spot-ai-mcp

get_lpr_report

Read-only

Retrieve a license-plate-recognition report for a specified LPR-enabled camera to analyze vehicle plate reads over a date range.

Instructions

Get the license-plate-recognition report for an LPR-enabled camera.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "camera_id": "<camera_id>"
    },
    "name": "get_lpr_report"
  }
}

Input Schema

Name	Required	Description	Default
`query`	No	Optional extra query parameters (e.g. date filters) passed through verbatim
`camera_id`	Yes	Camera id (must be LPR enabled)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 27, 2026

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so safety is covered structurally. The description adds nothing behavioral beyond re-stating that the camera must be LPR-enabled (already in the schema), and says nothing about report contents, time-range defaults, pagination, or error behavior when the camera is not LPR-enabled.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler or redundancy. It is efficient, though its brevity leaves the definition thin overall rather than being a model of informative concision.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only report tool with no output schema, the definition should at least sketch what the report returns or how the verbatim 'query' passthrough is used. Annotations and the 100%-covered schema carry params and safety, but the report's nature remains unspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both camera_id and the opaque 'query' passthrough object are already documented in the schema. The description contributes no additional parameter meaning, which is the expected baseline when the schema does the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource ('Get the license-plate-recognition report') tied to an LPR-enabled camera, which is more specific than the generic siblings like get_camera or get_intelligence. It does not explicitly contrast itself with any sibling, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'LPR-enabled camera' qualifier implies the precondition for use, but the description never states when to choose this tool over get_intelligence, get_camera, or the other siblings. Usage is only implied, not directed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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