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natl-set

ANTLR4 MCP Server

by natl-set

impact-analysis

Analyze change impact for ANTLR4 rules before renaming, deleting, or modifying. Identifies dependencies, dependents, usage, recursion, and risk to guide safe edits.

Instructions

Analyze change impact for a specific rule (dependencies, dependents, and risk).

When to use: Before renaming, deleting, or heavily modifying a rule.

Returns:

  • Direct/transitive dependencies (what this rule needs)

  • Direct/transitive dependents (what would be affected)

  • Usage count and recursion status

  • Entry-rule detection and risk level

  • Actionable impact summary

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
from_fileNoOptional: path to grammar file to analyze
rule_nameYesRule name to analyze for downstream and upstream impact
grammar_contentNoThe ANTLR4 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 discloses the return values (dependencies, dependents, usage count, recursion status, entry-rule detection, risk level, summary), which conveys behavioral traits. It doesn't explicitly state read-only behavior or clarify side effects, but 'analyze' implies a safe read operation.

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 well-structured with a summary line, a when-to-use callout, and a bullet list of returns. It is concise, front-loaded, and every sentence adds valuable context.

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

Completeness4/5

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

Given no output schema, the description lists the key output categories, covering what the tool returns. The when-to-use guidance and clear purpose make it complete enough for an agent to select and invoke the tool correctly. Minor gaps exist around parameter precedence and edge cases, but these are not critical for basic usage.

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 coverage is 100%, with each parameter having a description. The tool description doesn't add additional parameter semantics beyond what the schema provides, so the baseline of 3 applies.

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 explicitly states the tool analyzes change impact for a specific rule, listing dependencies, dependents, and risk. This is a specific verb+resource+scope that distinguishes it from siblings like find-rule-usages by focusing on impact analysis.

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 provides a clear 'When to use' instruction: before renaming, deleting, or heavily modifying a rule. It doesn't mention alternatives or when not to use, so it gives clear context without explicit exclusions.

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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