from typing import Dict, List, Optional
from fastmcp import FastMCP
class AnalyzerTools:
def __init__(self, search_client):
self.search_client = search_client
def register_tools(self, mcp: FastMCP):
@mcp.tool()
def analyze_text(
text: str,
index: Optional[str] = None,
analyzer: Optional[str] = None,
tokenizer: Optional[str] = None,
filter: Optional[List[str]] = None,
char_filter: Optional[List[str]] = None,
explain: bool = False,
attributes: Optional[List[str]] = None,
) -> Dict:
"""
Analyze text to see how it would be tokenized.
Use this tool to understand how Elasticsearch/OpenSearch tokenizes and
transforms text using analyzers. This is essential for debugging search
queries and understanding why certain documents match or don't match.
Args:
text: The text to analyze
index: Index name to use its configured analyzer. If not specified,
uses cluster-level analysis with built-in analyzers only.
analyzer: Name of the analyzer to use (e.g., 'standard', 'korean',
'korean_search'). If index is specified, you can use
custom analyzers defined in that index.
tokenizer: Tokenizer to use for custom analysis chain. Cannot be
used together with 'analyzer'.
filter: List of token filters to apply (e.g., ['lowercase', 'stop']).
Used with 'tokenizer' for custom analysis chain.
char_filter: List of character filters to apply before tokenization.
Used with 'tokenizer' for custom analysis chain.
explain: If True, returns detailed information about each token
including all token attributes and filter transformations.
Useful for debugging complex analyzer chains.
attributes: List of token attributes to return when explain=True
(e.g., ['keyword', 'type']). If not specified, all
attributes are returned.
Returns:
Dict containing 'tokens' array. Each token has 'token', 'start_offset',
'end_offset', 'type', and 'position' fields. With explain=True,
returns detailed 'detail' object showing each filter's effect.
"""
return self.search_client.analyze_text(
text=text,
index=index,
analyzer=analyzer,
tokenizer=tokenizer,
filter=filter,
char_filter=char_filter,
explain=explain,
attributes=attributes,
)
