# Brazilian Judiciary Dictionary Engine MCP for AI Agents AI Agent Connect > Brazilian Judiciary Dictionary Engine detects every Brazilian court, tribunal, agency, and regulatory body mentioned in legal documents. It uses a pre-indexed dictionary of 100+ entities to perform strict regex matching, ensuring zero AI inference and high accuracy for legal research and data normalization. ## Overview - **Category:** knowledge-management - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_nVGrOtVSle3rd3ZJsdubKaXkNu6UPUyg7TJgcWW0/ai-agent-connect - **Tags:** entity-extraction, regex, judiciary-data, data-normalization, legal-research, text-parsing ## Description When you're digging through hundreds of pages of Brazilian litigation, finding every mention of a specific court or regulatory body is a nightmare. Language models often hallucinate or miss acronyms like STJ or TRF3 because they don't understand the specific legal landscape. This Connector changes that by providing a deterministic way to pull those entities out of your text. It doesn't guess; it matches against a hard-coded list of every major court, tribunal, and agency in Brazil, from the Superior Tribunals down to the state-level TJs. You'll get organized results grouped by category, like Superior or Regulador, which makes it easy to spot patterns in jurisdictional reach or regulatory oversight. It's a reliable way to handle data normalization without the risk of an AI making things up. You can even feed in your own custom dictionary if you're dealing with niche bodies outside the standard list. It's one of the many tools you can find in the Vinkius catalog to make your legal tech stack actually reliable. ## Tools ### search_legal_entities Searches your text for known Brazilian legal entities using a strict offline dictionary. This gives you a precise count of every court or agency mentioned. ## Prompt Examples **Prompt:** ``` Scan this 300-page litigation bundle and tell me exactly which Brazilian courts and agencies are referenced. ``` **Response:** ``` Scan complete. Found 9 entities: STJ (45 mentions), TJSP (23), TRF3 (18), CADE (12), MPF (8), CNJ (5), TRT2 (4), ANVISA (3), AGU (2). Category summary: {"Superior": 45, "TJ": 23, "TRF": 18, "Regulador": 15, "MP/Advocacia": 10, "Controle": 5, "TRT": 4}. ``` **Prompt:** ``` I need to know how many times each TRT appears in this labor law case file to determine jurisdictional concentration. ``` **Response:** ``` Analysis complete. TRT2 (São Paulo Capital) leads with 34 mentions, TRT15 (Campinas) has 12, TRT1 (Rio de Janeiro) has 8, and TST has 19. Category breakdown: {"TRT": 54, "Superior": 19}. ``` **Prompt:** ``` Check if any regulatory agency (CADE, CVM, BACEN, ANVISA) is mentioned in this corporate compliance report. ``` **Response:** ``` Found 4 regulatory entities: BACEN (22 mentions), CVM (15), CADE (9), SUSEP (3). No mention of ANVISA. Category summary: {"Regulador": 49}. ``` ## Capabilities ### Extract court names from text The Connector identifies every mention of a Brazilian court or tribunal in your document. ### Group entities by legal category Results are automatically sorted into groups like Superior, TRF, TRT, and TJ. ### Identify regulatory agencies It catches mentions of major bodies like ANVISA, CVM, and BACEN with high precision. ### Use a custom dictionary You can add your own entities to the list to cover non-Brazilian jurisdictions or niche bodies. ### Count jurisdictional mentions The tool provides a tally of how many times each specific entity appears in your text. ## Use Cases ### Litigation Bundle Scan A lawyer needs to know which courts are involved in a 300-page bundle. They ask their agent to scan the text and get a categorized count of every tribunal mentioned. ### Jurisdictional Analysis An analyst wants to see how often TRT1 vs TRT2 appears in labor law files to determine concentration. The agent uses search_legal_entities to provide the exact tally. ### Compliance Audit A corporate officer checks a report for mentions of CVM, BACEN, or CADE. The agent scans the document and returns a summary of all regulatory entities found. ### Data Normalization A legal tech team needs to tag thousands of documents with the correct court types. This Connector extracts the names and categories automatically for the database. ## Benefits - Eliminate AI hallucinations by using deterministic regex matching for all court entities instead of relying on probabilistic guesses. - Get organized results grouped by category like Superior Tribunals or State Courts using search_legal_entities for cleaner data. - Identify every major regulatory body like ANVISA or CADE without manual searching or complex keyword lists. - Handle high-volume documents quickly to find jurisdictional concentration in labor law cases or large litigation bundles. - Add your own custom entities via a JSON dictionary to cover niche bodies or entities from other jurisdictions. ## How It Works The bottom line is it turns messy legal text into a structured, verified list of Brazilian authorities. 1. Provide the legal text or document to your AI client. 2. The Connector runs a strict regex scan against the pre-indexed Brazilian judiciary database. 3. You get a categorized list of every entity found with its total mention count. ## Frequently Asked Questions **Does the Brazilian Judiciary Dictionary Engine find all types of courts?** Yes, it covers Superior Tribunals, Federal Regional Courts (TRFs), Regional Labor Courts (TRTs), and every State Court (TJs) including TJDFT. **Can I use the Brazilian Judiciary Dictionary Engine for US courts?** No, this Connector is specifically built for the Brazilian judiciary and regulatory apparatus. For other countries, you'll need to use the custom dictionary parameter. **How accurate is the entity detection?** It's extremely accurate because it uses strict regex boundary matching against a pre-indexed list. It doesn't rely on AI inference, so it won't hallucinate court names. **Does the Brazilian Judiciary Dictionary Engine include regulatory bodies?** Yes, it includes major agencies like ANVISA, BACEN, CVM, and CADE, along with others like INPI and ANATEL. **Can I add my own custom agencies to the list?** You can. The Connector supports an extensible custom JSON dictionary if you need to include entities from other jurisdictions or niche bodies. **Will this help me with labor law cases?** Yes, it's particularly useful for identifying TRT mentions to help you determine jurisdictional concentration in labor files. **What exactly is pre-indexed?** The complete Brazilian judiciary: 5 Superior Courts, 6 TRFs, 24 TRTs, 27 TJs (including TJDFT), 3 military TJMs, oversight bodies (CNJ, CNMP, TCU), prosecution and advocacy (AGU, MPF, MPT, MPM, MPDFT, DPU, OAB), and 15 regulatory agencies (CADE, CVM, BACEN, INPI, INSS, SUSEP, ANATEL, ANVISA, ANS, ANAC, ANEEL, ANP, ANA, ANTT, ANTAQ). **Does it cover courts from other countries?** No. This engine covers exclusively the Brazilian legal system. To add courts from other countries, pass a custom JSON dictionary mapping acronyms to full names. Custom entries are tagged separately in the output. **How are results organized?** Each detected entity includes its acronym, full official name, category (Superior, TRF, TRT, TJ, TJM, Controle, MP/Advocacia, Regulador), and exact mention count. A category summary is also provided for quick analysis.