Edwin Genego

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Legal intelligence background

Case Study

Legal Research & Paralegal MCP

Experimental protocol surface for structured legal workflows: precedent retrieval, citation validation, contract comparison & brief scaffolding - delivered as inspectable tools, resources and prompt templates.

  • Multi-source research adapters (planned)
  • Citation normalization & staging
  • Clause differ + risk hints
  • Brief & argument outline templates
Experimental Case Study

This Legal MCP is an experimental / exploratory implementation – a working prototype validating protocol-driven legal workflow capabilities (precedent retrieval, citation validation, contract comparison, brief scaffolding). It is not a commercial product; the focus is on schema-first architecture, audit-friendly reasoning primitives and demonstrating MCP portability across professional domains.

Iterating with a small partner cohort to refine tool contracts, resource structures and prompt templates. The capability surface is intentionally narrow to maintain inspectability and contract stability during validation.

Edwin

Note from the Author

Hey, I'm Edwin. I like working on interesting stuff with AI, LLMs and web development - this idea made the cut, which is why you're seeing this page. The intersection of structured legal reasoning and protocol-driven AI workflows felt compelling enough to explore in depth. If you're thinking about similar patterns in other domains (finance, compliance, logistics) or want to discuss the architecture, feel free to reach out.

Workflow Examples

Representative end-to-end flows demonstrating how legal teams interact with the MCP surface through AI assistants.

Use Case

A paralegal at a mid-size law firm receives a proposed Non-Disclosure Agreement from a prospective client's vendor. The client wants to understand how the vendor's version differs from the firm's standard NDA template and whether any modifications present legal risks. The paralegal needs to quickly identify substantive changes, assess their potential impact, and provide actionable recommendations for negotiation - all while maintaining client confidentiality and ensuring no sensitive case details are exposed during the analysis.

Use Case

A junior associate is handling a commercial litigation case where the defendant, a manufacturing supplier, failed to deliver critical components by the contractually specified deadline, causing significant business disruption for the plaintiff. The contract includes language about delivery timelines but doesn't explicitly state 'time is of the essence.' The associate needs to find relevant precedents from California courts that address breach of contract claims involving delivery delays, understand when such delays constitute material breach, and determine what legal standards apply when time-of-essence clauses are absent or ambiguous. This research will form the foundation of the plaintiff's motion for summary judgment.

Use Case

A senior partner is preparing to file a motion for summary judgment in the breach of contract case. After reviewing the completed legal research on time-of-essence provisions and material breach standards, the partner now needs to structure a comprehensive motion that presents the strongest possible argument for summary judgment. The brief must follow proper procedural format, present undisputed material facts persuasively, apply the correct legal standards, and organize supporting authorities in a logical hierarchy that demonstrates why no triable issues of fact exist. The partner needs an intelligent scaffolding system that can generate a well-structured outline with proper section headings, allocate the strongest case authorities to each argument element, and identify any gaps in the legal reasoning that need to be addressed before filing.

Architectural Vision: The following describes the ideal approach for a production legal MCP implementation. This case study explores how such a system should be architected to prevent hallucination and ensure citation integrity - validating the pattern before scaling deployment.

Source-Backed Intelligence

Data Verifiability & Hallucination Prevention

Legal workflows demand absolute citation integrity. In an ideal implementation, this MCP would connect to authoritative legal databases and research repositories - ensuring every precedent, statute excerpt, and case reference can be traced to a verified source.

