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esq-builder-mcp

esq-builder-mcp

by mo9652962-ai
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Python
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Esq Parse Wordlist

esq_parse_wordlist

Parse kajweb/dict high-frequency wordlist JSONL files to extract vocabulary ranked by real-exam frequency, adding optional level labels and top-N limits.

Instructions

解析 kajweb/dict 高频词表 JSONL（wordRank=真题词频排序）。

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": {
      "jsonl_path": "<jsonl_path>"
    },
    "name": "esq_parse_wordlist"
  }
}

Input Schema

NameRequiredDescriptionDefault
levelNo标注级别名（如 "四级·高频"）
top_nNo只取前 N 个（如四级核心词取 1162）
jsonl_pathYeskajweb book/*.zip 解压后的 JSONL 文件路径

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden and mostly does not meet it: it does not state that the operation is a read-only parse, whether it writes anything to disk, or how it behaves on malformed JSONL. It only clarifies the meaning of wordRank, which is domain metadata rather than behavioral disclosure.

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?

One dense sentence with the resource front-loaded and no filler; the parenthetical about wordRank earns its place. It is arguably too terse to be maximally useful, but nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists and parameters are fully documented, so return-value explanation is unnecessary. What is missing is pipeline context: which sibling precedes and follows this parse step, and what precondition the jsonl_path implies. Adequate but with clear gaps.

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 description coverage is 100%, so the baseline is 3 and the schema already documents level, top_n and jsonl_path. The description adds only the wordRank semantics, which is a useful gloss on the output ordering rather than meaning for any specific parameter.

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 gives a specific verb (解析/parse) and resource (kajweb/dict 高频词表 JSONL), so the agent knows exactly what is being processed. It does not, however, distinguish this tool from the nearest sibling esq_hot_words, leaving the boundary between 'parse a wordlist' and 'get hot words' to inference.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no stated prerequisite (e.g. that the JSONL must first be extracted from book/*.zip), and no reference to any alternative such as esq_hot_words or esq_build_package. The agent must guess where this fits in the esq_* pipeline.

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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