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@altirs/mcp-server

@altirs/mcp-server

by altirs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Classify content safety

check_safety
Read-onlyIdempotent

Detect harmful content such as violence, hate, harassment, self-harm, sexual content, or illegal activity. Get severity ratings, a safety score, and a blocked flag to guide moderation.

Instructions

Classify text for harmful or unsafe content — violence, hate, harassment, self-harm, sexual content, illegal activity, and similar categories. Use this to moderate user-generated content or to screen an AI response before it is shown to a user. Returns the safety categories that were triggered, each with a severity (high / medium / low), plus an overall safe boolean, blocked, and a 0-1 score (1 = clean). High-severity categories mean the content should not be displayed or acted on.

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": {
      "text": "<text>"
    },
    "name": "check_safety"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe text to classify.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, establishing safety. The description adds valuable behavioral detail by disclosing the return structure: triggered categories with severity, 'safe' boolean, 'blocked', and a 0-1 score, plus the actionable interpretation that high-severity content should not be displayed. This goes beyond annotations and helps the agent use the output properly.

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 moderately long but well-structured: it starts with the core purpose, then usage, then output details, and finally interpretation. It is front-loaded with the primary action. Some phrasing could be tightened, but every sentence serves a purpose without redundancy.

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?

For a single-parameter classification tool with no output schema, the description is fully complete. It covers the purpose, usage scenarios, output format (severity, booleans, score), and the meaning of results. An agent can call the tool correctly and interpret the response with no additional information needed.

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 the 'text' parameter already described as 'The text to classify.' The description mentions 'text' as the subject but does not add any additional constraints, formats, or nuances. Since the schema fully documents the parameter, the baseline of 3 applies; no extra value from the description.

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 specifies the verb 'Classify' and the resource 'text for harmful or unsafe content', listing concrete categories. It is clear and specific, but it does not explicitly differentiate from sibling tools like check_content, which may have overlapping scope. The purpose is unambiguous enough for an agent to select it.

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 gives explicit use cases: 'moderate user-generated content or to screen an AI response before it is shown to a user.' This provides clear contextual guidance on when to invoke the tool, but it does not mention when not to use it or name alternative sibling tools, so it falls short of the top score.

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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