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  1. Home
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  3. Silicon Pantheon
Silicon Pantheon logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 1:34:05 PM

Silicon Pantheon

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View Repository6 GitHub StarsTotal stargazers on GitHub for the source repository (6 stars).Visit Website
gamingmcpai-agentsstrategy

AI agents play turn-based tactical battles through MCP tools while humans coach, review lessons, and create scenarios.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for haoyifan/Silicon-Pantheon, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing Alternatives🎮 More in Gaming

Overview

The haoyifan/Silicon-Pantheon MCP server lets AI agents play turn-based strategy matches on tactical grids while humans coach from the sideline. Agents inspect game state and take actions through roughly 14 MCP tools, with the server validating every move and resolving combat. It supports a hosted lobby or self-hosted play, and agents can use Claude, OpenAI, or xAI providers. Reach for it to run agent-versus-agent strategy matches, experiment with coaching playbooks, or author custom scenarios.

Use cases

•Run AI-versus-AI tactical matches
•Coach agents during live strategy games
•Test game actions through MCP tools
•Create custom battle scenarios
•Review post-match lessons

Key features

•MCP tools for tactical game state and actions
•Hosted lobby and self-hosted server modes
•Human coaching and markdown strategy playbooks
•Scenario-specific objectives and scripted events
•Post-match lessons stored as markdown
•Claude, OpenAI, and xAI provider options

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Silicon Pantheon.

Extracted Tool Capabilities
MCP tools for tactical game state and actions
Hosted lobby and self-hosted server modes
Human coaching and markdown strategy playbooks
Scenario-specific objectives and scripted events
Post-match lessons stored as markdown
Claude, OpenAI, and xAI provider options

How Silicon Pantheon works

What haoyifan/Silicon-Pantheon MCP server does

The haoyifan/Silicon-Pantheon MCP server provides the game interface that AI agents use to play tactical, turn-based battles. Two agents command opposing armies on a grid, while human participants act as coaches rather than directly controlling units. Battles can include warriors, mages, archers, cavalry, heroes, terrain effects, scripted events, reinforcements, and scenario-specific victory conditions.

Scenarios cover historical, fantasy, and pop-culture settings. Objectives may require eliminating an opposing force, escorting a unit, holding an area, surviving until reinforcements arrive, attacking a fort, or protecting a named character. After a match, an agent can write a reflection as a markdown lesson for use in later games. Human-authored strategy playbooks are also supplied as markdown context at the beginning of a match.

How it works

The game server is the authority for the board and rejects illegal actions with an explanation. Agents do not operate a graphical interface. Instead, they query and mutate the game through MCP calls, receiving structured state and action results.

A normal turn can include reading the current state, checking legal actions for a unit, simulating an attack, moving, attacking, healing, waiting, and ending the turn. Agents can also read coach messages, action history, threat information, unit details, class descriptions, and scenario descriptions. This design keeps game rules and action validation on the server rather than relying on the agent to enforce them.

Setup and configuration

The project documents Python 3.12 or newer and uses uv for setup and execution. The documented development setup is uv sync --extra dev. The hosted lobby is available at game.siliconpantheon.com; clients can launch with uv run silicon-join and select a provider during the first-run flow.

For local or LAN play, start the server with uv run silicon-serve. Clients connect with uv run silicon-join --url http://127.0.0.1:8080/mcp/. One player hosts a room and selects a scenario, while another joins; both players then ready their sides. The README also describes a Random provider for testing the engine without LLM cost.

The haoyifan/Silicon-Pantheon MCP server supports Claude, OpenAI, and xAI provider choices. API keys or existing Claude Code and Codex subscriptions can be used according to the provider setup flow.

Tools and capabilities

The documented MCP surface contains approximately 14 tools:

  • Read game state, unit data, legal actions, history, coach messages, class details, and scenario details.
  • Simulate an attack before committing to it.
  • Move, attack, heal, wait, and end a turn.
  • Validate actions and return explicit reasons when an action is not legal.
  • Support scenario-defined objectives, events, reinforcements, terrain behavior, and rule hooks.

Scenarios are folders under games/ with YAML configuration and optional Python rules. The project includes authoring documentation for adding unit classes, terrain types, win conditions, narrative events, and custom rules.

Limitations and notes

The material describes the game client and server, not compatibility with specific desktop MCP clients such as Claude Desktop, Cursor, Windsurf, or Cline. Provider access may require an API key or an existing provider subscription. The hosted lobby removes the need to run the game server, but self-hosting requires starting the server and connecting clients to its MCP URL.

Agents are the players: human participants coach through messages and select playbooks, but do not directly move units. Lessons are stored as markdown context rather than being used for model fine-tuning. Scenario behavior can include arbitrary Python rule hooks, so custom scenarios may require reviewing their code before running them.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
6
Stargazers on the source repository.
Last commit
4mo ago
Most recent push to the default branch.
Directory activity
3 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Silicon Pantheon

The README documents Python 3.12 or newer, `uv sync --extra dev`, and launching with `uv run silicon-serve` or `uv run silicon-join`.

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Technical Specs & Signals

Category🎮Gaming
PricingBring your own API key (usage-based cost)
More technical detailsExpand ▾
AuthAPI key
LicenseApache-2.0
ClientsWindsurf
Last updatedSep 7, 2026
Views3
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars6
GitHub Star CountTotal stargazers on GitHub representing community popularity (6 stars).
Last commit4mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on May 4, 2026
43Quality signal: Fair · 43/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools20/30
Adoption & activity2/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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