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LiLBrain

LiLBrain

by MangoByteLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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lilbrain_trace

Trace call chains from a function up to a specified depth to identify code execution paths and dependencies.

Instructions

Depth-limited call chain trace from a function.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>"
    },
    "name": "lilbrain_trace"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesStarting function
depthNoMax depth (default 5)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior3/5

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

The phrase 'Depth-limited' discloses an important behavioral constraint, and 'trace' implies a read-only analysis operation. However, with no annotations, the description still leaves side effects, traversal direction, and output behavior to inference.

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 terse phrase with no filler, and the key constraint 'Depth-limited' is front-loaded. It earns its place, though it reads as a noun fragment rather than a complete action-oriented sentence.

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?

Given two simple parameters and no output schema, the description conveys the starting point and depth limitation adequately. It does not explain what the returned call chain looks like, how depth is counted, or how this trace differs from sibling tools, which would help an agent 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?

The input schema already documents both parameters with 100% coverage ('Starting function' and 'Max depth'), so the description does not need to carry much parameter meaning. It adds only a lightweight mapping: 'from a function' to the name parameter and 'depth-limited' to the depth parameter, with no new format or interpretation details.

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 identifies a specific operation: a depth-limited call chain trace starting from a function, which goes beyond the bare tool name. It is not fully differentiated from siblings like lilbrain_callers or lilbrain_dataflow, but the core purpose is reasonably clear.

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 is given about when to use this tool versus alternatives such as lilbrain_callers, lilbrain_function, or lilbrain_dataflow. The description implies only that a starting function is needed, with no conditions, exclusions, or selection criteria.

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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