Potential Data Sources

A production implementation would integrate with a combination of premium and open-access legal databases:

Premium Legal Databases

  • PACER – U.S. federal court documents
  • Westlaw – Comprehensive case law & statutes
  • LexisNexis – Legal research platform

Open-Access Repositories

  • CourtListener – Free legal opinions database
  • Google Scholar – Searchable case law archive
  • Casetext – AI-enhanced legal research

Proprietary & Internal

  • Firm knowledge bases & vetted case notes
  • Expert-curated reference materials

Pre-Surface Citation Validation

Citations and legal clauses are validated before being shown to the user - ensuring every reference is verified or clearly flagged:

  1. 1.
    Clause/Citation Generated

    LLM proposes a legal reference, precedent, or statutory citation

  2. 2.
    Pre-Surface Fact Check

    Before displaying to user, citation is cross-referenced against source databases

  3. 3.
    Doubt Triggers Deep Investigation

    Any uncertainty prompts additional verification layers and alternative source checking

  4. 4.
    Fail-Safe Response

    If validation fails, either continue investigating alternate sources or return nothing rather than risk hallucination

  5. 5.
    Verified Citation Only

    Only citations passing full validation are surfaced to the user - with source attribution

Architectural Goal

Never surface unverified legal information - validation happens invisibly in the background, before user sees any output

Human-in-the-Loop Design

Professional Tool, Not Autonomous Agent

This MCP is not designed for autonomous legal work. Instead, it's architected as an intelligent assistant for legal professionals - augmenting, not replacing, expert judgment.

What This Enables

  • Rapid precedent discovery across databases
  • Structured contract comparison workflows
  • Citation normalization & validation support
  • Brief scaffolding with source-backed arguments

Expert Oversight Required

  • Legal professional reviews all outputs
  • Final judgment calls remain human-driven
  • Context-aware interpretation still essential
  • Attorney responsibility & ethics maintained

Advantage over generic AI interfaces: Unlike ChatGPT or Claude web interfaces (which lack domain-specific verification, structured workflows, or proprietary database access), this in-house MCP integrates directly with your firm's knowledge base, enforces citation validation, and provides reproducible research patterns - making legal professionals significantly more efficient while maintaining full control and oversight.

Dataset Versioning & Change Tracking

Legal precedents evolve. Statutes are amended. Regulations shift. The proposed data methodology (inspired by software version control) would track every dataset mutation with semantic versioning and explicit changelogs.

View Full Methodology
v

Semantic Versioning

Each dataset tagged with version numbers (e.g., v2.3.1). Major changes trigger breaking version bumps.

📝

Change Logs

Every update documented: what changed, why, and when. Enables audit trails and rollback if needed.

🔔

Freshness Probes

Automated signals detect when source data drifts, triggering manual review cycles for re-validation.

This approach mirrors the documented data methodology - designed to maintain factual integrity across MCP server implementations while enabling continuous dataset evolution without breaking client integrations.

Verifiable by Design (Architectural Goal)

Unlike generic LLM chat interfaces that risk hallucination, this proposed MCP architecture would enforce source-backed responses only. Every legal reference validated against authoritative databases before delivery. Every dataset evolution versioned and logged. The result: audit-friendly legal intelligence that can be relied upon - this case study validates the pattern before production deployment.

Architecture Snapshot

🧱Protocol Core

  • MCP transport
  • Tool registry
  • Template catalog
  • Resource resolver

📚Retrieval Layer

  • Source adapters
  • Ranking pipeline
  • Result normalization

🧪Analysis Layer

  • Clause differ
  • Brief outliner
  • Citation validator (planned)

🔐Governance

  • Redaction hooks
  • Access tiers
  • Audit logging

Planned Tool Catalog

Identifiers & intent only; schemas under active iteration.

🔎Research

  • search_cases
  • fetch_statute_excerpt
  • list_related_precedents

Multi-source retrieval & precedent surfacing.

📑Draft & Review

  • analyze_clause
  • diff_contract_sections
  • generate_brief_outline

Structured analysis & synthesis outputs.

Prompt Layer (Templates)

Deterministic scaffolds - no hidden network calls, pure reasoning frames.

🧪Brief Strategy

Outline core arguments + supporting precedent slots.

  • brief_strategy_frame
  • argument_issue_matrix

🧪Clause Risk

Assess clause deviations vs canonical pattern.

  • clause_risk_scan

Representative Flows

Illustrative chaining patterns (external contract only).

