| copyright | Copyright © Patsnap. All rights reserved. |
| name | alloy-composition-search |
| description | 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. |
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
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
