Tutorial 07: MCP Integrations for Legal Workflows (OpenAI)

{
  "mcpServers": {
    "midpage": {
      "command": "npx",
      "args": ["-y", "@midpage/mcp-server"],
      "env": {
        "MIDPAGE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Can you search Midpage for recent California Supreme Court
cases on non-compete agreements?

I'm drafting a motion to compel arbitration in Texas.
Search Midpage for the leading Texas cases on
arbitration enforceability and summarize the current standards.

I want to cite Johnson v. Smith Corp., 234 F.3d 567 (5th Cir. 2020)
in my brief. Use Midpage to verify this citation is still good law.

My client was terminated after reporting safety violations.
Search Midpage for Texas whistleblower retaliation cases where
the plaintiff prevailed. Focus on cases from the last 5 years.

npm install -g <courtlistener-mcp-package>

{
  "mcpServers": {
    "courtlistener": {
      "command": "<mcp-command>",
      "args": ["serve"],
      "env": {
        "COURTLISTENER_API_KEY": "your-api-key"
      }
    }
  }
}

Search CourtListener for Ninth Circuit cases discussing
software license enforceability.

npm install @modelcontextprotocol/sdk zod

{
  "mcpServers": {
    "legalcontext": {
      "command": "npx",
      "args": ["-y", "legalcontext-mcp"],
      "env": {
        "CLIO_API_KEY": "your-clio-api-key"
      }
    }
  }
}

Retrieve the Smith v. Jones complaint from our Clio matter files
and summarize the key allegations.

Search our SharePoint legal templates folder for NDAs
and list all templates by date modified.

// imanage-mcp.js
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
import iManageClient from "imanage-api-client";
 
const server = new McpServer({ name: "imanage", version: "1.0.0" });
 
server.registerTool(
  "search_documents",
  {
    description: "Search iManage for documents",
    inputSchema: z.object({
      query: z.string().describe("Search query"),
      workspace: z.string().describe("Workspace ID"),
    }),
  },
  async ({ query, workspace }) => {
    const client = new iManageClient(process.env.IMANAGE_CREDENTIALS);
    const results = await client.search({ query, workspace });
    return { content: [{ type: "text", text: JSON.stringify(results) }] };
  },
);
 
server.registerTool(
  "get_document",
  {
    description: "Retrieve document content",
    inputSchema: z.object({
      documentId: z.string().describe("Document ID"),
    }),
  },
  async ({ documentId }) => {
    const client = new iManageClient(process.env.IMANAGE_CREDENTIALS);
    const document = await client.getDocument(documentId);
    return { content: [{ type: "text", text: JSON.stringify(document) }] };
  },
);
 
const transport = new StdioServerTransport();
await server.connect(transport);

{
  "mcpServers": {
    "imanage": {
      "command": "node",
      "args": ["path/to/imanage-mcp.js"],
      "env": {
        "IMANAGE_CREDENTIALS": "path/to/credentials.json"
      }
    }
  }
}

I need to research the enforceability of liquidated damages
clauses in software agreements under Delaware law.

Workflow:
1. Search Midpage for Delaware cases on liquidated damages
2. Search CourtListener for federal cases applying Delaware law
3. Pull our firm's memo on liquidated damages from SharePoint
4. Synthesize into a research summary

Let me research this across multiple sources...

## Midpage Results (Delaware State Cases)
[ChatGPT queries Midpage, returns cases]

## CourtListener Results (Federal/Delaware Law)
[ChatGPT queries CourtListener, returns additional cases]

## Firm Precedent (SharePoint)
[ChatGPT retrieves firm memo]

## Synthesis
Based on my research across these sources:

The leading Delaware case is [X], which established...
Federal courts applying Delaware law have held...
Our firm's prior analysis in the [Client] matter noted...

**Key Takeaways**:
1. Delaware generally enforces liquidated damages if...
2. The "reasonableness" test requires...
3. Our recommended approach is...

**Citations** (verified via Midpage citator):
- [Citation 1] - Still good law
- [Citation 2] - Still good law

[Upload software agreement]

[ChatGPT identifies concerning liability provision under CA law]

"I've flagged the liability limitation as potentially problematic.
Let me research California's position on liability caps in
software agreements..."

[ChatGPT queries Midpage for CA cases]

"Based on my research, California courts have held that [X].
The clause as drafted may be enforceable because [Y], but
you should consider [Z] modification."

We're acquiring a company in the healthcare space.
Research the regulatory landscape:

1. Search our SharePoint for prior healthcare M&A memos
2. Search Midpage for recent HIPAA enforcement actions
3. Search for FTC healthcare antitrust cases
4. Generate a due diligence issue checklist

{
  "mcpServers": {
    "midpage": {
      "command": "npx",
      "args": ["-y", "@midpage/mcp-server"],
      "env": { "MIDPAGE_API_KEY": "key1" }
    },
    "courtlistener": {
      "command": "courtlistener-mcp",
      "args": ["serve"]
    },
    "legalcontext": {
      "command": "npx",
      "args": ["-y", "legalcontext-mcp"],
      "env": { "CLIO_API_KEY": "key2" }
    },
    "sharepoint": {
      "command": "npx",
      "args": ["-y", "@microsoft/sharepoint-mcp"]
    }
  }
}

# Legal research config
export OPENAI_MCP_CONFIG=~/.openai/mcp-research.json
 
# Contract review config
export OPENAI_MCP_CONFIG=~/.openai/mcp-contracts.json
 
# Full access config
export OPENAI_MCP_CONFIG=~/.openai/mcp-full.json

What MCP servers do you have access to right now?
List each server and its available tools.

