模型上下文协议 (MCP) 服务器，使 AI 模型能够通过自然语言对话理解和分析代码库。
自然代码探索：询问有关代码库的高级问题
"What are all the different payment providers integrated in the system?"深度代码理解：提取有关数据模型和系统架构的见解
"How does the user authentication flow work from frontend to database?"动态分析：跟踪数据流并了解系统关系
"Show me all the places where we calculate transaction fees"Related MCP server: Notion MCP Server
此工具是Aider等更复杂的代码分析工具/副驾驶的一个更简单的替代方案。虽然它缺乏 Aider 等工具的高级代码分析功能和稳健性，但如果您已经订阅了 Claude Pro，它为代码库探索提供了一个轻量级的解决方案。主要优点如下：
经济高效：使用您现有的 Claude Pro 订阅意味着无需额外的 API 成本，这与分析大型代码库时可能变得昂贵的工具不同
简单设置：只需最少的配置即可快速上手
基本分析：有利于高级代码理解和探索
请注意，由于其代码分析方法更简单，与更专业的工具相比，它可能会犯更多错误或提供不太详细的见解。
安装服务器：
git clone https://github.com/saiprashanths/code-analysis-mcp.git
cd code-analysis-mcp安装Claude 桌面应用。有关使用 Claude 桌面应用设置 MCP 服务器的更多说明，请参阅文档
在Claude Desktop的配置文件中配置服务器：
macOS： ~/Library/Application Support/Claude/claude_desktop_config.json
Windows： %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"code-analysis": {
"command": "uv",
"args": [
"--directory",
"/PATH/TO/YOUR/REPO",
"run",
"code_analysis.py"
]
}
}
}重新启动 Claude Desktop 并开始分析您的代码！
确保工具正确设置
单击工具图标将显示code-analysis工具
单击集成按钮应显示analyze_code_repository提示
您可以按照以下方式使用该工具来理解复杂的代码库：
首先点击下面显示的analyze_code_repository提示符。这将引导您完成初始化代码库并开始使用。
You: How does the transaction processing work in this system?
Claude: Let me analyze the data flow for transactions.
Looking at the codebase, I can see that transaction processing happens through several key components:
1. Raw transaction data is captured in the `TransactionEvent` model with fields for:
- Payment status
- Amount
- Fee calculations
- Processing timestamps
2. This data is then processed through the PaymentSummary model, which:
- Validates payment provider responses
- Calculates processing fees
- Records transaction status
Would you like me to explain any part of this in more detail?
You: What are the key database tables involved?
Claude: The main tables in the transaction processing pipeline are:
1. transaction_events: Stores raw payment events
2. payment_summary: Processed transaction records
3. payment_providers: Provider configuration
4. merchant_accounts: Account-specific settings
Let me know if you'd like to see the schema for any of these tables!初始化代码存储库以供分析：
initialize_repository("/path/to/repo")
# Output: Successfully initialized code repository at: /path/to/repo检索有关已初始化存储库的信息：
get_repo_info()
# Output:
# Code Repository Information:
# Path: /path/to/repo
# Exists: True
# Is Directory: True
# Found .gitignore file检查存储库文件结构：
get_repo_structure(depth=2)
# Output:
# 📁 src/
# 📁 api/
# 📄 routes.py
# 📄 models.py
# 📁 utils/
# 📄 helpers.py
# 📄 main.py阅读并分析具体文件：
read_file("src/api/models.py")
# Output:
# File: src/api/models.py
# Language: python
# Size: 2.3 KB
#
# [File contents...]默认扫描深度：3级
最大文件大小：1MB
每个文件的最大行数：1000
忽略 .gitignore 中列出的路径
仅限本地文件系统访问
验证文件路径以防止目录遍历
该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅LICENSE文件。
Get information about the currently initialized code repository.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states the tool gets information, implying a read-only operation, but doesn't specify what type of information (e.g., metadata, status, details) or any behavioral traits like error handling, permissions needed, or response format. This leaves significant gaps for a tool with zero annotation coverage.
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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured.
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 (simple read operation) but lack of annotations and output schema, the description is incomplete. It doesn't explain what information is returned, potential errors, or how it differs from siblings, leaving the agent with insufficient context to use it effectively beyond basic purpose.
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 tool has 0 parameters, and the schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't mention parameters, aligning with the schema. A baseline of 4 is applied as it handles the zero-parameter case correctly without redundancy.
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 verb ('Get information') and resource ('currently initialized code repository'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'get_repo_structure' or 'initialize_repository', which prevents a perfect score.
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 minimal guidance by implying usage when a repository is 'currently initialized,' but it doesn't specify when to use this tool versus alternatives like 'get_repo_structure' or 'initialize_repository.' No explicit when-not-to-use or prerequisite information is included.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the structure of files and directories in the repository.
Args:
sub_path: Optional subdirectory path relative to repository root
depth: Optional maximum depth to traverse (default is 3)
| Name | Required | Description | Default |
|---|---|---|---|
| sub_path | No | ||
| depth | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states what the tool does but lacks behavioral details such as whether this is a read-only operation, potential rate limits, error conditions, or the format of the returned structure. The description doesn't contradict annotations, but it's insufficient for a mutation-free tool with zero annotation coverage.
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 and front-loaded with the purpose in the first sentence, followed by parameter details. It's efficient with no wasted sentences, though the parameter explanations could be slightly more integrated into the flow.
