生成目录树结构
分析 JavaScript/TypeScript 和 Python 文件
提取代码符号（函数、变量、类、导入、导出）
兼容MCP协议，与AI助手无缝集成
Related MCP server: syntax-map-mcp
要通过Smithery自动为 Claude Desktop 安装 Code Context Provider：
npx -y @smithery/cli install @AB498/code-context-provider-mcp --client claude{
"mcpServers": {
"code-context-provider-mcp": {
"command": "cmd.exe",
"args": [
"/c",
"npx",
"-y",
"code-context-provider-mcp@latest"
]
}
}
}{
"mcpServers": {
"code-context-provider-mcp": {
"command": "npx",
"args": [
"-y",
"code-context-provider-mcp@latest"
]
}
}
}或者使用npm全局安装：
npm install -g code-context-provider-mcp然后通过运行来使用它：
code-context-provider-mcp # if you're not using @latest, you may want to clear the cache for latest version using `Remove-Item -Path "$env:LOCALAPPDATA\npm-cache\_npx" -Recurse -Force` for windows and `rm -rf ~/.npm/_npx` for linux/macosget_code_context分析目录并返回其结构以及代码符号（可选）。
参数：
absolutePath （字符串，必需）：要分析的目录的绝对路径
analyzeJs （布尔值，可选）：是否分析 JavaScript/TypeScript 和 Python 文件（默认值：false）
includeSymbols （布尔值，可选）：是否在响应中包含代码符号（默认值：false）
symbolType （枚举，可选）：如果 includeSymbols 为真，则包含的符号类型（选项：'functions'、'variables'、'classes'、'imports'、'exports'、'all'，默认值：'all'）
filePatterns （字符串数组，可选）：要分析的文件模式（例如 [' .js', ' .py', 'config.*']）
maxDepth （数字，可选）：要分析的最大目录深度（默认值：5 级）
注意：匿名函数会自动从结果中过滤掉。
Directory structure for: C:\Users\Admin\Desktop\mcp\context-provider-mcp
Code Analysis Summary:
- Files analyzed: 3
- Total functions: 29
- Total variables: 162
- Total classes: 0
Note: Symbol analysis is supported for JavaScript/TypeScript (.js, .jsx, .ts, .tsx) and Python (.py) files only.
Code analysis limited to a maximum depth of 5 directory levels (default).
├── index.js (39 KB)
│ └── [Analyzed: 22 functions, 150 variables, 0 classes]
│ Functions:
│ - initializeTreeSitter [39:0]
│ - getLanguageFromExtension [107:0]
│ - getPosition [138:24]您可以使用filePatterns参数指定要分析的文件。这对于包含多种语言或特定文件的复杂项目非常有用。
例子：
["*.js", "*.py"] - 分析所有 JavaScript 和 Python 文件
["config.*"] - 分析所有配置文件，无论其扩展名是什么。
["package.json", "*.config.js"] - 分析 package.json 和任何 JavaScript 配置文件
[".ts", ".tsx", ".py"] - 分析 TypeScript 和 Python 文件（使用扩展格式）
文件模式匹配支持：
带有通配符 (*) 的简单 glob 模式
直接文件扩展名（带或不带点）
确切的文件名
对于非常大的项目，您可以使用maxDepth参数来限制工具遍历目录的深度：
maxDepth: 2 - 仅分析根目录和一层子目录
maxDepth: 3 - 分析根目录和两级子目录
maxDepth: 0 - 仅分析根目录中的文件
这在以下情况下特别有用：
使用大型 monorepos
分析具有许多依赖项的项目
只关注主要源代码，不关注第三方库
支持以下代码符号分析：
JavaScript (.js)
JSX（.jsx）
TypeScript（.ts）
TSX（.tsx）
Python（.py）
使用filePatterns参数允许您在目录结构中包含其他文件类型，但符号分析可能会受到限制。
# Clone the repository
git clone https://github.com/your-username/code-context-provider-mcp.git
cd code-context-provider-mcp
# Install dependencies
npm install
# Set up WASM parsers
npm run setup安装完成后，软件包的prepare脚本会自动运行，以下载 WASM 解析器。如果由于某种原因下载失败，用户可以手动运行安装程序：
npx code-context-provider-mcp-setup麻省理工学院
Returns Complete Context of a given project directory, including directory tree, and code symbols. Useful for getting a quick overview of a project. Use this tool when you need to get a comprehensive overview of a project's codebase. Useful at the start of a new task.
| Name | Required | Description | Default |
|---|---|---|---|
| absolutePath | Yes | Absolute path to the directory to analyze. For windows, it is recommended to use forward slashes to avoid escaping (e.g. C:/Users/username/Documents/project/src) | |
| analyzeJs | No | Whether to analyze JavaScript/TypeScript and Python files. Returns the count of functions, variables, classes, imports, and exports in the codebase. | |
| includeSymbols | No | Whether to include code symbols in the response. Returns the code symbols for each file. | |
| maxDepth | No | Maximum directory depth for code analysis (default: 5 levels). Directory tree will still be built for all levels. Reduce the depth if you only need a quick overview of the project. | |
| symbolType | No | Type of symbols to include if includeSymbols is true. Otherwise, returns only the directory tree. | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (context, directory tree, code symbols) and its usefulness for project overviews, but lacks details on performance, error handling, or specific output format. This provides basic behavioral context but leaves gaps for a tool with 5 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences that each add value: stating the tool's purpose, its usefulness for overviews, and when to use it. It is front-loaded with the core functionality. A minor deduction for slight redundancy ('useful' appears twice).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no output schema, no annotations), the description is moderately complete. It covers the purpose and usage context but lacks details on output format, error cases, or performance considerations. Without an output schema, more guidance on return values would be beneficial for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema fully documents all 5 parameters. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining interactions between parameters like analyzeJs and includeSymbols. The baseline score of 3 reflects adequate but not enhanced parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('returns complete context') and resources ('project directory, including directory tree, and code symbols'). It distinguishes the tool's comprehensive overview capability, which is well-defined even without sibling tools for comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('useful for getting a quick overview of a project' and 'useful at the start of a new task'). However, it lacks explicit guidance on when not to use it or alternatives, as there are no sibling tools mentioned to differentiate from.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0get_code_contextScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose focused on providing project context.
The naming follows a consistent verb_noun pattern (get_code_context), and with only one tool, there is no inconsistency to evaluate. The naming is clear and descriptive.
A single tool is generally too few for most MCP server purposes, as it limits functionality and can feel thin. For a 'Code Context Provider', one tool may not cover all potential needs like updating or filtering context, making it borderline inadequate.
The tool set is severely incomplete for the implied domain of code context provision. While it offers a comprehensive overview, there are obvious gaps such as tools for updating context, filtering by file type, or handling specific symbols, which could lead to agent workarounds or failures.
Codebase graphs, caller impact analysis, and recorded project context for AI coding agents.
Code intelligence for LLMs. Analyze, search, and retrieve code from any public git repository.
Code intelligence for coding agents: semantic, AST, graph, and full-text search. 279+ languages.
Deterministic context layer for your codebase: change impact, blast radius, answers with receipts.
