"""
Example demonstrating MCP Server usage with CRISP-T

This script shows how the MCP server tools can be used in sequence
to perform a complete analysis workflow.

Note: This is a conceptual example showing the tool call sequence.
In practice, these tools would be called through an MCP client.
"""

# Example workflow using MCP tools:

# Step 1: Load corpus
# Tool: load_corpus
# Arguments: {"inp": "/path/to/corpus_folder"}
# Returns: "Corpus loaded successfully with N document(s)"

# Step 2: List documents to see what we have
# Tool: list_documents
# Arguments: {}
# Returns: ["doc1", "doc2", "doc3", ...]

# Step 3: Get DataFrame columns if numeric data is present
# Tool: get_df_columns
# Arguments: {}
# Returns: ["age", "gender", "outcome", ...]

# Step 4: Generate coding dictionary for qualitative analysis
# Tool: generate_coding_dictionary
# Arguments: {"num": 10, "top_n": 5}

# Step 5: Perform topic modeling
# Tool: topic_modeling
# Arguments: {"num_topics": 5, "num_words": 10}

# Step 6: Assign documents to topics
# Tool: assign_topics
# Arguments: {"num_topics": 5}

# Step 7: Perform sentiment analysis
# Tool: sentiment_analysis
# Arguments: {"documents": true, "verbose": true}

# Step 8: Run regression analysis on numeric data
# Tool: regression_analysis
# Arguments: {"outcome": "satisfaction_score"}

# Step 9: Decision tree classification for feature importance
# Tool: decision_tree_classification
# Arguments: {"outcome": "readmission", "top_n": 10}

# Step 10: Link textual findings with numeric data
# Tool: add_relationship
# Arguments: {
#   "first": "text:healthcare_access",
#   "second": "num:insurance_status",
#   "relation": "correlates"
# }

# Step 11: Save the corpus with all metadata
# Tool: save_corpus
# Arguments: {"out": "/path/to/output_folder"}


print("This is a conceptual example. See comments for tool usage.")
