interest_rate
Return a precise reference interest rate — the exact figure an agent injects into a treasury, lending, valuation or trading model. Available rates: fed_funds, sofr, us_10y, us_2y, us_3m, ecb_main, euribor_3m. Source: FRED (Federal Reserve Bank of St. Louis). When to use: an agent's computation ne...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/interest-rate.md
What interest_rate does on Mcp Knowledge
AI agents call interest_rate to retrieve information from Mcp Knowledge without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
rate | string | Yes | Reference rate name |
async | boolean | — | If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client ti |
Parameters from the server's own tool schema.
Why interest_rate is rated Low
This tool retrieves published benchmark interest rate data from FRED; it has no side effects and does not execute trades or move money. However, severity is medium because the output is directly injected into financial models (treasury, lending, valuation, trading), meaning a wrong or manipulated rate could propagate into high-stakes financial decisions downstream.
From the tool's definition Return a precise reference interest rate — the exact figure an agent injects into a treasury, lending, valuation or trading model. Source: FRED (Federal Reserve Bank of St. Louis).
Attacks that exploit this kind of access
The rule that runs interest_rate safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp Knowledge, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For interest_rate, this is the rule to start with:
interest_rate 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 Knowledge, apply this rule, and every interest_rate call is checked against it from then on.
Questions about interest_rate
Return a precise reference interest rate — the exact figure an agent injects into a treasury, lending, valuation or trading model. Available rates: fed_funds, sofr, us_10y, us_2y, us_3m, ecb_main, euribor_3m. Source: FRED (Federal Reserve Bank of St. Louis). When to use: an agent's computation needs a current benchmark rate as a precise input. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
interest_rate accepts 2 parameters: rate, async. Required: rate. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for interest_rate: 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 Knowledge. Nothing to install.
interest_rate 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 interest_rate 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 interest_rate. 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.
interest_rate is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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