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lilbrain_runtime

Correlate OpenTelemetry/Jaeger traces with the static call graph to reveal hot production paths and cold code. Pinpoint bottlenecks and dead code from trace JSON files.

Instructions

Correlate OpenTelemetry/Jaeger traces with static call graph. Find hot production paths and cold code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trace_dirNoDirectory containing trace JSON files
trace_fileNoPath to trace JSON file
Install Server

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the burden. 'Correlate' and 'find' clearly signal a read-only analysis operation, which helps, but it doesn't disclose any behavioral details such as input precedence between trace_dir and trace_file, whether it requires at least one parameter, or what kind of output it returns. It avoids contradictions but leaves operational behavior under-specified.

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?

Two sentences, no wasted words, and the core purpose is front-loaded. It conveys the essential idea and key outcome without bloat or repetition.

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?

This is a complex analysis tool with no output schema and two optional parameters. The description doesn't explain what the tool returns, whether one of trace_dir/trace_file is needed, how the correlation is performed, or how results should be interpreted. Given the absent output schema and the general-purpose sibling set, more operational context is required for an agent to invoke it correctly.

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% — both trace_dir and trace_file have descriptions in the input schema. The description itself adds nothing about parameter formats, precedence, or relationships, so the baseline 3 applies.

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 uses a specific verb ('correlate') and identifies its resources ('OpenTelemetry/Jaeger traces with static call graph'), plus the goal ('Find hot production paths and cold code'). It distinguishes itself from siblings like lilbrain_trace and lilbrain_hotspots by emphasizing the correlation between runtime traces and static structure, though it could be more explicit about how it differs from each sibling.

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 gives no explicit when-to-use, when-not-to-use, or alternative tools. It implies usage is for trace-to-callgraph correlation, but among 22 siblings—including lilbrain_trace, lilbrain_hotspots, and lilbrain_deadcode—an agent gets no routing guidance about choosing this tool over those.

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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