ABAPilot
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- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to access SAP ADT APIs for reading, writing, debugging, deploying, and testing ABAP code through natural language or DSL automation.495MIT
- AlicenseAqualityDmaintenanceEnables AI agents to read, write, activate, and transport ABAP code in SAP systems via ABAP ADT REST API, without needing SAP GUI.24396 npmMIT
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- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to work with SAP ABAP systems through the ADT REST API, supporting repository search, source code read/write, activation, syntax checks, and transport request management.1Apache 2.0
- AlicenseNot gradedqualityCmaintenanceConnects AI assistants to SAP systems through the ABAP Development Tools API, exposing 85 tools that cover the full ABAP development lifecycle — reading and writing source, creating and activating objects, managing transports, running syntax/ATC checks and unit tests, querying tables, refactoring, remote debugging, tracing, and executing code — all without SAP GUI. Supports single- or multi-system landscapes with credentials supplied by the MCP client at launch.MIT
- FlicenseNot gradedqualityCmaintenanceConnects to SAP ABAP Development Tools (ADT) via MCP, enabling AI assistants to manage SAP systems through natural language.-

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AlicenseNot gradedqualityDmaintenanceMCP server for SAP ABAP development via the ADT API. Connect AI assistants to your SAP system — read, write, test, and deploy ABAP code without SAP GUI.13MIT- AlicenseNot gradedqualityDmaintenanceAn AI-powered server that provides comprehensive coding assistance for SAP ABAP development, including tools for code generation, analysis, and refactoring. It integrates with various LLMs and the SAP ADT REST API to enable intelligent interaction with ABAP repository objects and best practices.402 npmMIT
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TDQS
Scored across 49 tools
Multiple tools have overlapping retrieval purposes (e.g., sap_read_table_data vs sap_read_table_data_enhanced vs sap_smart_table_query, sap_read_domain_values vs sap_read_field_domain_values, sap_read_change_docs vs v2). While some descriptions explicitly redirect, the sheer number of near-duplicate read/query variants creates real selection ambiguity.
The set consistently uses the sap_ prefix and mostly verb_noun patterns (read_, write_, search_, check_, run_, etc.). However, there are style mismatches such as smart_table_query vs read_table_data, discover_change_object vs read_change_docs, and get_* vs read_* for similar retrieval actions, making the pattern less predictable.
49 tools is well beyond the 25+ threshold for a single MCP server, making it heavy even for a broad SAP domain. While each tool addresses a specific niche, the set would be better split into focused servers (e.g., data access, code management, diagnostics).
The tool surface covers a wide range of SAP operations—table reads, metadata, source code, translations, dumps, jobs, notes, and enhancements. However, lifecycle operations are incomplete: there is no create/activate/delete for repository objects, no data modification, and run_transaction only supports report transactions, leaving notable dead ends.