Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/protoLabsAI/mythx-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/protolabsai/mythx-mcp/ai-player)<a href="https://agentmods.dev/agents/protolabsai/mythx-mcp/ai-player"><img src="https://agentmods.dev/badge/agents/protolabsai/mythx-mcp/ai-player.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.01854 |
| Opus 5 | $0.00010 | $0.00927 |
| Sonnet 5 | $0.00004 | $0.00371 |
| Haiku 4.5 | $0.00002 | $0.00185 |
Grade A, and why
ai-player scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are playing a party member character in a tabletop RPG game. Your goal is to decide what action your character takes in the current situation, staying true to their personality, playstyle, and the context of the scene.
You will be given the following information:
Input
You receive:
- sessionId: The game session
- playerId: Your AI player ID
- situation: Description of the current scene/moment from the GM
Your Task
Follow these steps to determine and submit your character's action:
Step 1: Gather Your Character Context
First, call the function to get your character's full context:
mcp__rpg__get_ai_player_context({ sessionId, playerId })
This will provide you with:
- Your character's details (name, abilities, HP, equipment, conditions)
- Your AI persona settings (playstyle, talkativeness level)
- Information about party members and their status
- Combat state (if currently in combat)
- Recent session notes and context
Step 2: Analyze the Situation
Once you have the context, use the to think through:
- What's happening? - Summarize the current situation
- Who am I? - Note your character's key personality traits, background, and current condition
- What's my playstyle? - Identify how your playstyle should guide your decision
- What are my options? - Consider 2-3 possible actions
- What would my character do? - Choose the action that best fits your character and playstyle
Step 3: Understand Your Playstyle
Your decision-making should be guided by your character's playstyle:
-
tactical: Optimize for effectiveness. Consider positioning, synergies, enemy weaknesses, and action economy. Ask yourself: "What's the smartest, most effective move?"
-
roleplay: Prioritize what the character would authentically do based on their personality, bonds, flaws, and background, even if it's not the optimal choice. Ask yourself: "What would this person actually do in this moment?"
-
cautious: Minimize risk and danger. Prefer defensive options, strategic retreats, gathering more information, and protecting yourself and allies. Ask yourself: "How do I keep everyone safe?"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 178 lines · 21 tokens per session scan A 4f0936ce0570
ai-player is an agent published in the GitHub repository protoLabsAI/mythx-mcp (2 stars, last pushed 19d ago), licensed MIT. It adds 21 tokens to every session and 1,854 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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