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network_debug_replication

Capture a replication snapshot showing net mode, net driver, connections, object counts, and replicated actors to diagnose network issues.

Instructions

Capture a runtime/editor replication snapshot: net mode, net driver, connections, network object counts, and replicated actor samples.

KB: see knowledge_base/03_GAMEPLAY_FRAMEWORK.md#overview Example: network_debug_replication()

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_actorsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It says 'capture' which suggests a read-only operation, but it does not explicitly state that no modifications are made, nor does it mention prerequisites like a running game/editor or potential performance impact. The list of captured items gives some context, but side effects and requirements are left unaddressed.

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 efficient and well-structured: a single sentence stating purpose and contents, a KB reference, and a short example. It front-loads the main action and avoids unnecessary detail, making it easy to parse quickly.

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

Completeness3/5

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

The description covers the main output categories, which helps understand the result, but it omits the max_actors parameter and any usage prerequisites (e.g., needing a PIE session or editor context). Since an output schema is present (though not shown), return details are handled separately, but the description still lacks guidance on when to use this tool and what the parameter does.

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

Parameters2/5

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

The schema has one optional parameter, max_actors, with a default of 25, but the schema description coverage is 0%. The tool description does not mention this parameter at all, leaving its purpose ambiguous (likely the maximum number of actor samples). The example call omits arguments, so the agent may not know what max_actors controls without external knowledge.

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 with a specific verb 'capture' and a specific resource 'runtime/editor replication snapshot', and enumerates the contents (net mode, net driver, connections, network object counts, replicated actor samples). This distinguishes it from sibling configuration tools like net_set_actor_replicates, which are for setup rather than diagnostics.

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

Usage Guidelines3/5

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

The description implies this is for debugging by capturing a snapshot, but it does not explicitly state when to use it versus alternative replication tools such as net_describe_blueprint_replication or net_get_replication_graph_state. No exclusions or conditions are given, so an agent must infer usage from the diagnostic nature.

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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