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benchmark_project

Measure token efficiency by comparing raw file reads against compact responses for symbol lookup, file exploration, search, and impact analysis scenarios.

Instructions

Synthetic token efficiency benchmark: compare raw file reads vs trace-mcp compact responses across symbol lookup, file exploration, search, and impact analysis scenarios.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queriesNoQueries per scenario (default 10)
seedNoRandom seed for reproducibility (default 42)
formatNoOutput format (default: json)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the tool performs benchmarking across scenarios but doesn't disclose behavioral traits like whether it's read-only vs. mutating, execution time, resource requirements, or output characteristics. 'Synthetic' and 'benchmark' imply read-only analysis, but this isn't explicitly stated.

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 a single, efficient sentence that packs substantial information: the tool's purpose (benchmarking), what's being compared (two response types), and the four scenarios covered. Every word earns its place with zero wasted text.

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?

For a benchmarking tool with 3 parameters and 100% schema coverage but no output schema, the description provides adequate purpose but lacks context about output format, performance characteristics, or integration with sibling tools. It's minimally complete but leaves gaps in behavioral understanding.

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 documents all three parameters with descriptions and constraints. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate schema coverage.

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 performs a 'synthetic token efficiency benchmark' comparing 'raw file reads vs trace-mcp compact responses' across four specific scenarios. It provides a specific verb ('compare') and resource ('responses'), though it doesn't explicitly differentiate from sibling tools which appear to be code analysis and project management tools rather than benchmarking utilities.

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 doesn't mention prerequisites, appropriate contexts, or exclusions. With 100+ sibling tools focused on code analysis and project management, this benchmarking tool stands out but lacks explicit 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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