$ loading_
Give coding agents persistent debugging memory across sessions and investigation scopes.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "elim-mcp" yet — see the docs or source repo.
Continue the previous debugging investigation for this API timeout issue. First summarize what has been tried, what has been ruled out, and what still needs validation, then suggest next steps.
A continued debugging summary with attempted fixes, ruled-out causes, unresolved hypotheses, and recommended next steps.
Chain together the investigation records for this issue across local development, staging, and production, and explain what was found in each scope and which conclusions can be reused.
A scope-based debugging record that helps the team quickly understand findings and shared conclusions across environments.
Review the historical debugging memory for this bug and tell me which approaches have already been tried and found ineffective so I can avoid repeating them.
A list of already-tried ineffective approaches, plus directions that are more worth investigating next.
Developers can use it to preserve the investigation chain across days or multiple debugging rounds, avoiding loss of validated findings. This helps them resume analysis quickly even after switching sessions.
When multiple coding agents or engineers work on the same issue in sequence, it can track what was tried, ruled out, and solved. This reduces repeated work and improves collaboration efficiency.
For issues that need ongoing investigation across different sessions or scopes, it can chain debugging memory together. It fits troubleshooting workflows that require persistent context.
It is an MCP server that gives coding agents persistent, chained memory for debugging investigations. It tracks what has been tried, ruled out, and solved during the investigation process.
It is suitable for ongoing debugging and troubleshooting work, especially when issues are followed across sessions or scopes. Its core value is preserving investigation context and reducing repeated attempts.
The provided material does not include installation steps, runtime requirements, or key information. See the source repository for exact prerequisites and integration details.
Give AI agents persistent memory, searchable knowledge, and automatic consolidation.
Give AI coding assistants memory, code graph insight, and safe multi-agent coordination.
Give AI coding agents persistent semantic memory with embedding-based retrieval.
Centralize memory, tools, and logic for leaner prompts and scalable AI agents.
Give AI coding agents long-term memory, search, and organization across sessions.
Delegate coding tasks to Codex via MCP with security and result checks.