{% set tool_name = namespace.tools.get("tabular_analysis_tool_name") %}
{% set list_tool = namespace.tools.get("list_files_tool_name") %}
{% set search_tool = namespace.tools.get("semantic_search_tool_name", "knowledge_search") %}

<{{ tool_name }}>
Retrieve CSV tables from the knowledge base and execute Pandas operations on them.

**CSV files only.** Will fail silently or error on PDF, DOCX, XLSX, TXT, or images.{% if list_tool %} Confirm file format via `{{ list_tool }}` before calling.{% endif %}

**Use when:**
- Aggregations: sum, average, count grouped by a column
- Rankings: top/bottom N by a metric
- Statistics: correlation, distribution, variance
- Filtering: rows matching a condition

**Do not use when:**
- File is not CSV
- Only 1 data point exists (can't compute trends or comparisons)

**Query construction:**
- Name the columns, operation, and any grouping or filter conditions explicitly. Use natural language.
- If the user query is vague or ambiguous, do not ask for clarification — issue a broad exploratory query first to inspect the CSV structure and content, then follow up with a precise operation based on what you find

**Finding the right artifacts:**
Before calling this tool, determine whether the relevant data lives in one or multiple files:
{% if list_tool %}
- Use `{{ list_tool }}` to list available files and identify which CSVs are relevant
{% endif %}
{% if search_tool %}
- Use `{{ search_tool }}` to surface which artifacts contain the relevant content
{% endif %}
Once you know the scope, prefer calling this tool with a single artifact. If the data spans multiple files, identify all relevant artifact_ids first, then decide whether to call this tool once with all of them or make separate calls and combine results.

**Multi-artifact calls — use with care:**
- Always explicitly name the join key and downstream operation in the query when combining files

**Parameters:**
- `query` (required): Describe the operation in precise natural language, naming columns, operation, and grouping or filter conditions
- `artifacts` (optional): List of CSV artifact IDs. **Prefer a single artifact.**
{% if few_shots == "True" %}

**Examples:**
- Aggregation → `query="sum of revenue grouped by department for Q4 2024", artifacts=["sales_csv_id"]`
- Ranking → `query="top 5 products by total units sold in the west region", artifacts=["inventory_csv_id"]`
- Filter + stat → `query="average order value where customer_tier is 'premium'", artifacts=["orders_csv_id"]`
- Correlation → `query="correlation between marketing_spend and sales by region", artifacts=["marketing_csv_id"]`
- Join → `query="join orders and customers on customer_id, then sum order_total grouped by customer_region", artifacts=["orders_csv_id", "customers_csv_id"]`
{% endif %}
</{{ tool_name }}>