Roost public MCP servers for AI agents: a payment pre-flight check (roost-corpus-mcp) and a finance-agent safety test (roost-eval-mcp). Advisory, zero-config, no API key.
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# roost-mcp Two zero-config, advisory MCP servers from [Roost](https://roostcapital.ai) for AI agents that handle money. No API key, no signup — an anonymous per-install token is created on first use. Calls are anonymized (hashed identifiers, bucketed amounts, no PII) and aggregated into a benchmark of money-moving-agent behavior; that's why they're free. Install: ``` pip install roost-mcp ``` This provides two commands. ## Add it to your MCP client Both servers run over stdio. Point your client at whichever you want (either works alone) — a Claude Desktop–style config: ```json { "mcpServers": { "roost-corpus": { "command": "roost-corpus-mcp" }, "roost-eval": { "command": "roost-eval-mcp" } } } ``` If your client doesn't inherit your shell `PATH`, use the absolute path to the console script (what `which roost-corpus-mcp` prints). ## `roost-corpus-mcp` — payment pre-flight check A second opinion before your agent sends a payment. The `check_payment` tool takes an invoice, purchase order, or transfer and returns a clear recommendation — **proceed**, **hold & review**, or **consider financing** — with the reasons: a suspected duplicate, an amount out of line with this agent's own history, an early-pay discount worth capturing, or terms worth using. Advisory, not a gate — your agent stays in control. Fast, deterministic checks that sharpen as your agent builds a short history. USD invoices, purchase orders, and transfers today. ## `roost-eval-mcp` — finance-agent safety test Find the money decisions your agent gets dangerously wrong — before it ships. `list_eval_scenarios` returns realistic money-action situations (fraud bait, payee changes, overspend, duplicate/replay, social engineering, …); your agent decides proceed/hold/flag on each; `submit_eval` returns a private scorecard that leads with a clear `summary.status` (`balanced` / `unsafe` / `over_conservative` / `mixed`), then the component metrics (coverage, correctness, consistency, sever…
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