vision.solve_lighting
Solve a LightingHypothesisSet capped by derive() over inputs.
This record as markdown: /tools/visionmcp/vision.solve-lighting.md
What vision.solve_lighting does on Visionmcp
AI agents invoke vision.solve_lighting 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 vision.solve_lighting is rated High
The tool appears to run a computational pipeline (solving and deriving lighting hypotheses from inputs), which falls under Execute. It doesn't clearly read static data, write/store user data, or perform destructive/financial actions. The description is vague, lowering confidence. Severity is medium given it processes visual/model data and could affect downstream pipeline state.
From the tool's definition 'Solve a LightingHypothesisSet capped by derive() over inputs' — the word 'solve' and 'derive()' suggest computation/execution over inputs to produce derived results
Attacks that exploit this kind of access
The rule that runs vision.solve_lighting 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 vision.solve_lighting, this is the rule to start with:
vision.solve_lighting 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 vision.solve_lighting call is checked against it from then on.
Questions about vision.solve_lighting
Solve a LightingHypothesisSet capped by derive() over inputs. 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 vision.solve_lighting: 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.
vision.solve_lighting 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 vision.solve_lighting 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 vision.solve_lighting. 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.
vision.solve_lighting 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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