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natl-set

ANTLR4 MCP Server

by natl-set

visualize-parse-tree

Visualize parse trees from ANTLR4 grammars in ASCII, JSON, or Lisp formats to understand parsing results and debug grammar structure.

Instructions

Visualize the parse tree structure for a given input.

When to use: Understanding parse results, debugging grammar structure, documentation.

Output formats:

  • ascii: Text-based tree with indentation (default)

  • json: Structured JSON tree representation

  • lisp: S-expression style (rule child1 child2 ...)

Parameters:

  • grammar_files: Object mapping filename to content

  • start_rule: Parser rule to start from

  • input: Sample input text

  • format: Output format (ascii, json, lisp)

Example: grammar_files: {"Expr.g4": "grammar Expr; ..."} start_rule: "expr" input: "1 + 2 * 3" format: "ascii"

Returns: ASCII example:

expr
├── term
│   └── factor
│       └── NUMBER '1'
├── PLUS '+'
└── term
    ├── factor
    │   └── NUMBER '2'
    ├── TIMES '*'
    └── factor
        └── NUMBER '3'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesInput text to parse
formatNoOutput format (default: ascii)
start_ruleYesParser rule to start parsing from
grammar_filesYesMap of filename to grammar content
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It details output formats, defaults, and provides a return example, giving agents a clear picture of tool behavior. It does not cover error handling or edge cases, but for a read-only visualization tool, the disclosure is adequate.

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 well-structured with clear sections, but it redundantly repeats parameter information already present in the schema. The example is valuable and front-loaded, making the description easy to scan despite its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides comprehensive information: purpose, when to use, parameters, example, and return format. Despite no output schema, the return example gives a concrete representation of the expected output, making the tool fully usable for an agent.

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?

Schema coverage is 100%, so the schema documents all parameters. The description adds value by clarifying the default format ('ascii') and providing a concrete example mapping parameters to values. The example helps agents understand how to construct input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function with a specific verb ('Visualize') and resource ('parse tree structure'). It distinguishes itself from sibling grammar tools by focusing on visual output of parse trees for given input.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a 'When to use' section listing specific use cases: understanding parse results, debugging grammar structure, and documentation. It does not explicitly mention alternatives or when not to use, but the context is clear.

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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