topoteretes/cognee
Jan 2026 – Jan 2026
1 Month active
Languages Used
Python
Technical Skills
API developmentPythonasynchronous programmingbackend developmentdocumentation
AnveshJarabani
PROFILE
Anveshjarabani
During January 2026, Xzput enhanced the MCP search functionality in the topoteretes/cognee repository by introducing and standardizing a configurable top_k parameter throughout the server, client, and core search layers. Using Python and asynchronous programming, Xzput ensured backward compatibility while reducing response sizes by approximately 97% and improving search latency by up to 90%. The engineering work included updating internal call chains, aligning default values, and clarifying recommended usage in both code and documentation. Comprehensive docstrings and documentation updates improved API clarity, while production validation confirmed the changes delivered measurable business value without introducing regressions or breaking changes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
42
Activity Months1
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Work History
January 2026
2 Commits • 1 Features
Jan 1, 2026Month: 2026-01 — Key MCP search enhancements in topoteretes/cognee delivering measurable business value and solid engineering quality. Implemented top_k parameter support and standardized its usage across the MCP call chain, enabling configurable search results while preserving backward compatibility. The changes touched server-level wiring, internal search_task flow, and cognee_client integration, aligning the whole stack to a consistent top_k surface. Key engineering work included extending the MCP search to accept top_k (default 5) and propagating it to cognee_client.search() and core search, along with comprehensive documentation updates. The commit set also focuses on documentation quality with improved docstrings and clarifications on default values and performance implications.
2 Commits • 1 Features
Jan 1, 2026Month: 2026-01 — Key MCP search enhancements in topoteretes/cognee delivering measurable business value and solid engineering quality. Implemented top_k parameter support and standardized its usage across the MCP call chain, enabling configurable search results while preserving backward compatibility. The changes touched server-level wiring, internal search_task flow, and cognee_client integration, aligning the whole stack to a consistent top_k surface. Key engineering work included extending the MCP search to accept top_k (default 5) and propagating it to cognee_client.search() and core search, along with comprehensive documentation updates. The commit set also focuses on documentation quality with improved docstrings and clarifications on default values and performance implications.
January 2026
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture90.0%
Performance100.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API developmentPythonasynchronous programmingbackend developmentdocumentation
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