predict.matched-pairs
Cross-venue equivalent market pairs (same question on Polymarket vs Kalshi) with price spread for arbitrage spotting (heuristic).
This record as markdown: /tools/io-github-2s-io-mcp/predict.matched-pairs.md
What predict.matched-pairs does on Mcp
AI agents call predict.matched-pairs to retrieve information from Mcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why predict.matched-pairs is rated Low
This tool queries and returns market data (price spreads across venues) for human decision-making. It performs no write operations, does not execute trades, does not delete data, and does not commit financial transactions. While the server operates in a financial domain (USDC settlement), this specific tool is purely informational—it surfaces arbitrage opportunities but leaves execution to other agents/users.
From the tool's definition Tool name 'predict.matched-pairs' and description indicate it 'retrieves...market pairs...with price spread' for analysis. Words like 'spotting' and '(heuristic)' signal query/comparison functionality with no modification or execution of trades.
Attacks that exploit this kind of access
The rule that runs predict.matched-pairs safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For predict.matched-pairs, this is the rule to start with:
predict.matched-pairs is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp, apply this rule, and every predict.matched-pairs call is checked against it from then on.
Questions about predict.matched-pairs
Cross-venue equivalent market pairs (same question on Polymarket vs Kalshi) with price spread for arbitrage spotting (heuristic). It is categorised as a Read tool in the Mcp MCP Server, which means it retrieves data without modifying state.
Register the MCP server in PolicyLayer and add a rule for predict.matched-pairs: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp. Nothing to install.
predict.matched-pairs is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the predict.matched-pairs rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for predict.matched-pairs. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
predict.matched-pairs is provided by the MCP server (@2sio/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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