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 moderate complexity (2 optional parameters) and lack of annotations or output schema, the description is partially complete. It covers the purpose and parameters well but misses behavioral context and usage guidelines, leaving gaps for an AI agent to understand full tool behavior.
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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'sub_path' is an 'Optional subdirectory path relative to repository root' and 'depth' is an 'Optional maximum depth to traverse (default is 3)', providing clear semantics and default values not present in the schema.
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: 'Get the structure of files and directories in the repository.' This is a specific verb ('Get') and resource ('structure of files and directories'), though it doesn't explicitly distinguish from sibling tools like 'get_repo_info' or 'read_file'.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_repo_info' (which might provide metadata) or 'read_file' (which reads file contents), nor does it specify prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Initialize the repository path for future code analysis operations.
Args:
path: Path to the repository root directory that contains the code to analyze
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool initializes a path for future operations, but doesn't explain what 'initialize' entails (e.g., does it cache data, set up configurations, or validate the path?). It also omits details like error handling, permissions needed, or side effects, leaving significant gaps for a tool with no annotation support.
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 and front-loaded: the first sentence states the core purpose, followed by a brief parameter explanation. There's no wasted text, and the structure is clear. However, it could be slightly more efficient by integrating the parameter info more seamlessly, preventing a perfect score.
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 complexity (a setup tool with no annotations, no output schema, and low schema coverage), the description is incomplete. It doesn't explain what happens after initialization (e.g., success/failure states, return values, or how it interacts with sibling tools like 'read_file'). For a tool that likely enables other operations, more context on its role and outcomes is needed.
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 description adds some meaning beyond the input schema: it explains that 'path' is 'Path to the repository root directory that contains the code to analyze,' clarifying the parameter's purpose. However, with 0% schema description coverage and only one parameter, the baseline is 4 for zero parameters, but here it's reduced to 3 because the description doesn't fully compensate for the lack of schema details (e.g., format expectations like absolute vs. relative paths).
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: 'Initialize the repository path for future code analysis operations.' It specifies the verb ('initialize') and resource ('repository path'), making the action explicit. However, it doesn't distinguish this from sibling tools like 'get_repo_info' or 'get_repo_structure', which prevents a perfect score.
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 minimal guidance: it implies this tool should be used before other code analysis operations. However, it doesn't specify when to use it versus alternatives (e.g., if 'get_repo_info' might also initialize a path), nor does it mention prerequisites or exclusions. This lack of explicit context limits its utility for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read and display the contents of a file from the repository.
Args:
file_path: Path to the file relative to repository root
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool reads and displays file contents, implying a read-only operation, but lacks details on permissions, error handling (e.g., for missing files), output format (e.g., text vs. raw bytes), or limitations (e.g., file size constraints). This leaves significant gaps in understanding how the tool behaves beyond its basic function.
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 two sentences: a clear purpose statement and parameter explanation. It's front-loaded with the core function, and the parameter note adds necessary detail without redundancy. However, the formatting with 'Args:' could be slightly more integrated for optimal flow.
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 low complexity (one parameter, no output schema, no annotations), the description is minimally complete but has gaps. It covers the basic purpose and parameter semantics but lacks usage guidelines, behavioral details, and output information, making it adequate for simple tasks but insufficient for robust agent operation in varied scenarios.
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 description adds meaningful context for the single parameter 'file_path' by specifying it's 'relative to repository root', which clarifies usage beyond the schema's basic type definition. With 0% schema description coverage and only one parameter, this compensates adequately, though it doesn't detail format constraints (e.g., path syntax).
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 specific action ('Read and display the contents') and resource ('a file from the repository'), distinguishing it from sibling tools like get_repo_info (repository metadata) and get_repo_structure (directory listing). It precisely communicates what the tool does without being vague or tautological.
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 no guidance on when to use this tool versus alternatives. While it implies usage for reading file contents, it doesn't specify prerequisites (e.g., repository must be initialized), exclusions (e.g., binary files), or comparisons to sibling tools like get_repo_structure for browsing directories. This leaves the agent without contextual decision-making help.
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.
get_repo_infoget_repo_structureinitialize_repositoryread_fileScored across 4 tools
Each tool has a clearly distinct purpose: initialize_repository sets up the context, get_repo_info provides metadata, get_repo_structure shows the file hierarchy, and read_file accesses file contents. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent verb_noun pattern (e.g., initialize_repository, get_repo_info, get_repo_structure, read_file). The naming is uniform and predictable, using snake_case throughout with clear action descriptors.
With 4 tools, the count is reasonable for a code analysis server, covering core operations like initialization, metadata retrieval, structure viewing, and file reading. However, it feels slightly thin, as advanced analysis features (e.g., linting, dependency checks) are absent, though not essential for basic functionality.
The toolset covers basic repository operations (initialize, info, structure, read), but there are notable gaps for a code analysis domain, such as missing tools for analyzing code quality (e.g., lint, test), searching within files, or modifying content. Agents can perform foundational tasks but may hit dead ends for deeper analysis.
The Ramp MCP server enables users to securely connect Ramp with AI assistants like ChatGPT and Claude to query financial data and take actions using natural language. It transforms Ramp's developer API into a SQL interface that LLMs can query, allowing admins to analyze spend trends, identify cost savings, and run complex SQL analyses on comprehensive datasets (transactions, purchase orders, vendors, users), while all users can manage cards, view transactions, request reimbursements, and get expense policy answers.
Enterprise code intelligence for M&A, security audits, and tech debt. Hosted server with 200k free.
Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer
The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.
