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analyze_circuit_image

Extracts circuit topology and components from schematic images, then cross-references a knowledge graph to provide engineering insights and related principles.

Instructions

Analyze a circuit schematic (or any technical image) using VLM + knowledge graph.

    Workflow:
      1. Classifies the image (circuit, plot, block_diagram, layout, etc.)
      2. Extracts circuit topology, components, signal flow, and design features
         using the frontier vision model (e.g. grok-4.20).
      3. Cross-references the extracted description against the knowledge graph
         (Razavi, ingested papers) to surface related concepts, equations,
         principles, and open hypotheses.

    Use this when a user sends a circuit schematic and wants engineering insights
    grounded in the knowledge base — NOT for ingesting the circuit as new knowledge.
    For PDFs/papers to be stored permanently, use ingest_pdf() instead.

    Args:
        image_path: Absolute path to the image file (PNG, JPG, etc.).
                    Typically the Telegram download path on disk.

    Returns:
        Formatted text with: figure type, visual analysis, and knowledge context.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the multi-step workflow (classify, extract, cross-reference), mentions the use of a vision model, and clarifies that it does not ingest new knowledge. It also notes the return format, providing solid behavioral context.

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?

The description is well-organized with a short intro, numbered workflow, usage guidance, args, and returns. It avoids filler but includes useful detail like example model and path; slightly long but each part contributes.

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

Completeness4/5

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

The description covers purpose, workflow, usage boundaries, parameter semantics, and return value. Given the tool's complexity and the existence of an output schema, it is sufficiently complete and contextualized.

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

Parameters4/5

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

Schema coverage is 0%, but the description compensates by explaining image_path as an absolute path to an image file, with the typical Telegram download path as an example. It also lists accepted formats (PNG, JPG, etc.), adding practical meaning beyond the bare schema field.

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

Purpose5/5

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

The description names a specific verb ('Analyze') and resource ('circuit schematic or any technical image') and differentiates from siblings by mentioning the VLM + knowledge graph workflow. It clearly states the tool's scope, unlike a tautology.

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

Usage Guidelines5/5

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

It explicitly states when to use the tool ('when a user sends a circuit schematic and wants engineering insights grounded in the knowledge base') and when not to ('NOT for ingesting the circuit as new knowledge'). It names an alternative for a different task ('use ingest_pdf() instead'), satisfying the when/when-not/alternatives requirement.

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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