agentsvc-mcp

useragent_parse

Parse User-Agent strings to extract browser, engine, OS, device type, vendor, model, and CPU details, while flagging bots and AI crawlers like GPTBot and ClaudeBot.

Instructions

User-Agent Parser — Parse a User-Agent string into browser, engine, OS, device type/vendor/model and CPU, and flag bots and AI crawlers (GPTBot, ClaudeBot, PerplexityBot ...). Params: user_agent (required). Cost: $0.002 USDC per call. Latency p99: 200ms.

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": {
      "user_agent": "<user_agent>"
    },
    "name": "useragent_parse"
  }
}

Input Schema

Name	Required	Description	Default
`user_agent`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 21, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses important operational traits: cost ($0.002 USDC per call), p99 latency (200ms), and what the response contains (browser, engine, OS, device type/vendor/model, CPU, bot flags). It does not cover auth requirements or error behavior, but for a simple read-only parse utility this is a strong disclosure.

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 tightly packed sentences, front-loaded with purpose and output details. Every sentence earns its place: the first defines the operation and return categories, the second specifies the required parameter, the third gives cost and latency. No filler.

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?

Given the simple 1-parameter schema, no annotations, and no output schema, the description is nearly complete: it explains what the tool returns (parsed fields and bot flags), the required input, and cost/latency. It does not describe the exact output structure or error cases, but for this complexity level those gaps are minor.

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

Parameters2/5

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

Schema description coverage is 0% (the single parameter has no description in the schema). The description only states 'user_agent (required)', which is redundant with the schema's required field. It adds no format, example, or semantic meaning beyond what the schema already defines.

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?

States a specific verb (Parse) and resource (User-Agent string), and enumerates the output components (browser, engine, OS, device, CPU) plus bot/AI crawler detection. An agent can immediately distinguish this from all sibling tools, none of which parse user-agent strings.

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 provides clear context: use this tool when you have a User-Agent string and need its parsed components or bot classification. It does not state exclusions or alternatives, but none exist among the siblings, so the context is sufficient without them.

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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