Context-safe final credits ledger query. Defaults to aggregated summary, supports precise search by entry type/direction/credit range, and writes large exports to a local JSONL file instead of returning all rows.
AI agents call credits_ledger to retrieve information from Qveris Agent Toolkit without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and queries financial ledger data without modifying it. While it operates on financial information, it performs no financial transactions, money movement, or irreversible operations. The mechanism of 'writing large exports to a local JSONL file' is a read operation (exporting query results), not a financial action.
From the tool's definition Tool is described as a 'query' that 'defaults to aggregated summary' and 'supports precise search' with filtering parameters.
Documented attack patterns abuse exactly the kind of access credits_ledger gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Qveris Agent Toolkit, and nothing reaches the server without passing your rules. This is the rule we recommend for credits_ledger:
{
"version": "1",
"default": "deny",
"tools": {
"credits_ledger": {}
}
}credits_ledger is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Context-safe final credits ledger query. Defaults to aggregated summary, supports precise search by entry type/direction/credit range, and writes large exports to a local JSONL file instead of returning all rows. It is categorised as a Read tool in the Qveris Agent Toolkit MCP Server, which means it retrieves data without modifying state.
Register the Qveris Agent Toolkit MCP server in PolicyLayer and add a rule for credits_ledger: 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 Qveris Agent Toolkit. Nothing to install.
credits_ledger 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 credits_ledger 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 credits_ledger. 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.
credits_ledger is provided by the Qveris Agent Toolkit MCP server (qverisai/qveris-agent-toolkit). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 8 Qveris Agent Toolkit tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
8 Qveris Agent Toolkit tools catalogued and risk-classified — across an index of 42,500+ MCP servers.