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WalnutLink A-share Depth Desk

a_share_chain_edge

Use when the user asks HOW two Chinese companies relate: who supplies whom, who competes with whom, who owns equity in whom, or whether an acquisition target overlaps with the acquirer's product line. Every relationship returned carries a verbatim quote from a filed document (annual report, prospectus, restructuring draft, related-party announcement) and a confidence tag: primary = the filing says it outright, secondary = broker or media, inferred = our deduction with no direct textual proof. Unlisted companies work as inputs too, which matters for acquisition targets. If the pair has not been researched it says so plainly instead of guessing - absence of a record is not evidence of no relationship. Do NOT call this for prices, valuation or index levels.

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": {
      "a": "<a>",
      "b": "<b>"
    },
    "name": "a_share_chain_edge"
  }
}

Input Schema

NameRequiredDescriptionDefault
aYesCompany A: 6-digit A-share code or Chinese name.
bYesCompany B: 6-digit code or name. An unlisted company name also works (e.g. an acquisition target).
relationNoOptional filter when the pair has more than one link.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

No annotations exist, so the description carries the entire burden and does so richly: it discloses the confidence taxonomy (primary/secondary/inferred), that every edge carries a verbatim source quote, that unlisted companies are valid inputs, and that missing data is reported explicitly rather than guessed. This is exactly the behavioral context an agent needs before trusting output.

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 trigger, then evidence model, then inputs, then exclusions - a sensible order with no filler sentences. Slightly long, and the unlisted-company point is touched twice, but every sentence contributes information.

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?

There is no output schema, yet the description explains the return shape (verbatim quote + confidence tag) and the negative-result case, so an agent knows what to expect and how to interpret it. Complete for a 3-parameter lookup tool with no annotations.

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 schema already documents all three params including the relation enum and unlisted-name support. The description reinforces the unlisted-input point and the optional relation filter but adds little syntax or format detail beyond the schema. Baseline 3 applies.

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?

Opens with a specific verb+resource+scope: 'HOW two Chinese companies relate', then enumerates the exact relationship types (supplier, competitor, equity, product overlap). An agent can immediately tell this is the pairwise-relationship tool rather than the single-entity answer or deep-research siblings.

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?

Gives explicit trigger conditions ('when the user asks HOW...'), a clear exclusion ('Do NOT call this for prices, valuation or index levels'), and edge-case handling ('if the pair has not been researched it says so plainly'). Nothing about selection is left to inference.

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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