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lilbrain_pipeline

Trace a named pipeline or pattern in your codebase to see each processing stage from parse to compile. Understand the flow and pinpoint where issues occur.

Instructions

Trace a named pipeline or pattern (parse, validate, handle, compile, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPipeline name
Install Server

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the operation is to 'trace' but does not explicitly say whether this is read-only, what output or trace format the agent should expect, whether it searches the codebase, or what limitations exist.

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 one concise sentence that front-loads the core action and resource. Every word contributes to the meaning, with no filler or redundant restatement of the tool name.

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?

Despite having only one required parameter, the tool exists alongside many similar analysis siblings, including lilbrain_trace, and the description does not explain what a 'trace' returns or how the tool behaves. With no output schema and no annotations, the description is too sparse to fully guide an agent in selecting and invoking it correctly.

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

Parameters4/5

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

The single parameter `name` is already fully described in the schema as 'Pipeline name', and schema coverage is 100%. The description adds value by saying 'or pattern' and listing examples like parse, validate, handle, and compile, which helps the agent understand what kinds of names are acceptable beyond a literal pipeline name.

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 ('Trace') and a clear resource ('named pipeline or pattern') with illustrative examples (parse, validate, handle, compile), so an agent can identify the general purpose. However, it does not explicitly distinguish this tool from the sibling lilbrain_trace, which also uses 'trace' in its name.

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 implies the tool should be used when the agent needs to trace a pipeline or pattern, but it gives no explicit guidance on when to choose this over lilbrain_trace, lilbrain_function, or other siblings. No exclusions or alternative conditions are provided.

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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