Precedent Scan

Query: Key negligence precedents (jurisdiction: CA)

  1. search_cases
  2. fetch_statute_excerpt
  3. argument_issue_matrix

Shape: Ranked precedent list + issue matrix

Contract Delta

Query: Compare NDA v1 to base template

  1. diff_contract_sections
  2. analyze_clause
  3. clause_risk_scan

Shape: Changed sections + risk flags

Structured Workflow Patterns

Experimental chains combining retrieval + analysis + prompt scaffolds. Deterministic sequencing; some utilities marked (planned).

Precedent Retrieval & Issue Matrix

Trigger: New matter intake or argument refinement

Inputs: issue_terms, jurisdiction, date_range?, limit?

  1. search_cases(issue_terms, jurisdiction)
  2. fetch_statute_excerpt(referenced_sections)
  3. list_related_precedents(top_case_ids)
  4. argument_issue_matrix(precedent_set)

Output: {'precedents': 'ranked[]', 'issues': 'matrix[issue -> supporting_cases]', 'citations': 'list'}

Build a structured map of issues to strongest supporting authorities.

Clause Deviation & Risk Scan

Trigger: Incoming contract draft vs canonical template

Inputs: base_template_id, draft_document_id

  1. diff_contract_sections(base_template_id, draft_document_id)
  2. analyze_clause(changed_sections)
  3. clause_risk_scan(changed_section_summaries)

Output: {'changes': 'list[section, delta]', 'risk_flags': 'list[level, rationale]', 'recommendations': 'list'}

Surface high-impact deviations early with transparent rationale.

Citation Integrity Preflight

Trigger: Before brief draft distribution

Inputs: draft_brief_id

  1. extract_citations(draft_brief_id) (planned external util)
  2. fetch_statute_excerpt(cited_sections)
  3. search_cases(citation_case_names subset)
  4. validate_citation_references(compiled_sources) (planned)

Output: {'citations': 'list[citation, status]', 'anomalies': 'list', 'coverage_ratio': 'number'}

Identify broken or ambiguous citations before partner review.

Case Timeline Synthesis

Trigger: Docket update ingestion

Inputs: docket_events

  1. normalize_docket_events(docket_events) (planned)
  2. derive_phases(event_stream)
  3. generate_brief_outline(phase_summaries)

Output: {'timeline': 'ordered[]', 'phase_summary': 'list', 'next_deadlines': 'list'}

Provide an at-a-glance procedural and strategic snapshot.

Multi-Jurisdiction Statute Comparison

Trigger: Cross-state compliance inquiry

Inputs: statute_identifier, jurisdictions[]

  1. fetch_statute_excerpt(jurisdiction A)
  2. fetch_statute_excerpt(jurisdiction B...)
  3. compare_statute_sections(excerpts) (planned)
  4. brief_strategy_frame(differences)

Output: {'differences': 'list[dimension, delta]', 'alignment_score': 'number', 'watch_points': 'list'}

Highlight material divergences impacting strategy or compliance.

Argument Brief Outline Generation

Trigger: Initial drafting after research pass

Inputs: selected_precedents[], issues[]

  1. group_precedents_by_issue(selected_precedents)
  2. argument_issue_matrix(grouped)
  3. brief_strategy_frame(issue_matrix)

Output: {'outline': 'sections[]', 'support_map': 'issue->cases', 'gaps': 'list'}

Produce a deterministic scaffold for drafting with traceable support.

Roadmap (Selective)

Focus on verifiable outputs before scale. Expansion gated by schema stability & audit tooling maturity.

🧮Citation Chain

R&D

Automated citation verification & Shepardizing hooks.

  • Parallel source fetch
  • Signal aggregation
  • Confidence scoring

🗂️Docket Normalizer

Planned

Standardize docket event taxonomy.

  • Event class mapping
  • Timeline synthesis
  • Hearing extraction

📊Argument Scoring

Exploring

Heuristic argument strength hints (transparent).

  • Factor tagging
  • Precedent density
  • Diversity weighting

Exploring structured AI augmentation for legal operations or research? Share current tooling surface & constraints - I'll outline a staged MCP integration path.

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