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screenshot_annotate

Captures a screenshot with numbered bounding-box overlays, enabling vision-language models to reference specific elements spatially.

Instructions

Screenshot with @eN bounding-box overlays (vision-LLM friendly).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesOutput PNG path.
leaseNoOptional lease token to present if the target session is leased (0.7.0). Threaded per-call; never read from the server's env.
sessionNoOptional session name to target (omit for the shared 'default'). On a daemon shared with other agents, pass a UNIQUE name for stateful multi-step work (go→click→fill) so you don't collide on 'default'.
full_pageNoCapture full page.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that the tool takes a screenshot with bounding-box overlays. It does not mention side effects (e.g., whether the page is modified), authentication requirements, rate limits, or what the output file actually contains beyond 'overlays'. For a tool that saves to a path, details about the file format or how overlays are rendered are missing. This is a significant gap given zero annotation coverage.

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 sentence, highly concise, and front-loads the core purpose. Every word contributes: 'Screenshot' sets the action, 'with @eN bounding-box overlays' adds the distinguishing feature, and 'vision-LLM friendly' signals the intended use case. No fluff or repetition. It is an exemplary concise description.

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?

The tool has moderate complexity (4 params, including session and lease), but the description provides only the bare essence. It lacks context on when to use this instead of plain 'screenshot' or vision tools, and it does not describe the nature of the @eN overlays or their format. There is no output schema, so the description should clarify what the agent can expect from the call, but it does not. An agent would need to infer too much.

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 covers 100% of parameters with descriptive text (path, lease, session, full_page). The description adds no additional parameter meaning beyond what the schema already provides. Per the calibration rule, when schema_coverage is high (80%+), the baseline is 3, and there is no extra value from the description to raise it.

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 the core function: 'Screenshot with @eN bounding-box overlays (vision-LLM friendly).' It specifies a verb ('Screenshot'), a resource (bounding-box overlays), and a distinguishing feature (vision-LLM friendly) that separates it from the plain 'screenshot' sibling. The purpose is unambiguous and specific.

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 gives no explicit guidance on when to use this tool versus alternatives. It does not mention plain 'screenshot', vision tools, or any exclusions. The parenthetical 'vision-LLM friendly' hints at a use case (preparing images for vision models) but does not state when to prefer it over other tools or when not to use it. This leaves an agent without clear direction.

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