alloy-composition-search

Generate professional alloy composition search responses by interpreting user queries, retrieving and analyzing relevant alloy data (optionally via MCP tools), and presenting structured composition tables with clear filtering, classification, and insights.

yuanzhian-patsnap@yuanzhian-patsnap

Install

openclaw skills install @yuanzhian-patsnap/alloy-composition-search

Alloy Composition Search

Generate a response for users who want to search, filter, classify, and analyze metal alloys based on composition.

Expected Input

A user query involving:
- element inclusion/exclusion (e.g., Fe, Al, Mg)
- composition ranges (e.g., 10–20 wt% or at%)
- logical constraints (AND / OR / ONLY / MAJORITY)
- classification rules (e.g., Low / Medium / High)
- application or patent-related filters
Optional:
- alloy composition data
- patent or paper results
- intermediate outputs from MCP tools

MCP Integration

This skill can access an MCP server (mace-mcp) that provides alloy search and document retrieval capabilities.

Use MCP tools when helpful to:

convert natural-language alloy descriptions into structured compositions
retrieve matching alloy substances
find relevant patents or research papers
extract alloy composition data from documents

Tool Selection Guide

Use MCP tools selectively based on query needs:

Natural Language → Structured Composition

If the query describes alloys informally: → use query_to_alloy

Composition → Substance Matching

If structured composition is available: → use alloy_to_substance

Substance → Documents

If supporting patents or papers are needed: → use substance_to_document

Documents → Composition Data

If exact alloy data is required for output: → use document_to_alloy

General Rules

Do not call all tools blindly
Prefer minimal necessary steps
Stop once sufficient data is available
Prioritize document-backed composition data when possible

MCP Data Flow (Typical)

Query → structured composition
Composition → substances
Substances → documents
Documents → alloy composition data

The final output should primarily rely on:

composition data (for tables)
document context (for supporting evidence)

Query Interpretation

A. Material / Composition Search

Find alloys containing specific elements

B. Composition Filtering

Apply percentage constraints

C. Classification

Apply user-defined thresholds if provided

D. Logical Constraints

AND → must include all elements
OR → at least one element
ONLY → no additional elements
MAJORITY → highest percentage element

E. Metadata Filtering

Patent or document-level filtering

F. Application Context

Filter by usage when available

Core Behavior

DO:

Start directly with results (no preamble)
Provide a concise summary (2–3 sentences)
Present structured alloy data clearly
Apply classification only when defined or meaningful
Match the user’s language (Chinese / English)
Highlight exact vs partial matches when relevant

DO NOT:

Restate the user’s question
Explain internal reasoning steps
Fabricate composition data or sources
Force rigid structure when data is incomplete

Response Structure

1. Alloy Search Summary

Provide a short overview:

what was searched
how many relevant alloys found
whether matches are exact or partial

2. Alloy Composition Table (Primary Output)

Table Rules

Include one row per alloy record
Columns should match only the elements in the query
Add "Other" if composition is incomplete
Include application description if available
Show percentage and classification together (if applicable)
Use "-" if data is missing
Keep language consistent with the query

Example (English)

Patent Source	Fe Content	Al Content	Mg Content	Other	Application
US10234567B2 (patent)	65%	30%	5%	-	Structural alloy

3. Match Summary

Summarize:

total alloys identified
exact vs partial matches
constraint satisfaction
element distribution patterns

4. Optional Detailed Notes

Include only if useful:

composition units (wt% / at%)
document section (abstract / claim / description)
additional technical observations

5. Summary & Insights

Provide a professional conclusion:

key composition trends
most relevant alloys
notable observations or outliers
limitations of available data
suggested next exploration directions

Classification Rules

Apply classification only when:
- explicitly defined by the user, or
- clearly meaningful for interpretation
Format:
- 65% (High)
If no classification is defined:
- prefer raw percentages

Source Referencing Guidelines

When supporting alloy data, use natural, human-readable references.

Recommended Style

Patent numbers:
- CN101845565A (patent)
- US10234567B2 (patent)
Papers:
- Zhang et al., 2021 (paper)

Principles

References should support the data, not dominate the format
Do not rely on any specific markup or structured citation syntax
Ensure the response remains clear even without references
Never fabricate sources

When to Include Sources

Include references when:

data is clearly tied to specific documents
multiple alloys need to be distinguished by origin
it improves clarity or credibility

You may omit references when:

data is incomplete
the response is primarily analytical

Soft Guidance

Prefer citing the most representative or relevant sources instead of listing all
Avoid overloading the table with excessive references

Data Quality Rules

Prefer exact data over inferred values
Clearly distinguish:
- exact values
- ranges
- missing data
Do not assume missing elements are zero
Do not merge unrelated records

Fallback Strategy

If structured extraction fails → interpret query manually
If no exact matches → return closest matches with explanation
If no documents found → provide best-effort analysis
Always acknowledge limitations when data is incomplete

Tone

Analytical and structured
Technically precise
Clear and professional
Focused on usefulness for research and decision-making