms}. Returns agent_id + initial graph summary.

Parameters2

genome	object	required
request_id	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "genome": {
      "additionalProperties": true,
      "title": "Genome",
      "type": "object"
    },
    "request_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Id"
    }
  },
  "required": [
    "genome"
  ],
  "title": "create_agentArguments"
}

submit_evaluation

Evolve an agent with one task outcome: {task_id, task_type, success_score in [0,1], optional accuracy/user_satisfaction/ cost_efficiency/safety_score/notes/used_nodes/used_edges}. Success strengthens the used edges, failure weakens them; growth and pruning follow the genome's rules under its safety axioms (NG1). Returns the new developmental-ledger events.

Parameters3

agent_id	string	required
evaluation	object	required
request_id	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "title": "Agent Id",
      "type": "string"
    },
    "evaluation": {
      "additionalProperties": true,
      "title": "Evaluation",
      "type": "object"
    },
    "request_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Id"
    }
  },
  "required": [
    "agent_id",
    "evaluation"
  ],
  "title": "submit_evaluationArguments"
}

get_agent

Full current state: genome, cognitive graph (nodes/edges with weights and trust), and summary counts.

Parameters1

agent_id

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "title": "Agent Id",
      "type": "string"
    }
  },
  "required": [
    "agent_id"
  ],
  "title": "get_agentArguments"
}

list_agents

All developmental agents on this mount, with summary counts.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "list_agentsArguments"
}

best_next_steps

Routing recommendation: the strongest next cognitive steps from a given node, by learned edge weight and trust.

Parameters3

agent_id	string	required
from_node	string	required
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "title": "Agent Id",
      "type": "string"
    },
    "from_node": {
      "title": "From Node",
      "type": "string"
    },
    "limit": {
      "default": 3,
      "title": "Limit",
      "type": "integer"
    }
  },
  "required": [
    "agent_id",
    "from_node"
  ],
  "title": "best_next_stepsArguments"
}

get_ledger

The append-only developmental ledger: every growth, pruning, and evaluation event with reasons (NG3 — returned verbatim).

Parameters2

agent_id	string	required
limit	integer	optional

Raw schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "title": "Agent Id",
      "type": "string"
    },
    "limit": {
      "default": 100,
      "title": "Limit",
      "type": "integer"
    }
  },
  "required": [
    "agent_id"
  ],
  "title": "get_ledgerArguments"
}

export_state

Portable state document for an agent (import_state recreates it anywhere — including a self-hosted verdigraph-neurogenesis).

Parameters1

agent_id

string

required

Raw schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "title": "Agent Id",
      "type": "string"
    }
  },
  "required": [
    "agent_id"
  ],
  "title": "export_stateArguments"
}

import_state

Recreate an agent from an export_state document.

Parameters2

state	object	required
request_id	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "state": {
      "additionalProperties": true,
      "title": "State",
      "type": "object"
    },
    "request_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Id"
    }
  },
  "required": [
    "state"
  ],
  "title": "import_stateArguments"
}

delete_agent

Remove a developmental agent from this mount.

Parameters2

agent_id	string	required
request_id	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "title": "Agent Id",
      "type": "string"
    },
    "request_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Id"
    }
  },
  "required": [
    "agent_id"
  ],
  "title": "delete_agentArguments"
}

register_compute_profile

Register a caller-owned Wu Wei execution profile: reuse/cache, deterministic rule, local model, tool/workflow, or cloud model. Profiles can declare quality, reliability, capabilities, token costs, latency, locality, energy rates or average power, carbon intensity, and cache confidence/age. Routing never invents capacity or energy evidence.

Parameters2

profile	object	required
request_id	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "profile": {
      "additionalProperties": true,
      "title": "Profile",
      "type": "object"
    },
    "request_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Id"
    }
  },
  "required": [
    "profile"
  ],
  "title": "register_compute_profileArguments"
}

route_task

Choose the least-burden eligible route for a task. Hard constraints include quality, reliability, locality, capabilities, context, cost, latency, and energy. Optional baselines quantify predicted savings. Explicit allow_defer/value/urgency fields may produce a no-work decision; no result is then claimed. Returns a hash-bound decision receipt.

