The Chancery Lane Project Opens Climate Legal Knowledge Graph via MCP — ChatGPT and Claude Can Navigate Clauses, Context, and Relationships Directly
The Chancery Lane Project (TCLP), the global network of 3,600+ lawyers across 113 countries building climate clauses for commercial contracts, launched a new API and MCP server on July 14 that makes its climate legal knowledge graph directly accessible to legal technology platforms, AI assistants, and developers. The MCP server allows AI assistants including ChatGPT and Claude to search, navigate,
BY FRONTIER DESK · JULY 20, 2026 · 1 MIN READ
The Chancery Lane Project (TCLP), the global network of 3,600+ lawyers across 113 countries building climate clauses for commercial contracts, launched a new API and MCP server on July 14 that makes its climate legal knowledge graph directly accessible to legal technology platforms, AI assistants, and developers. The MCP server allows AI assistants including ChatGPT and Claude to search, navigate, and interrogate the relationships within TCLP's structured knowledge graph — returning not just individual clauses but the interconnected collection of implementation guidance, legal concepts, definitions, and related climate topics that surround them. A search for "deforestation," for example, returns TCLP's Deforestation and Land Use Change Clause, its accompanying questionnaire, implementation guidance, the Carbon Sequestration definition, and related concepts including carbon sinks, ecological restoration, embodied carbon, and offsetting — all with their relationships preserved. The platform is developer-accessible at mcp.chancerylaneproject.org with no stated pricing; the launch was supported by the Patrick J. McGovern Foundation, which described TCLP as "a model for scalable public-interest AI infrastructure." For legal KM leaders, TCLP's architecture is a template for how any practice-specific or regulatory knowledge domain can be structured for AI access: the design principle of exposing relationships between concepts, guidance, definitions, and clauses — rather than returning flat documents — is the difference between a knowledge base an AI can reason with and one it can only quote from.