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Tool Calling

A Tool Calling is a function that the LLM can invoke autonomously during a conversation to retrieve information or perform an action. Instead of relying purely on training data, the LLM calls tools to fetch live data, search indexed content, execute code, and more — then incorporates the results into its reasoning.

Turing ES includes native tools organized into categories, plus support for external tools via MCP Servers. Tools are enabled per AI Agent — each agent selects only the tools it needs.

DSL Search — 6 tools​

These tools provide Elasticsearch-compatible Query DSL access to any Semantic Navigation Site. Inspired by the Elasticsearch MCP Server, they allow the LLM to build and execute full DSL queries, aggregations, and analytics — automatically translated to the site's configured search engine (Elasticsearch, Solr, or Lucene).

ToolDescription
dsl_list_indicesLists all available SN Sites with engine type, locales, and status
dsl_get_mappingsReturns field mappings (types, facets, multi-valued) in Elasticsearch format
dsl_searchExecutes a full Elasticsearch Query DSL search (40 query types, 35 aggregations)
dsl_get_documentRetrieves a document by ID with all stored fields
dsl_suggestAutocomplete suggestions and spell-check corrections
dsl_get_shardsShard/core information: document counts, engine type, endpoints, locales

How the LLM uses DSL tools​

The LLM follows a pattern similar to how a developer uses the Elasticsearch API:

  1. Discover → dsl_list_indices("*") to find available sites
  2. Understand schema → dsl_get_mappings("mySite") to see fields and types
  3. Search → dsl_search("mySite", "en", queryBody) with full DSL
  4. Deep dive → dsl_get_document("mySite", "en", "doc-123") for full content
  5. Correct typos → dsl_suggest("mySite", "en", "enterprse") for corrections

The dsl_search tool​

This is the most powerful tool. The queryBody parameter accepts the complete Elasticsearch _search request body as a JSON string. The LLM constructs the query based on the user's intent:

Simple search:

{"query":{"match":{"title":"machine learning"}},"size":5}

Filtered search with aggregations:

{
  "query":{"bool":{"must":[{"match":{"title":"security"}}],"filter":[{"term":{"type":"article"}}]}},
  "size":10,
  "aggs":{"by_author":{"terms":{"field":"author","size":5}}},
  "highlight":{"fields":{"title":{},"body":{}}}
}

Date range with sorting:

{
  "query":{"range":{"date":{"gte":"2025-01-01"}}},
  "sort":[{"date":"desc"}],
  "size":20
}

Similar documents (More Like This):

{"query":{"more_like_this":{"fields":["title","body"],"like":"enterprise search platform","min_term_freq":1}}}

Facet value discovery (terms aggregation):

{"query":{"match_all":{}},"size":0,"aggs":{"categories":{"terms":{"field":"category","size":50}}}}

For the complete DSL reference, see DSL Query API and DSL Compatibility Matrix.

Prompt examples​

  • "Search for documents about authentication in the Sample site" → dsl_search with match query
  • "What fields can I filter by on the Sample site?" → dsl_get_mappings
  • "Show me the full content of document ID abc-123" → dsl_get_document
  • "How many documents do we have per content type?" → dsl_search with terms aggregation
  • "Find documents similar to the article about Solr configuration" → dsl_search with more_like_this
  • "What articles were updated in the last 7 days about security?" → dsl_search with range + sort
  • "Compare the features of Product A and Product B" → dsl_get_document called for each product
  • "What categories are available?" → dsl_search with size:0 and terms aggregation
  • "How many documents per locale does the site have?" → dsl_get_shards

RAG / Knowledge Base — 4 tools​

These tools enable the LLM to query the Knowledge Base — files indexed from MinIO via the Assets section — using semantic similarity search.

ToolDescription
search_knowledge_baseSearches for relevant documents by semantic similarity (top 10, threshold 0.7)
knowledge_base_statsReturns statistics: total files, chunks, and storage size
list_knowledge_base_filesLists all indexed files, with optional keyword filter
get_file_from_knowledge_baseRetrieves the full indexed content of a specific file

Prompt examples:

  • "What does our internal documentation say about deployment procedures?" → triggers search_knowledge_base
  • "How many files are indexed in the knowledge base?" → triggers knowledge_base_stats
  • "Show me the full content of the file onboarding-guide.pdf" → triggers list_knowledge_base_files then get_file_from_knowledge_base

Web Crawler — 2 tools​

ToolDescription
fetch_webpageFetches a web page by URL and returns its content as plain text
extract_linksExtracts all links from a web page, with optional keyword filter

Prompt examples:

  • "What does the Apache Solr documentation say about faceted search?" → triggers fetch_webpage
  • "List all links on our company blog homepage" → triggers extract_links

Finance — 2 tools​

ToolDescription
get_stock_quoteCurrent price and market data for a stock ticker symbol
search_tickerLooks up a ticker symbol by company name or keyword

Prompt examples:

  • "What is the current stock price of Apple?" → triggers search_ticker then get_stock_quote
  • "Show me the market data for MSFT" → triggers get_stock_quote

Weather — 1 tool​

ToolDescription
get_weatherCurrent weather and forecast for a city (1–7 day range)

Prompt examples:

  • "What's the weather forecast for São Paulo this week?" → triggers get_weather

Image Search — 1 tool​

ToolDescription
search_imagesSearches the web for images and returns URLs and descriptions

Prompt examples:

  • "Find images of enterprise search architecture diagrams" → triggers search_images

DateTime — 1 tool​

ToolDescription
get_current_timeReturns the current date and time for a given IANA timezone

Prompt examples:

  • "What time is it right now in Tokyo?" → triggers get_current_time

Code Interpreter — 1 tool​

ToolDescription
execute_pythonExecutes Python code in a sandboxed environment and returns stdout/stderr

The Code Interpreter runs Python in an isolated sandbox directory with a 30-second execution timeout. It supports:

  • Standard output capture
  • Matplotlib chart generation (with Agg backend — no display required)
  • Automatic print() wrapping for bare expressions

The Python executable path is configured in Administration → Settings → Python Path.

Prompt examples:

  • "Calculate the compound interest on $10,000 at 5% for 10 years" → triggers execute_python
  • "Generate a bar chart showing monthly sales: Jan=100, Feb=150, Mar=120" → triggers execute_python with matplotlib

External Tools via MCP Servers​

Beyond the native tools, AI Agents can access tools from any external server implementing the Model Context Protocol (MCP). This covers company-internal systems, proprietary data APIs, and the growing ecosystem of public MCP servers.

See MCP Servers for configuration details.

PageDescription
AI AgentsHow to compose agents with the tools they need
MCP ServersExtend agents with external tools via MCP
DSL Query APIFull reference for the Elasticsearch-compatible DSL
DSL Compatibility MatrixEngine compatibility for all DSL features
AssetsKnowledge Base files queried by RAG tools
Semantic NavigationThe search experience powering DSL tools
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