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Codebase Memory MCP

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Accelerate code discovery with graph-backed insights.

by github37.7k stars on github/awesome-copilot
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Updated Aug 10, 2026
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Free · Opens the source repo

What Codebase Memory MCP does

Codebase Memory MCP is a tool designed to enhance the process of exploring and understanding complex codebases by leveraging a graph-based memory server. It assists developers and designers in discovering project structures, tracing dependencies, and locating symbols efficiently. By utilizing this skill, users can navigate unfamiliar codebases with greater ease, gaining insights into architecture and data flows that are often difficult to ascertain through traditional means.

The skill operates by connecting to a configured Codebase Memory server, which provides a graph representation of the codebase. Users can initiate various commands to explore projects, retrieve architectural insights, and conduct symbol lookups. For instance, the list_projects command allows users to view indexed projects, while get_architecture provides an overview of the code structure. This capability is particularly beneficial for teams working on large or legacy systems where understanding the interdependencies and structure is crucial for effective development.

Codebase Memory MCP also includes features for tracing the flow of data and identifying impacts of changes within the code. Commands like trace_path enable users to track callers and callees, making it easier to analyze how different parts of the code interact. This functionality is essential for developers who need to assess the ramifications of code modifications or to understand how features are implemented across various modules.

While Codebase Memory MCP is a powerful tool for code discovery, it is important to remember that it should not be the sole source of truth. Users are encouraged to validate graph-derived insights with actual code snippets or local files to ensure accuracy before making significant changes. This skill is ideal for developers and teams who need to navigate complex codebases efficiently but should be complemented with traditional code exploration methods for comprehensive understanding.

When to use it

Use Codebase Memory MCP when working with large or unfamiliar codebases that require efficient exploration and understanding of architecture and dependencies.

When not to use it

This skill may not be suitable for small projects or straightforward codebases where traditional navigation methods suffice. Additionally, it should not be relied upon as the only source of information without validating against the actual code.

What you can build with it

Exploring a New Codebase

When starting work on a new project, use Codebase Memory MCP to quickly familiarize yourself with the architecture and key components.

Impact Analysis Before Changes

Before making modifications to the code, utilize the tracing capabilities to understand potential impacts across the codebase.

Navigating Legacy Systems

When dealing with legacy code, leverage the skill to uncover dependencies and relationships that may not be documented.

How to install Codebase Memory MCP

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1. Install with the skills CLI

npx skills add github/awesome-copilot/codebase-memory-mcp --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by github

Codebase Memory MCP

Use the configured Codebase Memory graph as a discovery accelerator, not as the sole source of truth. Confirm graph-derived conclusions with source snippets or local files before editing code or making strong claims.

Workflow

  1. Discover the Codebase Memory tools exposed by the current MCP client; clients may prefix or rename tool namespaces.
  2. Call list_projects when available and use the exact indexed project name. If the repository is not indexed, continue with local exploration or ask before calling index_repository when graph access is important.
  3. Before branch-sensitive or edit-sensitive conclusions, use index_status and verify the actual version-control state. Use detect_changes only when its Git base and head are valid for the checkout. If it unexpectedly reports zero changes, or the checkout uses another VCS, inspect that VCS's status or diff before claiming no impact.
  4. Use get_architecture once for unfamiliar structure. Request clusters to discover de-facto module seams. Treat cycles as an opt-in whole-call-graph scan: path does not scope cycle detection, so verify relevant cycles before making module-local claims.
  5. Use search_graph for definitions, implementations, routes, classes, interfaces, and related symbols. Prefer a natural-language query for discovery and a name or qualified-name pattern for known symbols. Narrow by label or path and set a result limit. For exhaustive claims, increase offset by limit while has_more is true.
  6. Use search_code or normal repository search for literal strings, configuration keys, test identifiers, error messages, and non-code files. Do not turn a precise text lookup into a broad graph query.
  7. After graph search, use get_code_snippet with the returned qualified name. If source snippets are unavailable, open the local file before relying on the result.
  8. Use trace_path for callers, callees, dependency paths, data flow, cross-service paths, and impact analysis. Include tests when the claim covers them. While truncated is true, pass next back as cursor with every other argument unchanged.
  9. After identifying candidate files, call check_index_coverage for every cited path. Before negative or exhaustive claims, also check the relevant scopes; advance scope_offset to each next_offset while has_more is true. This metadata is best-effort, not proof of completeness. Inspect local source for partial, skipped, excluded, stale, or otherwise uncovered paths.
  10. Use get_graph_schema before custom query_graph calls. Reserve them for bounded multi-hop or aggregate questions, apply LIMIT or max_rows, and use graph="missed" to audit files the main graph did not fully index.
  11. Complete every relevant result stream before an exhaustive claim. For bounded discovery, stopping early is acceptable when the result states its limit or truncation. When graph and checked-out source disagree, treat source as current and report likely index drift.

Indexing Modes

  • Use moderate by default for normal indexing: it filters files while retaining similarity and semantic edges.
  • Use fast only for an explicitly requested smoke index, or when moderate is blocked and a degraded fallback is useful. Disclose that similarity and semantic edges are absent.
  • Use full when moderate-only discovery filters omit relevant supported files and the additional indexing cost is justified. Full still honors .gitignore, .cbmignore, and always-skip rules.

Safety and Fallbacks

  • Do not install Codebase Memory or another third-party skill from this workflow.
  • Do not call delete_project, ingest traces, update ADRs, or index a repository unless the user explicitly requested or approved the action; announce it before execution.
  • Fall back to normal repository exploration when the MCP server, project, index, or required capability is unavailable; do not invent tool results or stop a task that can be completed safely without the graph.

Frequently asked questions about Codebase Memory MCP

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