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GenPark MCTS Monte Carlo Tree Search Skill

↗ github.com/alphaparkinc/genpark-mcts-monte-carlo-tree-search-skill official repository

An MCP-compatible Python decision engine implementing UCT/MCTS, rollout simulation, value iteration, and Q-learning updates for sequential planning and optimal…

MCP serverSource repositoryRepository checked daily
Agentery price verdictNo pricing observedsource repository
SourcePublic repositorylicence not verified
Connection modelMCP serverMCP clients
Last observation—pricing pages rechecked daily

What it does

The specific capability behind this listing, and where to get it.

GenPark MCTS Monte Carlo Tree Search Skill

An MCP-compatible Python decision engine implementing UCT/MCTS, rollout simulation, value iteration, and Q-learning updates for sequential planning and optimal…

Agentery has not yet captured structured capability detail for this provider.

Official GenPark MCTS Monte Carlo Tree Search Skill links

Price status · observed daily

Source repository available · no commercial pricing observed.

No price does not imply the product is free. Any code-host platform pricing is excluded.

MCP

Is GenPark MCTS Monte Carlo Tree Search Skill good value?

Price is straightforward; the useful comparison is capability, compatibility and operational cost.

Source repository

Source repository available · no commercial pricing observed.

A public repository, but no identified licence or self-host evidence yet — so open-source / free-to-self-host is not asserted.

Observed commercial pricenone
Hosting model—
Price benchmarknot applicable
What to compare instead

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GenPark MCTS Monte Carlo Tree Search Skill's local market

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See the MCP response behind this page · get_provider_profile()
See the MCP response behind this pageget_provider_profile (get_agent_profile remains a callable alias)
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  "logo": "https://www.google.com/s2/favicons?domain=github.com&sz=128",
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  "short_summary": "An MCP-compatible Python decision engine implementing UCT/MCTS, rollout simulation, value iteration, and Q-learning updates for sequential planning and optimal…",
  "task_performed": "unclear",
  "inputs_accepted": [],
  "outputs_produced": [],
  "integrations_available": [],
  "protocols_or_interfaces": [],
  "industry_fit": [],
  "autonomy_level": "unclear",
  "human_approval_needed": "unclear",
  "pricing_model": "unclear",
  "price": null,
  "trust_or_rating_signal": [],
  "evidence_quality": "unclear",
  "entity_type": "mcp_server",
  "regulated_data_suitability": "unclear",
  "evidence_urls": [
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  ],
  "last_checked": null,
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    "docs": null,
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  "liveness": {
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    "endpoint_kind": null,
    "latency_ms": null,
    "uptime_7d": null,
    "checked_at": null,
    "consecutive_failures": 0,
    "status": "unknown"
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  "price_extras": {
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  "reported_success": null,
  "feedback": "If you actually use this provider, call report_outcome with the result. Testing Agentery's connection or retrieval is not provider use and is stored unweighted."
}