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GSEP-MCP

GSEP-MCP

Official
by gsepcore
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TypeScript
Hybrid

gsep_scan_input

Scan user input to detect prompt injection, role hijacking, data exfiltration, and encoding evasion before sending to your LLM.

Instructions

Scan user input with C3 Content Firewall (53 patterns). Detects prompt injection, role hijacking, data exfiltration attempts, encoding evasion, and more. Use this before sending any external content to your LLM.

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": {
      "content": "<content>"
    },
    "name": "gsep_scan_input"
  }
}

Input Schema

NameRequiredDescriptionDefault
sourceNoContent source trust leveluser
contentYesUser input or external content to scan for prompt injection
genome_idNoGenome ID for trust registry contextgsep-scanner

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.9

TDQS

A3.7/5.0
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It does not mention side effects, state changes, authentication needs, or performance. It only lists detection categories, which is helpful but insufficient for full transparency.

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?

Two sentences, front-loaded with purpose and scope, followed by usage guidance. No wasted words.

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

Completeness3/5

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

Tool is simple and schema covers parameters fully, but description lacks information about return value or output format. Without output schema, some guidance would be beneficial for completeness.

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%, so baseline is 3. The description adds context by listing detection types but does not elaborate on individual parameters beyond what the schema already provides.

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 it scans user input using C3 Content Firewall and lists specific attack types detected. It is distinct from siblings like gsep_scan_output, which likely handles output scanning.

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?

Explicitly states 'Use this before sending any external content to your LLM', providing clear when-to-use context. Does not explicitly mention when not to use or alternatives, but sibling differentiation is implicit.

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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