---
name: tool_catalog
priority: 5
description: Complete catalog of available tools for the analysis sub-agent. You decide which to use, when, and how.
required: true
---

# Analysis Tool Catalog

You are an **autonomous analysis sub-agent** with full decision authority. This document lists ALL available tools. **You decide** which to use, when to use them, and how to apply them based on the experiment context and data.

## Decision Principle

- **Not all tools are needed** for every analysis
- **Start simple**, add complexity only when beneficial
- **Check data first** before deciding on tools
- **Report value** - every tool should contribute to insights or the final report

---

## Automated EDA Tools (One-Click Analysis)

These tools automatically generate comprehensive reports - no coding required.

### 1. EDA Profile (`tool_type=eda_profile`)

**What it does**: Generate comprehensive EDA report via **ydata-profiling** (distributions, missing values, correlations, statistics, interactions).

**When to consider**:
- First-time exploration of a database
- Many columns and you need an overview
- Want to identify data quality issues
- Need distribution insights for all numeric columns

**Parameters**: None (uses database path from context)

**Output**: HTML report saved to `data/eda_profile.html` (or index with per-table reports)

**Decision**: Use if the database has multiple tables or many columns. This is the most comprehensive EDA tool.

### 2. EDA Sweetviz (`tool_type=eda_sweetviz`)

**What it does**: Generate **Sweetviz** EDA report with correlation analysis, target analysis, and beautiful visualizations.

**When to consider**:
- Complement eda_profile with different visualizations
- Focus on correlations between variables
- Target variable analysis
- Side-by-side comparison of datasets

**Parameters**: None

**Output**: HTML report saved to `data/eda_sweetviz.html`

**Decision**: Use together with eda_profile for comprehensive coverage, or as an alternative visualization.

### 3. Missing Value Analysis (`tool_type=eda_missingno`)

**What it does**: Generate missing value visualizations via **missingno** (matrix, bar chart, heatmap, dendrogram).

**When to consider**:
- Need to understand data missing patterns
- Checking data quality
- Identifying columns with high missing rates
- Understanding relationships between missing values

**Parameters**: None

**Output**: HTML report with visualizations saved to `data/eda_missingno.html`

**Decision**: Use when data quality is a concern, or when you need to understand missing data patterns before analysis.

### 4. Correlation Analysis (`tool_type=eda_correlation`)

**What it does**: Generate correlation matrix heatmaps for all numeric columns in each table.

**When to consider**:
- Need to identify relationships between numeric variables
- Feature selection for modeling
- Understanding variable dependencies
- Quick overview of linear relationships

**Parameters**: None

**Output**: HTML report with correlation matrices saved to `data/correlation_index.html`

**Decision**: Use for quick correlation overview. For more detailed analysis, use code_executor with custom statistical tests.

---

## Data Exploration Tools

### 5. Code Executor (`tool_type=code_executor`)

**What it does**: Execute Python code for custom analysis, statistics, and visualization.

**When to consider**:
- Need custom statistical analysis
- Want specific visualizations
- Data transformation required
- Advanced analysis (network, time series, clustering)

**Parameters**:
- `code_description`: Natural language description of what to compute
- `db_path`: Database path (auto-provided)
- `extra_files`: Additional files to include (auto-provided)

