Wrap up a session by summarizing work, capturing repo changes as commit links, storing the record, proposing task candidates, and writing a handoff document. Returns an ordered checklist for the agent to execute.
MIT
306,653 tools. Last updated 2026-07-25 18:18
"A tool for storing and maintaining up-to-date codebase memory/context" matching MCP tools:
- Inspects a file to reveal its functions, classes, imports, and relationships with other files for codebase understanding.MIT
- Scan and analyze a codebase using tree-sitter AST analysis to produce a structured context payload. Enables deep codebase understanding for code review, refactoring, and planning.MIT
- Before starting work, retrieve a condensed project context snapshot: recent sessions, favorites, and stats to load memory.MIT
- Bundle codebase context for a task: overview, relevant code, symbols, and memories. Optionally generate a PageRank-pruned repo map within a token budget to orient an agent.MIT
- Check project index health, memory summary, and get tailored tool recommendations to understand the current codebase state.MIT
Matching MCP Servers
- AlicenseAqualityBmaintenanceProvides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.Last updated1120953Elastic 2.0
- Alicense-qualityDmaintenanceProvides persistent memory for AI coding agents through the Model Context Protocol, enabling them to store and retrieve project knowledge across sessions.Last updated89MIT
Matching MCP Connectors
Stop re-explaining yourself to Agents. Give it the right context, right when needed.
MCP server for accessing curated awesome list documentation
- Store a dialog turn with role, content, and metadata to persistent session memory for later retrieval and context assembly.MIT
- Search codebases using natural language queries to locate relevant code snippets. Automatically indexes projects to provide up-to-date results with file paths and line numbers for development workflows.Apache 2.0
- Delegate complex multi-step tasks to autonomous agents for independent execution with dedicated context, maintaining conversation continuity across sessions.MIT
- Retrieve privacy-filtered personal context from your local memory by searching for a specific topic. Use this to answer questions about your history, preferences, or stored information.MIT
- Consolidates search, intentions, status, predictions, and codebase context into a single response to initialize a session efficiently, reducing multiple calls to one.AGPL 3.0
- Search persistent memory for prior context and decisions before answering project-specific questions. Prevents amnesia by retrieving stored information.MIT
- Improves developer prompts by adding quality requirements, codebase context, and tool recommendations to help AI coding assistants generate better structured code.MIT
- Retrieve stored memories for the current project, including codebase knowledge, conventions, decisions, and session context. Results sorted by priority and recency.MIT
- Retrieve architectural context from a codebase-memory graph to understand module boundaries, dependencies, ADRs, and hotspot files for structured query planning.MIT
- Ask programming questions and get explanations from Kimi's model without codebase context. Use for algorithms, code reviews, or general coding advice.MIT
- Provides a ranked, token-capped briefing of the most relevant codebase context to prime understanding at session start, avoiding context window overload.MIT
- Index a codebase directory to enable semantic code search. Scans files and extracts functions, classes, and blocks into an in-memory search index.MIT
- Retrieves relevant code symbols and snippets from a codebase index, returning a token-budgeted context block of matching functions, classes, and types for efficient model use.MIT
- Retrieve recent memory activity summaries including memory counts, recent entries, and unresolved problems for session context analysis.MIT