Parameters2

task	object	required
request_id	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "task": {
      "additionalProperties": true,
      "title": "Task",
      "type": "object"
    },
    "request_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Id"
    }
  },
  "required": [
    "task"
  ],
  "title": "route_taskArguments"
}

record_route_outcome

Attach one observed outcome to a Wu Wei decision. Actual cost, latency, and energy are optional and remain explicitly unknown when omitted. One append-only, hash-bound outcome is allowed per decision.

Parameters7

decision_id	string	required
success_score	number	required
actual_cost_usd	any	optional
actual_latency_ms	any	optional
actual_energy_wh	any	optional
notes	string	optional
request_id	any	optional

Raw schema

{
  "type": "object",
  "properties": {
    "decision_id": {
      "title": "Decision Id",
      "type": "string"
    },
    "success_score": {
      "title": "Success Score",
      "type": "number"
    },
    "actual_cost_usd": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Actual Cost Usd"
    },
    "actual_latency_ms": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Actual Latency Ms"
    },
    "actual_energy_wh": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Actual Energy Wh"
    },
    "notes": {
      "default": "",
      "title": "Notes",
      "type": "string"
    },
    "request_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Request Id"
    }
  },
  "required": [
    "decision_id",
    "success_score"
  ],
  "title": "record_route_outcomeArguments"
}

compute_efficiency_report

Free read: decision/outcome receipts, route modes, compute avoided, predicted savings, predicted-vs-observed cost/latency/energy coverage, and Landauer-floor context. Estimates and observations stay distinct.

Parameters1

limit

integer

optional

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "default": 50,
      "title": "Limit",
      "type": "integer"
    }
  },
  "title": "compute_efficiency_reportArguments"
}

describe_agent

Return capabilities and input contract.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "title": "describe_agentArguments"
}

README.md2 KBView on GitHub

Viridis Agent Fleet: open-source agents for the agent economy

Deterministic, auditable MCP agents: trust and settlement rails (identity, trust, escrow, metering, arbitration, notary, surety, provenance, covenant, offsets, compute-ledger) and priced-grade services (tax credits, GHG accounting, construction takeoff, measurement, compliance). Every core is pure Python with no LLM in the serving path, and every result is content-addressed so anyone can re-verify it.

Licensed Apache-2.0. Run any agent yourself, fork it, embed it.

Run an agent locally (free, forever)

bash

pip install -r requirements-dev.txt
python3 agent-escrow-agent/adapters/mcp_server.py          # stdio MCP
python3 run_fleet_tests.py                                 # every agent's invariant tests

Or use the hosted fleet

https://mcp.viridisconservation.com runs all agents as one managed MCP service:

	Self-hosted (this repo)	Hosted by Viridis
Agent logic & verification	✅ identical	✅ identical
Viridis-signed receipts & notary chain	–	✅
Shared trust/reputation network data	–	✅
Maintained rule packs (tax, GHG factors, regs)	snapshot	✅ kept current
Payments (x402, card), seats, SLA	–	✅

Free tier: 10 calls/day per service; the rails (identity, escrow, metering…) are free forever. Verification is always free.

Boundary

This repo contains agent cores, MCP adapters, tests, standards and self-host tooling. The hosted service layer (gateway, payment and settlement, seats, signing authority, operations, telemetry and customer data) is proprietary and is not in this repo. Please don't open PRs that add it.

"Viridis", "Viridis Conservation" and the Viridis marks are trademarks of Viridis LLC; see TRADEMARKS.md.

Install

Tools (14)

create_agentsubmit_evaluationget_agentlist_agentsbest_next_stepsget_ledgerexport_stateimport_state

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://mcp.viridisconservation.com/neurogenesis/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "neurogenesis": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.viridisconservation.com/neurogenesis/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.

Model Context Protocol (MCP) Server: io.github.jdhart81/neurogenesis

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

create_agent

submit_evaluation

get_agent

list_agents

best_next_steps

get_ledger

export_state

import_state

delete_agent

register_compute_profile

route_task

record_route_outcome

compute_efficiency_report

describe_agent

Viridis Agent Fleet: open-source agents for the agent economy

Run an agent locally (free, forever)

Or use the hosted fleet

Boundary

Install

Remote endpoint

Related servers