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by io.github.seekdaseek
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Text classification

get_classify
Read-onlyIdempotent

Use when an agent needs text sorted into its own categories. Returns a score per label that sums to 1 and the best label, for up to 4,000 characters and 2 to 20 labels. Labels are names, never instructions. Cached 24 hours. Costs 0.005 USDC per call (x402, USDC on Solana, Base, Polygon, Arbitrum or Arc).

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>",
      "labels": "<labels>"
    },
    "name": "get_classify"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesthe text to classify, up to 4,000 characters
labelsYes2 to 20 labels, comma separated, e.g. billing,bug,feature

Output Schema

NameRequiredDescriptionDefault
dataYesthe get_classify payload; a real captured example is free at https://x402.ochinimus.app/api/sample/get_classify
toolYesthe tool that produced this payload

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the safe-read profile (readOnly, idempotent, non-destructive), and the description adds genuinely new operational facts: 24-hour caching and a 0.005 USDC per-call fee via x402 across named chains. The "labels are names, never instructions" note is a useful injection-safety signal. Error/rate-limit behavior is still unstated.

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?

Three sentences, front-loaded with the usage trigger, then output shape, constraints, caching and pricing. Every sentence carries information, though the chain enumeration adds length that borders on list-dumping.

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?

With an output schema present, return values need not be explained (they are described anyway as a bonus), and annotations carry the safety profile. The description supplies cost, caching and input limits, leaving little an agent needs to invoke it correctly, though no failure or retry guidance is given.

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?

Schema coverage is 100%, so the 4,000-character and 2-to-20-label limits are already documented and the description only reinforces them. It does add one semantic constraint absent from the schema – that labels must be names rather than instructions – which justifies a bump above the baseline 3.

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?

States a specific verb and resource: sorting text into caller-provided categories, with a clear output shape (per-label scores summing to 1 plus a best label). It does not explicitly differentiate itself from near-neighbors like get_extract or get_summarize, keeping it short of a 5.

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?

"Use when an agent needs text sorted into its own categories" gives a clear triggering context and implicitly points at the caller supplying labels. There are no when-not conditions or named alternative tools, so it stops at a solid 4.

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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