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CEL Perceive

cel_perceive

Maintain an always-updated mental model of your application's UI by monitoring background events and accessibility changes. Read the current state instantly, verify actions, and receive LLM-powered next-action suggestions.

Instructions

Always-on perception engine (Cortex). Maintains a continuously-updated mental model via background event streams with periodic accessibility tree refreshes on significant changes, and vision/screenshots when flagged as needed.

IMPORTANT: Singleton — only one perception session can be active at a time. cel_see 'watch' mode is unavailable during an active session.

Modes:

  • start: Boot the cortex with a goal. Set enable_suggestions=true (default) for LLM-powered next-action recommendations on each read.

  • read: Get the mental model snapshot (instant — model is kept warm by background events).

  • feed: Report an action you took (action, target, expected outcome). Cortex waits for screen to settle, diffs against current model, returns verification.

  • checkpoint: Summarize completed work and reset action history. Use between phases of multi-step tasks.

  • configure: Update goal or enable_suggestions mid-session.

  • status: Cortex health — confidence score, uptime, cycle count, element counts (stable vs volatile), temporal state (loading, errors, focus trail).

  • stop: Shutdown the cortex and get a summary.

The model includes temporal awareness (loading states, error persistence, focus trail) and element stability classification (stable vs volatile targets).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are present, so the description carries full burden. It discloses that the tool is always-on, maintains a background mental model, uses event streams, accessibility refreshes, and optional screenshots. It explains each mode's behavior and side effects (e.g., feed waits for screen settle, diffs model). Minor ambiguity about whether feedback modifies state, but overall highly transparent.

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 somewhat lengthy but well-organized with a clear mode list and important constraints upfront. Every sentence adds information, though some details could be tightened. Front-loading the singleton note is effective.

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 no parameters, no output schema, and no annotations, the description covers all essential information: purpose, modes, constraints, sibling differentiation, and behavioral model. It is fully adequate for an agent to select and invoke the tool correctly.

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?

The input schema has zero parameters (100% documented by schema), so baseline is 4. The description adds value by explaining the modes which act as sub-operations, but no parameter details are needed.

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 is an 'always-on perception engine' that maintains a mental model, and explicitly lists all modes (start, read, feed, etc.) with specific verbs and resources. It effectively distinguishes from siblings like cel_see by noting that 'watch' mode is unavailable during active session.

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 each mode, including when not to use certain modes (e.g., 'cel_see watch mode is unavailable during an active session'). It also highlights the singleton constraint, aiding the agent in choosing this tool appropriately.

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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