MCP server

Neurogenesis

io.github.jdhart81/neurogenesis

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

14

Token cost

About 1,863 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http neurogenesis https://mcp.viridisconservation.com/neurogenesis/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.neurogenesis]
url = "https://mcp.viridisconservation.com/neurogenesis/mcp"

Gemini CLI

{"mcpServers":{"neurogenesis":{"httpUrl":"https://mcp.viridisconservation.com/neurogenesis/mcp"}}}

Cursor

{"mcpServers":{"neurogenesis":{"url":"https://mcp.viridisconservation.com/neurogenesis/mcp"}}}

VS Code

{"servers":{"neurogenesis":{"type":"http","url":"https://mcp.viridisconservation.com/neurogenesis/mcp"}}}

Antigravity

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

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"neurogenesis","server_url":"https://mcp.viridisconservation.com/neurogenesis/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"neurogenesis","server_url":"https://mcp.viridisconservation.com/neurogenesis/mcp"}

Tools

• best_next_steps ~124 tokens
  Routing recommendation: the strongest next cognitive steps from a given node, by learned edge weight and trust.
• compute_efficiency_report ~120 tokens
  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.
• create_agent ~152 tokens
  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.
• delete_agent Deletes data · ~95 tokens
  Remove a developmental agent from this mount.
• export_state ~92 tokens
  Portable state document for an agent (import_state recreates it anywhere — including a self-hosted verdigraph-neurogenesis).
• get_agent ~84 tokens
  Full current state: genome, cognitive graph (nodes/edges with weights and trust), and summary counts.
• get_ledger ~106 tokens
  The append-only developmental ledger: every growth, pruning, and evaluation event with reasons (NG3 — returned verbatim).
• import_state ~102 tokens
  Recreate an agent from an export_state document.
• list_agents ~52 tokens
  All developmental agents on this mount, with summary counts.
• record_route_outcome ~278 tokens
  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.
• register_compute_profile ~200 tokens
  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 confidenc
• route_task ~190 tokens
  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 re
• submit_evaluation ~221 tokens
  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 u

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