jieba-mcp

Part-of-speech tagging

pos_tag

Read-only

Segment Chinese text and assign a part-of-speech tag to every word, enabling grammatical analysis and downstream NLP tasks.

Instructions

Segment Chinese text and tag each word with its part of speech.

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>"
    },
    "name": "pos_tag"
  }
}

Input Schema

Name	Required	Description	Default
`hmm`	No	Use the HMM model to discover words not in the dictionary.
`text`	Yes	Chinese text to process.

Output Schema

Name	Required	Description	Default
`count`	Yes
`tokens`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Nov 11, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds nothing about tokenization behavior, handling of unknown words (the hmm parameter's effect), or output shape; it stays at the level of the one-line purpose.

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?

A single sentence with zero waste, front-loading the segmentation step before the tagging step. Nothing is redundant or padded.

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?

The tool is simple: one required text parameter, one optional flag, and an output schema that relieves the description of explaining return values. What remains is a minor gap around how the HMM option affects results and how it relates to the sibling 'segment' tool.

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%, including a clear explanation of the hmm flag for out-of-dictionary words, so the schema carries the parameter burden. The description adds no parameter meaning beyond the schema and does not mention the hmm option at all, which is the baseline-3 outcome when the schema is complete.

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: segmenting Chinese text and tagging each word with its part of speech. That distinguishes it in substance from the sibling 'segment' tool, since POS tagging is the added operation, but the description never names the sibling or explicitly contrasts the two.

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

Usage Guidelines3/5

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

There is no explicit when-to-use or when-not-to-use guidance. An agent can infer that this is the choice over plain 'segment' when part-of-speech labels are needed, but that inference comes from the tool name and the phrase 'tag each word with its part of speech' rather than from stated routing 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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