Create a new pattern matching tool instance. 创建一个新的模板匹配工具实例。 This interface provides functionalities of pattern matching. 该接口提供模板匹配的功能。 Pattern detector is designed to single or multiple instances of the trained pattern in the given image and region. 模板匹配用于在指定图像和区域中基于训练的模板，搜索和定位一个或多个匹配结果。 The alg...
AI agents invoke pat_match_new to trigger actions in Leaper Vision Toolkit. 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.
This tool executes image processing algorithms (pattern detection/matching) on provided image data. While it reads images as input, its primary purpose is to run computational operations (shape feature extraction, gray-scale analysis, detection algorithms) whose output depends on the image content and configuration parameters.
From the tool's definition Tool description states it "Create a new pattern matching tool instance" with "functionalities of pattern matching" to "detect...instances of the trained pattern in the given image and region." This involves algorithmic execution on image data with real-time…
Attacks that exploit this kind of access
Create a new pattern matching tool instance. 创建一个新的模板匹配工具实例。 This interface provides functionalities of pattern matching. 该接口提供模板匹配的功能。 Pattern detector is designed to single or multiple instances of the trained pattern in the given image and region. 模板匹配用于在指定图像和区域中基于训练的模板，搜索和定位一个或多个匹配结果。 The algorithm is real-time and of sub-pixel accuracy. Detection is based mainly on shape information extracted from pattern template, with combination of the gray-scale features. 该算法具有高实时性和高亚像素角度的特点。匹配过程基于从模板图像中训练得到的形状特征，结合图像的灰度特征。 It's robust to translation, scale and rotation of the pattern objects, as well as the lightening or material changes in production line which results in intensity, contrast or blurring in input images. 可以定位目标物体的旋转、平移和缩放，可有效应对产线环境的光照变化、物体材质变化引起的明暗、对比度、模糊等现象。 To use this interface, you should create a LMatch object. 使用这个接口，需要创建一个 LMatch 对象。 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 Execute tool in the Leaper Vision Toolkit MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Leaper Vision Toolkit MCP server in PolicyLayer and add a rule for pat_match_new: 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 Leaper Vision Toolkit. Nothing to install.
pat_match_new 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 pat_match_new 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 pat_match_new. 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_new is provided by the Leaper Vision Toolkit MCP server (leaper-mcp/leaper-mcp-proxy). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
Teams ship this data inside their own products. See what a licence covers →