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perception_read

Refresh perception data to get updated structured state when monitoring conditions change or before taking action, returning attention, guard results, and recovery suggestions.

Instructions

Force-refresh a registered perception lens and return a full perception envelope. Use when post.perception.attention is dirty, stale, settling, guard_failed, or identity_changed, or when you need fresh structured state before the next action. Returns attention, guard results, latest target/browser state, changed fields, and suggested recovery actions. Prefer this over screenshot/get_context when a lens already exists.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lensIdYesLens ID returned by perception_register.
maxTokensNoOverride maxEnvelopeTokens for this read. Useful to get a richer snapshot on demand.
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 of behavioral disclosure. It effectively describes the tool's behavior: it performs a 'force-refresh' (implying it updates state), returns specific data ('attention, guard results, latest target/browser state, changed fields, and suggested recovery actions'), and mentions a use case for 'maxTokens' to 'get a richer snapshot on demand.' However, it lacks details on potential side effects, error handling, or performance implications.

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 appropriately sized and front-loaded, with the first sentence stating the core purpose. Each subsequent sentence adds necessary context without waste, such as usage conditions and comparisons to sibling tools, making it efficient and well-structured.

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?

Given the complexity of the tool (involving perception lenses and envelopes), no annotations, and no output schema, the description does a good job of explaining what the tool does, when to use it, and what it returns. However, it could be more complete by detailing the structure of the returned envelope or error scenarios, though the absence of an output schema makes this less critical.

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 description coverage is 100%, so the schema already fully documents both parameters ('lensId' and 'maxTokens'). The description adds minimal value beyond the schema by briefly mentioning 'maxTokens' in the context of getting a 'richer snapshot,' but it doesn't provide additional syntax, format, or usage details for the parameters.

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 tool's purpose: 'Force-refresh a registered perception lens and return a full perception envelope.' It specifies the verb ('force-refresh'), resource ('registered perception lens'), and output ('full perception envelope'), and distinguishes it from sibling tools like 'screenshot/get_context' by explaining when to prefer this tool.

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?

The description provides explicit guidance on when to use this tool: 'when post.perception.attention is dirty, stale, settling, guard_failed, or identity_changed, or when you need fresh structured state before the next action.' It also specifies when not to use it: 'Prefer this over screenshot/get_context when a lens already exists,' clearly differentiating it from alternatives.

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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