Legal Reasoning Prover MCP Connector for Claude
A+A legal memo cited cases that do not exist and confused jurisdiction. Legal Reasoning Prover forces IRAC-based arguments grounded in US law — real Bluebook citations, jurisdiction analysis, ABA-compliant counter-arguments, and specific remedies.
AI agents hallucinate legal citations at an alarming rate. Stanford HAI (2024) documented that LLMs fabricate case names in 30-40% of legal analysis outputs. The citations look right — proper party names, plausible reporter volumes, realistic court designations. But the cases don't exist. The holdings are invented. And the conclusions don't follow from the rules stated.
The Problem It Solves
AI-generated legal reasoning fails for five specific reasons:
- Broken syllogism — The conclusion doesn't follow from the rule applied to the facts. The agent states a legal standard, then leaps to a conclusion without showing how the facts satisfy each element.
- Phantom authority — Fabricated case citations. "Smith v. Jones, 542 F.3d 891 (9th Cir. 2019)" — sounds real, doesn't exist. LLMs generate these with complete confidence.
- Jurisdiction blindness — Analysis that says "the law provides" without specifying which court, which state, which circuit, whether Erie applies, or what standard of review governs.
- Missing counter-argument — One-sided analysis that ignores adverse authority. Under ABA Model Rule 3.3(a)(2), counsel must disclose directly adverse controlling authority.
- Detached remedy — Legal analysis disconnected from actionable relief. "Consult an attorney" is not a remedy. "File a 12(b)(6) motion under Twombly/Iqbal" is.
Key Benefits
- Eliminates phantom citations — Catches fabricated case names, placeholder parties, and weasel authority language before they reach your memo.
- Enforces IRAC discipline — Every legal conclusion must trace back through Issue, Rule, Application, and Conclusion with verifiable logical steps.
- Requires jurisdiction specificity — No more "under applicable law." The agent must name the court, the governing law, and the choice-of-law basis.
- Guarantees adversarial rigor — Forces the agent to confront the strongest counter-argument, not just present a one-sided analysis.
- Connects analysis to action — Every legal analysis must end with a specific procedural remedy: motion type, statutory basis, and relief sought.
US Legal Framework Coverage
- IRAC Method — Issue, Rule, Application, Conclusion
- Bluebook Citation — 21st Edition standard
- ABA Model Rules — Professional conduct requirements
- Erie Doctrine — Federal/state choice of law
- FRCP/FRE — Federal Rules of Civil Procedure and Evidence
- Stare Decisis — SCOTUS > Circuit > District hierarchy
- Standards of Review — De novo, abuse of discretion, clearly erroneous, substantial evidence
Related Connectors
Data Analysis Prover MCP
A marketing team asked an AI to analyze campaign data. The AI reported 'significant correlation between email frequency and purchase rate (p<0.05).' The team tripled emails. Unsubscribes spiked 340%. Sample: N=47 self-selected respondents, no power analysis. Correlation: observational, no confounders. Distribution: right-skewed but mean used. p=0.043 but Cohen's d=0.12 — trivial. Chart: truncated Y-axis making a 2% difference look enormous. This tool forces five axes: sample validity, causal inference, distribution awareness, significance with effect size, and visualization integrity.
Causal-Graph Navigator MCP
LLMs reason by statistical proximity, confusing word co-occurrence with direct causal relationships. This tool forces causal graph isolation: identify entities as nodes, map directed influence edges, isolate statistical associations, validate graph coherence, and derive paths strictly from the DAG.
Elon Musk Physics Prover MCP
An AI accepted every constraint as given, added layers of complexity, and automated bloated processes. That is consulting, not engineering. This tool forces the 5-Step Starbase Algorithm: question requirements, delete parts, simplify survivors, accelerate cycle time, automate last.
Article Architect MCP
Technical blog posts written by AI read like documentation — step 1, step 2, step 3, no argument, no tradeoffs, no opinion. Article Architect forces the agent to take a position, expose limitations, plan code as evidence, cite production data, and define a reader transformation.