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Universal Game Modder

Universal Game Modder

by doritoman90000
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

search_binary_pattern

Scan binary game files for text patterns using stream-based searching to locate offsets and matches needed for modding, patching, or reverse engineering.

Instructions

Search for text patterns in a binary file using stream-based approach.

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": {
      "file_path": "<file_path>",
      "patterns": "<patterns>"
    },
    "name": "search_binary_pattern"
  }
}

Input Schema

NameRequiredDescriptionDefault
patternsYesText patterns to search for
file_pathYesPath to binary file
max_resultsNoMax results per pattern (default 50)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

C2.9/5.0
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 mentions a 'stream-based approach' but does not explain what that implies (e.g., memory efficiency, handling of large files), nor does it state return format, error handling, or whether the operation is read-only. For a search tool with no annotations, this is a significant gap.

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, front-loaded sentence that efficiently states the tool's action and resource. It avoids unnecessary words, though it could benefit from additional concise details about usage or behavior to be more helpful.

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?

Given the absence of annotations and output schema, the description is incomplete. It does not explain return values, error conditions, or how the stream-based approach differs from other search tools. For a tool with three parameters and no structured behavioral cues, more completeness is needed to guide correct invocation.

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 has 100% description coverage for all three parameters, so the baseline is 3. The description adds nothing beyond the schema—it does not clarify pattern matching semantics (e.g., literal vs. regex), encoding expectations, or how 'stream-based' affects the file_path parameter. Thus, it earns the baseline score.

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 states a specific verb (Search) and resource (text patterns in a binary file), making the core purpose clear. However, it does not differentiate itself from sibling tools like pattern_scan or pattern_scan_all, which likely share overlapping functionality. Without explicit differentiation, an agent must infer distinctions.

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 provides no guidance on when to use this tool versus alternatives such as pattern_scan or pattern_scan_all. It lacks any usage context, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.

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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