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@ainize/mcp

@ainize/mcp

Official
by ainblockchain
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TypeScript
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Family tree

family_tree
Read-only

Trace a knowledge item's ancestors, descendants, and versions to check for a cheaper parent or newer release before buying.

Instructions

Ancestors, descendants and versions around one knowledge, walked from the ledger graph. Use it to see whether a cheaper parent or a newer version exists before buying. Free.

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": {
      "id": "<id>"
    },
    "name": "family_tree"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesthe knowledge id, e.g. "krx-all-2761"
depthNo
directionNoboth

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds genuinely new facts: the data comes from the ledger graph and the call is free, which matters for cost-aware agents. It says nothing about result size, truncation, or traversal cost for deeper walks.

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?

Two tight sentences with zero filler: the first defines scope, the second defines purpose, and 'Free' is a single-word cost signal. Purpose is front-loaded.

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?

For a 3-parameter, flat-schema, no-output-schema tool, the description supplies purpose, data source, cost, and a motivating scenario. The remaining gap is the shape of the returned tree (nodes, prices, version markers) and how depth affects it, which matters with no output schema.

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 only 33% (only id is documented), so the description must compensate. It partially does: 'ancestors' maps naturally to direction=up, 'descendants' to down, and 'around one knowledge' anchors the id. It never explains what depth measures (generations vs. edge hops) despite the 1-4 range.

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 names a specific resource shape: ancestors, descendants, and versions around one knowledge node, walked from the ledger graph. An agent can tell this is a graph-traversal read, distinct from get_knowledge or search_knowledge, though no sibling is named explicitly.

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?

It gives a concrete decision context: 'Use it to see whether a cheaper parent or a newer version exists before buying.' That is a clear when-to-use trigger tied to price comparison. It does not state when not to use it, or which sibling to prefer for plain retrieval.

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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