Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-rul1an-assay": {
"args": [
"-y",
"@modelcontextprotocol/server-filesystem"
],
"command": "npx"
}
}
}Are you the author?
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Your MCP agent calls read_file, exec, web_search — but should it, and what can you honestly prove about that run afterward?
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y '@modelcontextprotocol/server-filesystem' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
@modelcontextprotocol/server-filesystem vulnerability allows for path validation bypass via colliding path prefix
Versions of Filesystem prior to 0.6.3 & 2025.7.1 could allow access to unintended files in cases where the prefix matches an allowed directory. Users are advised to upgrade to 2025.7.1 to resolve the issue. Thank you to Elad Beber (Cymulate) for reporting these issues.
>= 0source →@modelcontextprotocol/server-filesystem allows for path validation bypass via prefix matching and symlink handling
Versions of Filesystem prior to 0.6.3 & 2025.7.1 could allow access to unintended files via symlinks within allowed directories. Users are advised to upgrade to 2025.7.1 to resolve. Thank you to Elad Beber (Cymulate) for reporting these issues.
>= 0source →Be the first to review
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Signed receipts for agent, API, and MCP interactions. Portable and offline-verifiable.
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Evidence compiler for agent review artifacts
Portable evidence receipts, verifiable bundles, and bounded Trust Basis claims for agent systems.
See It Work · Promptfoo JSONL · OpenFeature · CycloneDX ML-BOM · Quick Start · CI Guide · Discussions
Use Assay if you already have machine-readable AI outcomes or agent tool-call tests and want a small reviewable artifact boundary in CI.
Start with the path that matches what you already have:
| You have | Use this when | What you get | Next click |
|---|---|---|---|
| Promptfoo JSONL from CI evals | You want smaller PR evidence than a full eval export | Eval outcome receipts, verified bundle, Trust Basis diff | Promptfoo JSONL |
OpenFeature boolean EvaluationDetails | You want CI evidence for a runtime flag decision boundary | Decision receipt, verified bundle, Trust Basis diff | OpenFeature EvaluationDetails |
| CycloneDX ML-BOM model component | You want CI evidence for the model inventory/provenance boundary that existed | Inventory receipt, verified bundle, Trust Basis diff | CycloneDX ML-BOM |
| MCP tool calls | You are ready to put a policy file around tool execution | Allow/deny audit trail and evidence for observed tool behavior | MCP Quick Start |
| A GitHub PR gate | You want CI to block regressions from checked artifacts | Trust Basis diff, gate status, SARIF/JUnit-ready output | CI Guide |
The core workflow is intentionally small: import or record a bounded outcome, bundle and verify it, compile trust-basis.json, then gate the Trust Basis diff. Assay does not make the upstream tool the source of truth; it makes the evidence boundary inspectable.
Trust Basis Gate
Status: OK
Bundles verified: 1
Regressed claims: 0
Assay is not a trust-score engine, a generic eval dashboard, or a hosted observability product. See What Assay is and is not for the boundary.
Yes, if you:
Not yet, if you:
cargo install assay-cli
CI: GitHub Action. Python SDK: pip install assay-it.
No hosted backend. No API keys for core flows. Deterministic: same input, same decision.
Trust claims use explicit *epistemology