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emit_event

Emit consultation events for reactive processing in multi-agent systems, triggering suggestions based on event types like gap detection or pattern assessment.

Instructions

EVENT (emit) — Emit a consultation event for reactive processing. Valid types: gap_found, pattern_assessed, coverage_threshold_reached, coverage_dropped, plan_created, state_conflict. Returns a reactive suggestion based on the event type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
consultation_idYesThe consultation session ID
event_typeYesType of event to emit
dataNoOptional event payload (JSON object)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'emits' an event and returns a 'reactive suggestion', but lacks details on side effects (e.g., if it modifies state), authentication needs, rate limits, or error handling. This is inadequate for a tool that likely triggers downstream processing.

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 concise and front-loaded, starting with the core action ('emit a consultation event') followed by key details. Both sentences are informative, though the second sentence could be slightly more structured (e.g., separating the enum list from the return statement).

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?

Given the complexity of an event-emitting tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'reactive processing' entails, what a 'reactive suggestion' looks like, or potential side effects, leaving significant gaps for the agent to understand the tool's behavior and outputs.

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 has 100% description coverage, clearly documenting all three parameters. The description adds minimal value by listing the valid event types (which are already in the schema's enum) and mentioning the optional 'data' payload, but doesn't provide additional semantics beyond what the schema offers.

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

Purpose4/5

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

The description clearly states the tool's purpose with a specific verb ('emit') and resource ('consultation event'), and it specifies the valid event types. However, it doesn't explicitly differentiate this tool from its siblings like 'log_pattern_assessment' or 'get_events', which might have overlapping domains.

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 provides no guidance on when to use this tool versus alternatives. It mentions 'reactive processing' but doesn't clarify when this emission is appropriate compared to other event-related tools like 'get_events' or 'log_pattern_assessment', leaving the agent without usage context.

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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