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pgatzka

jar-inspector-mcp

jar_search

Read-only

Search inside Java archives without extraction to find property keys, bean names, SQL fragments, or log messages using literal queries or regex.

Instructions

Grep text and source entries inside an archive, returning matching lines.

Use it to find a property key, a bean name, an SQL fragment or a log message without extracting anything. With include_class_strings=True it also searches string constants compiled into .class files.

Args: jar: Archive path, classes directory, or outer.jar!nested.jar. query: Literal text, or a Python regular expression when regex=True. regex: Treat query as a regular expression. ignore_case: Case-insensitive matching. path: Comma-separated globs limiting which entries are searched. include_class_strings: Also search string constants inside .class files. limit: Maximum matches to report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jarYes
pathNo
limitNo
queryYes
regexNo
ignore_caseNo
include_class_stringsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true, and the description reinforces this by saying 'without extracting anything.' It also adds meaningful behavioral detail about searching inside .class files when include_class_strings=True and returning matching lines, going beyond the annotations.

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 compact and well-structured: a one-sentence purpose, a short usage rationale, then a clean Args list. Every sentence adds information without unnecessary padding.

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?

Given the tool has an output schema and annotations, the description fully covers invocation context: what to search, how to target entries, how to enable regex and class-string search, and how to limit results. No critical guidance is missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description carries the full burden for parameters. It provides meaningful explanations for all seven parameters, including jar's support for classes directories and nested jar notation, query's regex option, path glob semantics, and the behavior of include_class_strings.

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 opens with 'Grep text and source entries inside an archive, returning matching lines,' which names a specific verb, resource, and result. It clearly distinguishes itself from sibling tools like jar_list or jar_find_class by focusing on content search rather than listing or class discovery.

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?

It gives concrete use cases: finding a property key, bean name, SQL fragment, or log message. It does not explicitly mention when to prefer a sibling tool, but the use-case framing and content-search focus make the intended context 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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