credits_ledger
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.
This record as markdown: /tools/qverisai-qveris-agent-toolkit/credits-ledger.md
What credits_ledger does on Qverisai
AI agents call credits_ledger to retrieve information from Qverisai without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why credits_ledger is rated Low
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.
Attacks that exploit this kind of access
The rule that runs credits_ledger safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Qverisai, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For credits_ledger, this is the rule to start with:
credits_ledger 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 Qverisai, apply this rule, and every credits_ledger call is checked against it from then on.
Questions about credits_ledger
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 Qverisai MCP Server, which means it retrieves data without modifying state.
Register the Qverisai 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 Qverisai. 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 Qverisai 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.
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