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nodormu

unreal-mcp-additional-tools

by nodormu

start_csv_profiling

Capture CSV profiling data to identify performance regressions in Unreal Engine. Outputs results to a CSV file for analysis.

Instructions

Start CSV profiling capture for performance regression analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameNoOutput CSV filename (default: auto-generated)
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 doesn't state what happens when profiling starts, whether it overwrites existing files, if it requires a specific environment state, or how long it runs. It also doesn't mention any side effects or prerequisites. The description adds minimal behavioral context beyond the name, which is insufficient given no annotations.

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 a single sentence, efficient and front-loaded. It conveys the essential action without fluff. However, it could be slightly more informative without being verbose, but it earns a high score for conciseness.

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 tool has only one optional parameter and no output schema, the description could be more complete regarding the profiling lifecycle. It doesn't mention the companion stop_csv_profiling tool or any related considerations like file location or data format. The description is adequate for a simple tool but lacks enough context for the agent to fully understand the operation's scope and consequences.

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% because the only parameter (filename) has a description in the schema. The description adds no additional parameter semantics beyond the schema. Since coverage is high, baseline is 3. The description doesn't explain the format or constraints of the filename, but the schema already covers the basic meaning.

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 starts CSV profiling capture for performance regression analysis. It uses a specific verb ('Start') and resource ('CSV profiling capture'), but does not differentiate from the sibling tools like start_trace or stop_csv_profiling, though the purpose is evident.

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?

No guidance on when to use this tool versus alternatives. It doesn't mention that stop_csv_profiling is the counterpart to end the capture, nor does it explain when to use start_csv_profiling instead of start_trace or run_stat_command. The description implies usage for performance regression analysis but lacks explicit context or exclusions.

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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