Learn the pattern template features from the provided image. 训练模板，使用输入的图像。 The template center can be modified later. 模板中心可根据需求修改。 The shape and polarity of the template features are extracted from the shape image. 特征点通过给定的形状图像提取提取模板的形状和极性。 The feature points are extracted from the given shape...
Part of the Leaper Vision Toolkit MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents use pat_match_learn_with_shape_image to create or modify resources in Leaper Vision Toolkit. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call pat_match_learn_with_shape_image repeatedly, creating or modifying resources faster than any human could review. Intercept's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Leaper Vision Toolkit.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
tools:
pat_match_learn_with_shape_image:
rules:
- action: allow
rate_limit:
max: 30
window: 60See the full Leaper Vision Toolkit policy for all 169 tools.
Learn the pattern template features from the provided image. 训练模板，使用输入的图像。 The template center can be modified later. 模板中心可根据需求修改。 The shape and polarity of the template features are extracted from the shape image. 特征点通过给定的形状图像提取提取模板的形状和极性。 The feature points are extracted from the given shape image, thus not affected by DetailLevel. 特征点通过给定的形状图像提取，不受 DetailLevel 参数的影响。 The shape image should have the same size as the input image. 形状图像的尺寸需与输入图像相同。 如果别的工具需要训练后的模板匹配的功能对象，可以使用当前对象Json。 返回结构是一个序列化的JSON字符串，属性包括： LpvClassName(调用工具函数的名称) 和 InputParameterFile(调用工具函数需要传入的参数文件名称)。 LpvClassName 表示模板匹配的功能对象名称，目前名称应为：ILMatch (模板匹配的功能对象); InputParameterFile 表示模板匹配的功能对象的参数文件名称。. It is categorised as a Write tool in the Leaper Vision Toolkit MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Add a rule in your Intercept YAML policy under the tools section for pat_match_learn_with_shape_image. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Leaper Vision Toolkit MCP server.
pat_match_learn_with_shape_image is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the pat_match_learn_with_shape_image rule in your Intercept 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 Intercept policy for pat_match_learn_with_shape_image. 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.
pat_match_learn_with_shape_image is provided by the Leaper Vision Toolkit MCP server (leaper-mcp/leaper-mcp-proxy). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Open source. One binary. Zero dependencies.
npx -y @policylayer/intercept