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.
npx agentmods add mcp/iris-eval/mcp-server/irisgit clone --depth 1 https://github.com/iris-eval/mcp-serverGrade A, and why
iris 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 yesterday.
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.
What it actually says
{
"iris": {
"command": "npx",
"args": [
"-y",
"@iris-eval/mcp-server"
],
"env": {
"IRIS_LOG_LEVEL": "info"
}
}
}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.
- yesterday First seen · 12 lines scan A 14fa195ed15c
iris is an MCP server published in the GitHub repository iris-eval/mcp-server (7 stars, last pushed 8d ago), licensed MIT. Its token cost is not measured: an MCP server costs its tool schemas, not its config file. 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.
Other mcp servers, from other repositories
evalview
MCP server "evalview" as configured in hidai25/eval-view. Launched with evalview mcp serve. Needs 2 environment variables to run.
evalview-mcp
Open-source testing and regression detection framework for AI agents. Golden baseline diffing, CI/CD integration, works with LangGraph, CrewAI, OpenAI, Anthropic Claude, HuggingFace, Ollama, and MCP. Runs locally from the evalview Python package. Needs 1 environment variable to run.
governance-mcp
MCP server "governance-mcp" as configured in cirwel/unitares. Runs locally from the governance-mcp Python package.
rule-drift
Test whether AI agents retain critical instructions as conversations grow. Runs locally from the rule-drift npm package.
memtrust
Independent, reproducible benchmark harness for agent-memory backends (MemPalace, Mem0, Zep/Graphiti, OpenViking): runs LongMemEval, LoCoMo, contradiction-detection, and a dozen other evals against all four and publishes the raw logs, not vendor-curated numbers. Runs locally from the memtrust-cli Python package.
deskcert
Certify whether an AI agent is safe to operate your internal web application before production rollout. Author a YAML task suite against your own app, run it with Playwright, and gate CI/CD on a forbidden-action safety score. Runs locally from the deskcert-cli Python package.