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Read, edit, and refactor code precisely with AST-based, token-efficient operations.
The materials indicate this MCP tool focuses on local AST-based code reading, writing, and refactoring, with no required secrets and no declared remote endpoints; no clear high-risk red flags are evident. However, it has code-execution-related capability and can modify local code files, while community adoption and maintenance signals are weak, so it should be used in a constrained environment.
The materials explicitly state that no keys or environment variables are required. No API tokens, account credentials, or other sensitive authentication data are requested, so credential exposure and abuse risk appears low.
No remote endpoints or network dependencies are declared, and the available materials do not indicate that code or user data is sent to external services; based on the stated facts, network egress risk appears low.
The system checks mark it as having executes-code capability. Combined with its stated purpose of reading, writing, and refactoring code, it likely has some local code-processing or execution-related capability. This is a common MCP-tool risk surface and warrants restricting its runtime environment and project scope.
The description explicitly says it can read, write, and refactor code, which implies access to local repository contents and the ability to modify source files. This scope is broadly consistent with its stated function, but it still represents meaningful access to local development assets.
Positive signals include that it is open source and MIT-licensed, making it more auditable than closed-source tools. However, it comes from a third-party registry, has only 0 stars, unknown maintenance status, and lacks README detail, so supply-chain confidence is limited and the source and dependencies should be reviewed first.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-code-context" yet — see the docs or source repo.
Use AST-based operations to scan the project and refactor all logger.logInfo calls to logger.info, keeping argument order unchanged. Then list modified files and summarize the changes.
A list of affected files, precise refactoring results, and a brief explanation of each change.
Read this TypeScript module, identify code that does not match the UserProfile interface, directly fix the field names and type declarations, and explain why the changes are the safest option.
Corrected code snippets, an explanation of the errors, and the rationale for the fixes.
Do not read the entire repository. Using AST only, extract definitions, call chains, and dependencies related to the createInvoice method in paymentService, and organize them into a context summary for follow-up questions.
A compact structured context containing key symbols, call relationships, and relevant files.
Gives AI coding agents a structural map of your repository fast.
Build semantic memory and structural code indexes for persistent AI project context.
Read, search, and update codebase documentation context through MCP.
Explore codebases faster with context-aware search and fuzzy snippet matching.
Share code with LLMs via MCP or clipboard with task-specific context rules.
Safely run Python code with AI and MCP tool integration.