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vision_locate

Locate page elements by natural-language description using a vision LLM. Returns coordinates and confidence, with optional click action.

Instructions

⭐ Find an element by natural-language description using a vision LLM.

Uses the same provider as solve_recaptcha_ai (OPENAI_* / ANTHROPIC_* env).
Reuses solve_recaptcha_ai's vision plumbing so any vision-capable model
works (gpt-4o, gpt-5.x, claude, llava, llama-3.2-vision).

Args:
    description: NL description, e.g. "the red Create button at bottom right"
    click: if True, also dispatches a CDP mouse_click at the located point
    api_key/base_url/model/provider: explicit overrides (else from env)

Returns JSON: {"found":true/false, "x":int, "y":int, "confidence":"high|medium|low"}.
Use when CSS selectors are unreliable (visual-only differentiator, dynamic IDs).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYes
clickNo
api_keyNo
base_urlNo
modelNo
providerNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Despite no annotations, the description fully discloses behavior: it uses vision LLM, can optionally click via CDP, and returns JSON with found, x, y, confidence. It also notes provider dependency.

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 well-structured with emojis, bullet-like argument list, and clear return format. Every sentence is informative and concise.

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

Completeness5/5

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

Given the output schema exists (specified as JSON format), the description explains the return. It covers all 6 parameters, the use case, and behavioral details, making it complete.

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

Parameters5/5

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

Schema coverage is 0%, but the description explains each parameter: 'description' as NL, 'click' as boolean, and optional overrides. It adds meaning beyond the schema's titles and types.

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 clearly states 'Find an element by natural-language description using a vision LLM', specifying the verb, resource, and method. It differentiates from siblings like 'click' and 'find_by_image' by emphasizing NL description and vision LLM.

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

Usage Guidelines4/5

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

The description advises using when CSS selectors are unreliable and mentions it reuses solve_recaptcha_ai's plumbing, providing context. However, it lacks explicit when-not-to-use or detailed alternatives among siblings.

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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