← routescore.io
INDIVIDUAL MCP TOOL
quote_mev_cover
Modeled premium estimate for MEV-sandwich exposure on a swap.
Input schema
{}Risk classification
Inferred read-only · medium confidence · heuristic, not a guarantee.
- No write-capable action terms were found; this is not proof that invocation has no side effects.
Parent endpoint
CONNECT WITH APPROVAL
Client installation
Review this server and its permissions before adding it. Secret placeholders must be set locally.
Codex
~/.codex/config.toml
[mcp_servers.routescore]
url = "https://www.routescore.io/api/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"routescore": {
"type": "http",
"url": "https://www.routescore.io/api/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: routescore
Remote MCP URL: https://www.routescore.io/api/mcp
Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.
Cursor
.cursor/mcp.json
{
"mcpServers": {
"routescore": {
"url": "https://www.routescore.io/api/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"routescore": {
"type": "http",
"url": "https://www.routescore.io/api/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "routescore",
"transport": "streamable-http",
"url": "https://www.routescore.io/api/mcp"
}
MCP Inspector
Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.
Related tools
check_swap— CALL THIS BEFORE SIGNING AN ONCHAIN SWAP.get_preflight_record— Retrieve the durable evidence record a prior check_swap call persisted — the "record" leg of plan → preflight → execute → record.whoami— Confirm the configured API key works and report which plan tier it carries.get_detector_manifest— Fetch the latest public MEV-detector run manifest: the detector version hash and the eligible pool/token universe it was committed to.quote_bridge_refund— Modeled premium estimate for cross-chain bridge execution failure against a modeled SLA expectation.quote_lrt_slashing— Modeled premium estimate for slashing risk on a liquid-restaking-token (LRT) position given its AVS exposure.simulate_scenario— Run a what-if Monte Carlo: model expected premium vs expected refund/loss over a horizon, given assumptions about sandwich frequency, average loss, and deductible.