candidate.evaluate_transaction
Evaluate every mandatory gate atomically and auto-reject any regression.
This record as markdown: /tools/visionmcp/candidate.evaluate-transaction.md
What candidate.evaluate_transaction does on Visionmcp
AI agents invoke candidate.evaluate_transaction to trigger actions in Visionmcp. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why candidate.evaluate_transaction is rated High
This tool runs an automated evaluation process that not only reads/checks data but also executes consequential actions (auto-rejection of regressions). The 'auto-reject' behavior means it has side effects beyond mere reading — it modifies state or outcomes of a transaction. It does not permanently delete data but does execute logic that changes outcomes, placing it in Execute.
From the tool's definition 'Evaluate every mandatory gate atomically and auto-reject any regression' — triggers an automated evaluation pipeline that performs gate checks and auto-rejects (takes action) based on results
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs candidate.evaluate_transaction safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Visionmcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For candidate.evaluate_transaction, this is the rule to start with:
candidate.evaluate_transaction stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Visionmcp, apply this rule, and every candidate.evaluate_transaction call is checked against it from then on.
Questions about candidate.evaluate_transaction
Evaluate every mandatory gate atomically and auto-reject any regression. It is categorised as a Execute tool in the Visionmcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Vision MCP server in PolicyLayer and add a rule for candidate.evaluate_transaction: 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 Visionmcp. Nothing to install.
candidate.evaluate_transaction is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the candidate.evaluate_transaction 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 candidate.evaluate_transaction. 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.
candidate.evaluate_transaction is provided by the Vision MCP server (joshuahickscorp/visionmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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