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π¨++ New Plug-in Integrated ++ π¨
- Boundary-layer conductance plug-in integrated
- Updated helios-core to v1.3.52
- Removed wheel support for macos-13, as it is no longer a supported GitHub runner platform. Instead, Intel macs are still supported through macos-14.
@@ -664,32 +664,209 @@ static thread_local std::vector<unsigned int> static_result; // Not just static
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- Purpose: persist structured facts and relationships about this repo, projects, and collaborators using the knowledge-graph memory tools.
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- Safety: summarize what you plan to store before writing; do not store secrets or API keys.
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### **CRITICAL: How MCP Search Actually Works**
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MCP memory uses **simple case-insensitive substring matching** - NOT semantic search or AI understanding. It searches for your query string within entity names, types, and observations using basic `toLowerCase().includes()`.
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**Key Implications:**
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- Query "test crash" searches for the EXACT phrase "test crash" as a substring
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- Query "boundary-layer conductance test failure" WON'T match unless that exact phrase exists
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- NO fuzzy matching, NO synonyms, NO semantic understanding
Use `append_observations` to add a brief dated note when behavior or conventions change.
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**Creating Entities and Relations:**
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```python
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# 1. Create entities with atomic observations
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mcp__memory__create_entities(entities=[
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{
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"name": "pytest_forked_plugin",
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"entityType": "tool",
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"observations": [
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"Prevents ctypes contamination between tests",
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"Runs each test in subprocess for isolation",
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"Required for PyHelios test suite reliability"
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]
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}
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])
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# 2. Create meaningful relations
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mcp__memory__create_relations(relations=[
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{
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"from": "pytest_forked_plugin",
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"to": "ctypes_contamination_issue",
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"relationType": "solves"
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}
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])
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# 3. Update existing entities (use add_observations, NOT append_observations)
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mcp__memory__add_observations(observations=[
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{
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"entityName": "pytest_forked_plugin",
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"contents": ["Added to PyHelios in version 0.1.4"]
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}
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])
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```
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### When to read memory
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Before large refactors, onboarding explanations, or when the task mentions prior decisions, call `search_entities` or `search_relations` with a concise query, then cite what you found.
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### Usage examples
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- βSearch memory for entities about βHeliosβ and βstomatal conductanceβ and summarize relevant observations.β
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- βCreate entities for βGEMINI projectβ (type: project) and βNonpareil orchard datasetβ (type: dataset), then relate them with relationType βusesβ.β
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**ALWAYS search before creating** to avoid duplicates:
mcp__memory__open_nodes(names=["Entity_Name"]) # Specific retrieval
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mcp__memory__read_graph() # Export full graph
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```
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### References inside prompts
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- To reference MCP resources or trigger tools, you can type `/mcp` in Claude Code to view available servers and tools, or mention the server by name in your instruction, e.g., βUsing the `memory` server, run `search_entities` for βtrellisβ.β See Anthropicβs MCP guide for listing and managing servers.
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**When Search Returns Nothing:**
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1. Try shorter query (use word stem: "visual" not "visualization")
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