Test the Midpage search tool by searching for "contract breach"
and report any errors.

Your Query
    |
ChatGPT (OpenAI Servers)
    |
MCP Server (Your Control)
    |
External Service (Midpage/iManage/etc.)

{
  "mcpServers": {
    "congress": {
      "command": "npx",
      "args": ["-y", "@open-legal-tools/congress-mcp"],
      "env": {
        "CONGRESS_API_KEY": "optional-api-key"
      }
    }
  }
}

Research task:
Track amendments to the California CCPA across its
legislative history and identify key changes by session.

ChatGPT multi-step workflow:
1. Query Congress API for CCPA bill history
2. Retrieve full text for versions from 2018, 2020, 2023
3. Compare key sections (scope, penalties, exemptions)
4. Generate side-by-side amendment analysis
5. Flag enforcement implications of changes

Query: "Search all HIPAA regulations (45 CFR 160-164)
related to breach notification requirements and identify
which sections were modified in the last 3 years."

ChatGPT workflow:
1. Query regulatory database for HIPAA sections
2. Cross-reference Federal Register for amendments
3. Pull relevant case law on HIPAA enforcement (via Midpage)
4. Compile regulatory timeline
5. Provide implementation checklist for compliance

Monitor bills matching: [keywords], jurisdictions: [states/federal],
stages: [introduced, hearing, floor, etc.]

ChatGPT can use Congress API MCP to:
- Search for bills by topic
- Compare bill versions across sessions
- Track amendments
- Retrieve sponsor info

Upload legal brief to ChatGPT with instruction:
"Verify every case citation in this brief is still good law
and identify any corrections needed."

ChatGPT workflow:
1. Extract all citations (Midpage tool)
2. Check each citation status (Midpage citator)
3. Identify overruled/reversed cases
4. Generate citation correction report
5. Flag negative law that contradicts arguments

Citation verification prompt:
"For the following cases, provide Shepard's analysis:
- Smith v. Jones, 234 F.3d 567 (5th Cir. 2020)
- Brown v. Green Corp., 890 P.2d 123 (Cal. 2019)

For each case:
1. Current status (good law/overruled/limited)
2. Citing cases (positive/negative treatment)
3. Strength of authority for this proposition
4. Alternative authorities if weakened"

{
  "mcpServers": {
    "midpage": {
      "command": "npx",
      "args": ["-y", "@midpage/mcp-server"],
      "tools": [
        "search_cases",
        "verify_citation_status",
        "find_citing_cases",
        "check_negative_law"
      ],
      "env": {
        "MIDPAGE_API_KEY": "your-api-key"
      }
    },
    "google-scholar": {
      "command": "npx",
      "args": ["-y", "google-scholar-mcp"],
      "tools": ["search_scholar", "verify_citations"]
    }
  }
}

Upload brief with instruction:
"Validate every citation in this document for Bluebook/ALWD format.
For each citation: provide original, normalized form, formatted output,
and any corrections needed. Generate a citation correction report."

{
  "mcpServers": {
    "pacer": {
      "command": "npx",
      "args": ["-y", "@open-legal-tools/pacer-mcp"],
      "env": {
        "PACER_USERNAME": "your-pacer-login",
        "PACER_PASSWORD": "your-pacer-password",
        "PACER_CLIENT_CODE": "your-client-code"
      }
    },
    "recap": {
      "command": "npx",
      "args": ["-y", "@free-law-project/recap-mcp"]
    }
  }
}

Monitor docket: Smith v. Jones, Case No. 2023-CV-12345

Daily workflow:
1. Check for new filings (PACER alert)
2. Extract filing metadata (date, filer, document type)
3. Download documents if Rule 34 request received
4. Flag critical events (depositions, trial dates)
5. Summarize for team

Workflow: Competitive intelligence monitoring

Setup alert for:
- All cases involving Company X in federal courts
- Parties: Company X (plaintiff or defendant)
- Date range: Last 2 years

ChatGPT multi-source search:
1. Query PACER for Company X docket entries
2. Search RECAP for public documents
3. Cross-reference state court records (where available)
4. Extract defendants/plaintiffs for analysis
5. Generate competitive threat assessment

Query: "Track all cases involving [party name] as [plaintiff/defendant]
in [courts]. Provide: total cases, active cases, docket summaries,
and competitive analysis of party litigation history."

