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codesafer

codesafer

by goldmembrane
OverviewSchemaSourceRelated ServersDiscussionsAdmin
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ai_analyze

Uses CodeBERT deep learning to classify code as malicious or benign, detecting obfuscated payloads and novel attack patterns that static rules overlook.

Instructions

Deep AI analysis of code using the trained CodeBERT model. Classifies code chunks as malicious or benign with confidence scores. Detects obfuscated payloads, novel attack patterns, and threats that static rules may miss.

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>"
    },
    "name": "ai_analyze"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYesPath to the file to analyze with AI

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It mentions classification and detection capabilities but does not disclose behavioral traits like read-only nature, output format, or confidence score interpretation. Adequate but not fully transparent.

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?

Three concise sentences that are front-loaded with the core purpose, then expand on capabilities. No wasted words.

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?

For a simple tool with one parameter and no output schema, the description adequately covers purpose and capabilities. It could mention confidence score range or output format for clarity, but overall is sufficient.

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 a clear description for the single parameter. The description adds no extra semantic detail beyond the schema, so baseline 3 is appropriate.

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: deep AI analysis using CodeBERT, classifying code as malicious/benign with confidence scores. It distinguishes itself from siblings like scan_file by emphasizing AI-based detection of obfuscated payloads and novel patterns.

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

Usage Guidelines3/5

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

The description implies use when static rules may miss threats, but lacks explicit guidance on when to use this tool over siblings like scan_file or scan_rules_file. No clear when-not or alternative recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

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