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Search words

search_words

The immit dictionary entries that match a string: a headword prefix, an alternative spelling, an inflected form (食べた finds 食べる, walked finds walk) or, for Japanese, a kana reading (たべる finds 食べる). Results are ranked: an exact headword or spelling first, then an exact inflected form or reading, then the prefix matches, each rank by frequency with the most common first. Each result is a whole entry, as lookup_word returns it. Use it to turn an inflected form, a reading or a fragment into the headword, or to list the words that start with a string; use lookup_word when you already hold the dictionary form.

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": {
      "lang": "<lang>",
      "query": "<query>"
    },
    "name": "search_words"
  }
}

Input Schema

NameRequiredDescriptionDefault
langYesThe language of the word: eng for English, jpn for Japanese
limitNoEntries to return, 10 unless given, capped at 25; each is a whole entry, so ask for few
queryYesA headword prefix, a spelling, an inflected form or a kana reading, whatever the case

Output Schema

NameRequiredDescriptionDefault
entriesYes
metadataYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
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 behavioral burden. It discloses matching modes, ranking order, frequency-based ordering, and that each result is a whole entry, which is substantial. It does not discuss no-match behavior, case handling beyond the schema, permissions, or error conditions, so it falls short of complete.

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 front-loaded with the core matching behavior and then gives ranking and usage guidance. Every sentence carries useful information, though the long ranking sentence is dense and 'immit' is an unclear term. It remains appropriately sized for the tool.

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?

The tool is a search operation with three simple parameters and an output schema. The description explains match semantics, ranking, result format, and the relationship to lookup_word. Given the output schema exists, return-value details need not be repeated, and nothing essential is missing.

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 the schema already documents lang, limit, and query. The description reinforces query semantics with examples ('食べた finds 食べる, walked finds walk') but does not add much beyond the schema. Baseline 3 is appropriate when structured fields do the heavy lifting.

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 states a specific verb and resource: dictionary entries matching a string. It enumerates match modes (headword prefix, spelling, inflected form, kana reading), explains ranking, and distinguishes the tool from lookup_word. An agent can understand exactly what it searches.

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?

It explicitly says to use this tool to turn an inflected form, reading, or fragment into a headword, or to list words starting with a string. It also names the alternative and condition: 'use lookup_word when you already hold the dictionary form.' That is clear when-to-use and when-not guidance.

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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