M&A Data Room Query Pipeline Builder

Role: Principal Legal Technology Automation Engineer specializing in M&A due diligence pipelines and function-calling architectures.

Context

• Virtual data room provider: {{vdr_provider}}
• Due diligence focus domain: {{due_diligence_domain}}
• Target corporate structure: {{target_entity_type}}
• Max text chunks per query: {{max_document_chunks}}
• Semantic similarity cutoff: {{confidence_cutoff}}
• Search operational mode: {{retrieval_mode}}

Task

Develop an agentic tool definition specification and Python dispatch script that allows an LLM agent to execute semantic searches, pull raw agreement clauses from {{vdr_provider}}, filter by confidence scores, and summarize compliance risks for {{due_diligence_domain}} reviews.

Method

1. Define JSON schemas for two core tools: search_deal_room and extract_contract_clause.
2. Configure search parameter arguments enforcing {{retrieval_mode}} and pagination limits set to {{max_document_chunks}}.
3. Write Python client functions handling simulated API handshakes with {{vdr_provider}}.
4. Build a filter mechanism that discards extracted clauses falling below {{confidence_cutoff}}.
5. Structure output normalization logic to tag identified risks against {{target_entity_type}} standard warranties.
6. Implement defensive validation to prevent prompt injection or out-of-scope document retrieval.
7. Provide an executable demo showing the tool invocation cycle for an indemnification or termination clause search.

Constraints

• Code MUST be written in clean Python 3 with strict dictionary typing.
• MUST NOT process queries exceeding the specified chunk limit {{max_document_chunks}}.
• Tool schemas must adhere precisely to JSON Schema Draft-07 conventions.
• Returned payloads MUST include document metadata (document name, page number, confidence score).

Output format

1. Tool Declaration Specification

JSON schemas containing descriptive metadata and typed parameter definitions.

2. Orchestration Script

Executable Python script defining the tool handlers and dispatch registry (80-130 lines).

3. Sample Execution Log

A mock payload demonstrating an agent function call and the resulting normalized JSON response.

Self-review

• Confirm that {{confidence_cutoff}} is used inside the clause filtering loop.
• Verify metadata fields include page numbers and document identifiers.
• Ensure schema parameter descriptions are sufficiently clear for zero-shot LLM tool selection.