Borrowing it
Nothing to install: this file belongs to fritzprix/chess-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/fritzprix/chess-mcp-server/main/.cursor/skills/mcp-chess-e2e/SKILL.mdgit clone --depth 1 https://github.com/fritzprix/chess-mcp-serverWrote 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/skills/fritzprix/chess-mcp-server/mcp-chess-e2e)<a href="https://agentmods.dev/skills/fritzprix/chess-mcp-server/mcp-chess-e2e"><img src="https://agentmods.dev/badge/skills/fritzprix/chess-mcp-server/mcp-chess-e2e/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fritzprix/chess-mcp-server/mcp-chess-e2e"><img src="https://agentmods.dev/badge/skills/fritzprix/chess-mcp-server/mcp-chess-e2e.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00055 | $0.00550 |
| Opus 5 | $0.00028 | $0.00275 |
| Sonnet 5 | $0.00011 | $0.00110 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
mcp-chess-e2e 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chess MCP E2E Testing
Purpose
Test the server through the MCP protocol rather than calling server functions
directly. Use src.mcp_client.ChessMcpClient as the reusable client.
Standard command
Run the complete suite with:
uv run --extra dev pytest -q
Run only MCP scenario tests with:
uv run --extra dev pytest tests/test_mcp_client_scenarios.py -q
Test isolation
- Give each test a unique temporary
CHESS_MCP_DB_PATH. - Set
BROWSERto a nonexistent executable unless browser launching is being tested explicitly. - Do not use the user's persistent game database.
- Let
ChessMcpClientown the stdio and session lifecycle withasync with.
Required scenarios
Maintain coverage for:
- MCP initialization and tool discovery.
- Agent versus Computer with the agent as White.
- Agent versus Computer with the agent as Black, including the initial AI move.
- Agent versus Agent using two
ChessMcpClientinstances and the same temporary database path. Each client must run its own server process. 5. Human game creation returning an embedded UI resource. 6. State persistence after reconnecting to the same database.
Scenario rules
- Parse `game_id` from `createGame` and use the returned `GameSession` for
subsequent calls.
- Call
joinGamefrom the second client for Agent versus Agent scenarios. - Assert both
result.isErrorand meaningful state text where relevant. - Prefer stable state assertions such as turn, FEN presence, and game-over status over exact prose wording.
- Use legal moves that are deterministic for the scenario; do not assume which move the AI selected unless the test explicitly controls randomness.
Adding a scenario
- Add the test to
tests/test_mcp_client_scenarios.py. - Use a fresh
tmp_pathdatabase. - Use separate client contexts when process isolation matters.
- Verify both success and failure paths.
- Run the focused test file, then the complete suite.
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 · 75 lines · 55 tokens per session scan A 02556404eaef
mcp-chess-e2e is a skill published in the GitHub repository fritzprix/chess-mcp-server (0 stars, last pushed 11d ago), licensed MIT. It adds 55 tokens to every session and 550 once invoked, about $0.0003 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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