Research request:
"My client is negotiating a software licensing agreement
with a major tech company. Research the landscape of:
1. Recent software licensing disputes
2. Key contract provisions courts scrutinize
3. Best practices from firm precedents
4. Relevant patent litigation affecting licensing
5. Synthesize into deal negotiation memo"

ChatGPT agent workflow:
Step 1: Research Phase
  - Query Midpage for software licensing cases (last 5 years)
  - Extract critical issues from leading cases

Step 2: Precedent Phase
  - Search SharePoint for firm's prior licensing agreements
  - Extract successful clause language
  - Identify deal-breaker provisions by client type

Step 3: Patent Phase
  - Search Google Patents for relevant technology patents
  - Identify software licensing implications
  - Flag cross-license requirements

Step 4: Synthesis Phase
  - Organize findings by provision (IP ownership, liability, etc.)
  - Cross-reference case law to deal terms
  - Generate risk assessment matrix

Step 5: Deliverable Phase
  - Create negotiation memo with:
    * Market landscape summary
    * Key case takeaways
    * Recommended provisions
    * Risk mitigation strategies

User: "I need to understand California's position on
non-compete agreements in employment contracts.
What should I research?"

ChatGPT research planner:
"I'll develop a research plan covering:

Phase 1: Controlling Authority
  - California Supreme Court cases on non-competes
  - Test: Is non-compete reasonable? (Blue Pencil doctrine)
  - Key cases: Silguero, Edwards, Morbi

Phase 2: Legislature Response
  - California Business & Professions Code 16600
  - Legislative amendments (2024 updates)
  - Statutory interpretation

Phase 3: Modern Applications
  - Recent Court of Appeal decisions (2023-2025)
  - Non-compete vs. non-solicitation distinction
  - Remote work implications

Phase 4: Practical Synthesis
  - Enforceable provisions despite 16600
  - Trade secret protection alternatives
  - Employment agreement drafting best practices

Estimated research time: 2-3 hours
Key sources: Midpage, firm memos, Bar Association guidance"

Initial query: "California non-compete enforceability"

Iteration 1: Initial results show non-competes generally unenforceable
  -> Questions: Legitimate business interests exception? C-suite application?

Iteration 2: Refined searches for exceptions, executive contracts, remote work
  -> New findings: Narrow exceptions for limited legitimate interests

Iteration 3: Targeted research on 16600 interpretation, blue pencil doctrine
  -> Synthesis: Actionable guidance for client

Query: "Judge Smith handles patent licensing disputes
in the Northern District of California. What patterns
can I identify in her decisions?"

Analytics workflow:
1. Search RECAP for all Judge Smith cases
2. Extract relevant patent licensing decisions
3. Analyze patterns:
   - Grant rate on summary judgment motions
   - Treatment of injunction requests
   - Damages awards (high/low/reasonable)
   - Attitude toward expert witnesses
4. Compare to district averages
5. Generate litigation strategy recommendations

Workflow: Opposing Counsel Analysis

For: Attorney Jane Doe (plaintiff's counsel)

Metrics extracted (illustrative example):
- Settlement rate vs. district average
- Trial win rate
- Typical verdict range for case type
- Expert witness preferences
- Motion practice: Aggressive discovery requests
- Deposition style: Confrontational

Strategic implications:
- Prepare for aggressive discovery
- Expect push to trial
- May be open to structured settlements
- Anticipated expert witnesses based on historical patterns

Query: "Predict the likely outcome of our employment
discrimination case with Judge Smith in NDCA."

Analysis:
1. Historical data: Judge Smith's discrimination cases
2. Factors: Strength of evidence, damages, settlement history
3. Prediction: Probability of plaintiff success in trial
4. Outcome distribution: Summary judgment, trial, settlement
5. Median award range if plaintiff prevails
6. Confidence interval

Query: "Analyze historical settlements for [case type] in [jurisdiction]
before Judge [name]. Claimed damages: $X. Provide:
- Predicted low/mid/high settlement range
- Confidence level
- Comparable cases
- Key factors affecting range

Search for [topic] cases in [jurisdiction]
Verify citation [citation] is still good law
Find cases that cite [case name]
Check if [case] has been overruled or limited

Search CourtListener for [query]
Get full text of [case citation]
Find oral arguments in [case]
Search by judge: [judge name] in [court]

Search Congress for bills matching [criteria]
Track amendments to [statute] by year
Compare versions of [bill] across sessions
Search Federal Register for [regulation changes]
Monitor bills in [state legislature] related to [topic]

Search PACER for cases involving [party name]
Get docket for [case number] in [court]
Set alert for filings in [case number]
Search RECAP for [party] across all federal courts
Find all cases by Judge [judge name]

Verify citation [citation] is still good law
Check Shepard's for [case]: positive/negative treatment
Find alternative authorities for [proposition]
Validate citation format for [citation] (Bluebook/ALWD)

Search [system] for documents matching [criteria]
Retrieve document [name/ID] from [location]
List recent documents in [workspace/folder]

Analyze Judge [judge name] decision patterns for [case type]
Predict settlement range for [case type] before Judge [judge]
Compare outcome patterns for opposing counsel [attorney name]
Calculate motion grant rates for [judge] on [motion type]