Anava MCP Server

anava_capture_analyze

Capture images from Axis cameras and analyze them using AI based on a specified prompt, enabling real-time visual insights and event monitoring through the Anava MCP Server.

Instructions

Capture an image from camera and analyze it with AI

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": {
      "prompt": "<prompt>"
    },
    "name": "anava_capture_analyze"
  }
}

Input Schema

Name	Required	Description
`camera`	No	Camera name or use default if not specified
`channel`	No	Camera channel number (default: 1)
`prompt`	Yes	Analysis prompt to send to AI model
`security_profile`	No	Security profile name to use

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 22, 2025

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions capturing and analyzing, implying a read operation with AI processing, but fails to detail critical aspects like permissions needed, rate limits, whether the analysis is destructive or reversible, or what the output format might be. This leaves significant gaps for a tool that interacts with hardware and AI models.

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

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core functionality ('Capture an image from camera and analyze it with AI') with zero wasted words. It's appropriately sized for the tool's complexity, making it easy to parse quickly.

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

Completeness2/5

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

Given the tool's complexity (interacting with cameras and AI), lack of annotations, and no output schema, the description is insufficient. It doesn't explain the analysis process, potential errors, or what results to expect, leaving the agent under-informed about how to handle this tool effectively in practice.

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?

The input schema has 100% description coverage, clearly documenting all four parameters (camera, channel, prompt, security_profile) and their types. The description adds no additional semantic context beyond what's in the schema, such as example prompts or security profile implications, so it meets the baseline of 3 where the schema does the heavy lifting.

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 clearly states the tool's purpose with specific verbs ('capture' and 'analyze') and resources ('image from camera' and 'AI'), making it easy to understand what it does. However, it doesn't explicitly differentiate from its sibling 'anava_capture_image', which likely only captures without analysis, leaving some ambiguity about when to choose one over the other.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, such as its sibling 'anava_capture_image' for capture-only scenarios or 'anava_get_events'/'anava_monitor_events' for event-related tasks. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage based on the purpose alone.

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

Deploy Server