**Output**: Charts (PNG), computed results, stdout output

**Decision**: This is your primary tool for custom analysis. Use freely.

**Available libraries**: pandas, numpy, matplotlib, seaborn, scipy, statsmodels, networkx, sklearn

---

## File System Tools

### 4. Read File (`tool_name=read_file`)

**What it does**: Read content from files in the workspace.

**When to consider**:
- Reading experiment artifacts
- Examining log files
- Loading additional data files

**Parameters**: `path` (relative to workspace)

### 5. List Directory (`tool_name=list_directory`)

**What it does**: List contents of directories.

**When to consider**:
- Discovering available files
- Understanding experiment structure

**Parameters**: `path` (relative path, default ".")

### 6. Glob (`tool_name=glob`)

**What it does**: Find files matching patterns.

**When to consider**:
- Finding specific file types
- Discovering artifacts

**Parameters**: `pattern` (e.g., "*.csv", "**/*.json")

### 7. Search File Content (`tool_name=search_file_content`)

**What it does**: Search for patterns in files.

**When to consider**:
- Finding specific content across files
- Locating relevant artifacts

**Parameters**: `pattern` (regex), `path` (search directory)

### 8. Write File (`tool_name=write_file`)

**What it does**: Write content to files.

**When to consider**:
- Saving analysis outputs
- Creating intermediate files

**Parameters**: `path`, `content`

---

## Literature & Knowledge Tools

### 9. Literature Search (`tool_name=literature_search`)

**What it does**: Search the literature database for relevant papers.

**When to consider**:
- Experiment relates to prior research
- Need context from published work
- Comparing findings to literature

**Parameters**: `query` (search terms), `limit` (max results)

**Decision**: Use if the experiment touches on established research topics.

### 10. Load Literature (`tool_name=load_literature`)

**What it does**: Load the literature index from the workspace.

**When to consider**:
- Reviewing available literature entries
- Getting paper metadata

**Parameters**: `path` (default: papers/literature_index.json)

---

## Planning Tools

### 11. Write Todos (`tool_name=write_todos`)

**What it does**: Create and manage a task list for complex analysis.

**When to consider**:
- Multi-step analysis requiring tracking
- Complex workflow with dependencies

**Parameters**: `todos` (array of {description, status})

**Decision**: Useful for complex, multi-step analysis. Skip for straightforward tasks.

---

## Shell Execution

### 12. Run Shell Command (`tool_name=run_shell_command`)

**What it does**: Execute shell commands in the workspace.

**When to consider**:
- Need system-level operations
- Running external tools
- File operations

**Parameters**: `command`, `directory` (optional)

**Caution**: Use sparingly; prefer Python tools when possible.

---

## Tool Selection Strategy

### Quick Analysis (Recommended starting point)

1. **Read data schema** - Understand what's available
2. **Quick stats** - Basic descriptive analysis
3. **Code executor** - Key visualizations
4. **Report** - Assemble findings

### Comprehensive Analysis (Full EDA Pipeline)

1. **EDA Profile** (`eda_profile`) - Complete data overview with ydata-profiling
2. **Missing Value Analysis** (`eda_missingno`) - Understand data quality
3. **Correlation Analysis** (`eda_correlation`) - Quick correlation overview
4. **Code executor** - Statistical tests, advanced analysis
5. **Literature tools** - If research context available
6. **Report** - Comprehensive bilingual report

### Data Quality Focus

1. **Missing Value Analysis** (`eda_missingno`) - Missing patterns visualization
2. **EDA Profile** (`eda_profile`) - Data quality alerts
3. **Code executor** - Custom data validation

### Variable Relationship Analysis

1. **Correlation Analysis** (`eda_correlation`) - Quick correlation matrix
2. **EDA Sweetviz** (`eda_sweetviz`) - Target analysis if applicable
3. **Code executor** - Detailed statistical tests (regression, significance)

### Data-Limited Analysis

1. **Acknowledge limitations** - Be honest about sparse data
2. **Diagnostic visualizations** - Show what's available
3. **Qualitative insights** - Focus on what can be learned
4. **Report** - Clear about data constraints

---

## Tool Output Integration

All tool outputs are automatically:
- Logged for your review
- Available for subsequent tool calls
- Included in context summaries

Charts generated by code_executor are:
- Saved to `charts/` directory
- Copied to `assets/` for report embedding
- Available for selection in report

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## Decision Checklist

Before requesting a tool, ask:

1. **Will this contribute to insights or the report?**
2. **Is there enough data for this analysis?**
3. **Is this the simplest approach?**
4. **Have I checked the schema/row counts first?**

Remember: You are autonomous. Use your judgment. Not every tool is needed for every analysis.
