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Tanod MCP server

utilpeek: parse a User-Agent string (browser, OS, device, bot)

parse_user_agent
Read-onlyIdempotent

Parse a User-Agent string into browser, OS, device, and bot signals using ua-parser regexes, helping identify clients while noting self-reported data can be faked.

Instructions

utilpeek: parse a User-Agent string. For known x402 monitors and indexers with call rates use identify_x402_client. A User-Agent is self-reported and easy to fake; is_bot is a heuristic (declared crawlers, HTTP libraries, headless browsers). Parsed with ua-parser and the uap-core regexes (Apache-2.0). Typically under 0.1 s (first call up to 0.5 s). Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/user-agent-parser-api.html

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_agentYesUser-Agent string (1-1024 chars).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
osNo
noteNo
errorNoOnly on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
deviceNo
is_botNo
lengthNo
sourceNo
browserNo
is_mobileNo
bot_reasonNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.8/5.0
Behavior5/5

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

Adds substantial context beyond the annotations: warns that User-Agent is self-reported and spoofable, that is_bot is a heuristic covering declared crawlers, HTTP libraries, and headless browsers, and discloses latency budget and engine provenance (ua-parser/uap-core). It also conveys pricing and free-tier quota. None of this is in the structured fields.

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?

Front-loaded with the purpose, then caveats, engine, latency, and pricing. Multiple ideas in one block, but every sentence earns its place; the only minor concern is that pricing/docs info might be better segmented.

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?

Complete for a single-parameter parse tool: it explains what is parsed, the heuristic nature of is_bot, the engine, latency, and pricing, and it routes to the sibling for x402 detection. Output schema covers the return shape, so nothing is missing.

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

Parameters4/5

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

Only one parameter, so the 100% schema coverage already handles it. The description adds the 1-1024 char bound via the schema reference and clarifies the semantic nature of the input (self-reported string) which the schema doesn't.

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?

Specific verb+resource ('parse a User-Agent string') with the returned dimensions (browser, OS, device, bot) named in the title. It is clearly distinguished from parse_url, parse_phone_number, and identify_x402_client, which it explicitly routes away from.

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

Usage Guidelines5/5

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

Explicitly names the alternative (identify_x402_client) for known x402 monitors/indexers, including the differentiator ('with call rates'). This is proper when-to-use/when-not routing rather than vague hints.

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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