MCP Server Semgrep integrates the Semgrep static analysis tool with AI assistants via the Model Context Protocol, enabling code security scanning, rule management, and result analysis through a conversational interface.
scan_directory: Run static analysis on a directory to detect security vulnerabilities and code quality issues, with optional configuration (e.g., auto or a custom rule file).
list_rules: Retrieve available Semgrep rules, optionally filtered by programming language.
analyze_results: Perform detailed analysis of a previously generated JSON scan results file (e.g., identify magic numbers, deprecated code, or naming inconsistencies).
create_rule: Define and save custom Semgrep rules by specifying a pattern, target language, message, severity, and output path.
filter_results: Narrow down scan results by rule ID, language, severity level (ERROR, WARNING, INFO), file path pattern (regex), or message content.
export_results: Convert scan results into JSON, SARIF, or plain text formats.
compare_results: Diff two sets of scan results (e.g., before and after a fix) to identify newly introduced or resolved issues.
Beyond the core tools, the server supports automated security verification, technical debt reduction, and streamlined code reviews by proactively surfacing common issues before deployment.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Server Semgrep scan this directory for security vulnerabilities"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This project was initially inspired by robustness of Semgrep tool, The Replit Team and their Agent V2, as well as the implementation by stefanskiasan/semgrep-mcp-server, but has evolved with significant architectural changes for enhanced and easier installation and maintenance.
MCP Server Semgrep is a Model Context Protocol compliant server that integrates the powerful Semgrep static analysis tool with AI assistants like Anthropic Claude. It enables advanced code analysis, security vulnerability detection, and code quality improvements directly through a conversational interface.
Related MCP server: AWS Security MCP
Holistic Source Code Analysis - detecting issues throughout the entire project, not just in individual files
Proactive Error Detection - identifying potential problems before they become critical bugs
Continuous Code Quality Improvement - regular scanning and refactoring lead to gradual codebase improvements
Stylistic Consistency - identification and fixing of inconsistencies in code, such as:
Arbitrary z-index layers in CSS
Inconsistent naming conventions
Code duplication
"Magic numbers" instead of named constants
Automated Code Verification for Known Vulnerabilities - scanning for known security issue patterns
Customized Security Rules - creating project-specific rules
Team Education - teaching secure programming practices through detection of potential issues
"Live" Documentation - AI can explain why a code fragment is problematic and how to fix it
Technical Debt Reduction - systematically detecting and fixing problematic areas
Improved Code Reviews - automatic detection of common issues allows focus on more complex matters
Direct integration with the official MCP SDK
Simplified architecture with consolidated handlers
Clean ES Modules implementation
Efficient error handling and path validation for security
Interface and documentation in both English and Polish
Comprehensive unit tests
Extensive documentation
Cross-platform compatibility (Windows, macOS, Linux)
Flexible Semgrep installation detection and management
Semgrep MCP Server provides the following tools:
scan_directory: Scanning source code for potential issues
list_rules: Displaying available rules and languages supported by Semgrep
analyze_results: Detailed analysis of scan results
create_rule: Creating custom Semgrep rules
filter_results: Filtering results by various criteria
export_results: Exporting results in various formats
compare_results: Comparing two sets of results (e.g., before and after changes)
Code security analysis before deployment
Detection of common programming errors
Enforcing coding standards within a team
Refactoring and improving quality of existing code
Identifying inconsistencies in styles and code structure (e.g., CSS, component organization)
Developer education regarding best practices
Verification of fix correctness (comparing before/after scans)
Node.js v18+
TypeScript (for development)
The easiest way to install and use MCP Server Semgrep is through Smithery.ai:
Follow the installation instructions to add it to your MCP-compatible clients
Configure any optional settings like the Semgrep API token and allowed workspace roots
This is the recommended method for Claude Desktop and other MCP clients as it handles all dependencies and configuration automatically.
# Using npm
npm install -g mcp-server-semgrep
# Using pnpm
pnpm add -g mcp-server-semgrep
# Using yarn
yarn global add mcp-server-semgrepThe package is also available on other registries:
# Using npm
npm install -g git+https://github.com/vetcoders/mcp-server-semgrep.git
# Using pnpm
pnpm add -g git+https://github.com/vetcoders/mcp-server-semgrep.git
# Using yarn
yarn global add git+https://github.com/vetcoders/mcp-server-semgrep.gitClone the repository:
git clone https://github.com/vetcoders/mcp-server-semgrep.git
cd mcp-server-semgrepInstall dependencies (supports all major package managers):
# Using pnpm (recommended)
pnpm install
# Using npm
npm install
# Using yarn
yarn installBuild the project:
# Using pnpm
pnpm run build
# Using npm
npm run build
# Using yarn
yarn buildNote: The installation process will automatically check for Semgrep availability. If Semgrep is not found, you'll receive instructions on how to install it.
This server only reads and writes files inside explicitly allowed workspace roots.
By default, the allowed root is the process working directory (process.cwd()).
For Claude Desktop, Smithery, or any launcher that does not start the server inside your project root, set MCP_SERVER_SEMGREP_ALLOWED_ROOTS to one or more absolute directories.
Use your platform path delimiter for multiple roots: : on macOS/Linux, ; on Windows.
This server does not implement its own Semgrep account handling. It shells out to the installed semgrep CLI and relies on Semgrep's normal authentication behavior.
Local terminal and local development runs can often use an existing semgrep login session from the current OS account.
Managed launches such as Claude Desktop, Smithery, containers, or CI should prefer an explicit SEMGREP_APP_TOKEN for deterministic behavior.
SEMGREP_APP_TOKEN is still the safest option when you need portable configuration across machines or runners.
Semgrep can be installed in several ways:
Via package managers:
# Using pnpm
pnpm add -g semgrep
# Using npm
npm install -g semgrep
# Using yarn
yarn global add semgrepPython pip:
pip install semgrepHomebrew (macOS):
brew install semgrepLinux:
sudo apt-get install semgrep
# or
curl -sSL https://install.semgrep.dev | shWindows:
pip install semgrepThere are two ways to integrate MCP Server Semgrep with Claude Desktop:
Click "Install in Claude Desktop"
Follow the on-screen instructions
Install Claude Desktop
Update the Claude Desktop configuration file (claude_desktop_config.json) and add this to your servers section.
For local launches started under a user account that is already authenticated with semgrep login, the Semgrep CLI may be able to reuse that login. For desktop-managed or shared environments, we still recommend setting SEMGREP_APP_TOKEN explicitly:
{
"mcpServers": {
"semgrep": {
"command": "node",
"args": [
"/your_path/mcp-server-semgrep/build/index.js"
],
"env": {
"SEMGREP_APP_TOKEN": "your_semgrep_app_token",
"MCP_SERVER_SEMGREP_ALLOWED_ROOTS": "/Users/you/projects"
}
}
}
}Launch Claude Desktop and start asking questions about code analysis.
If you want to scan more than one workspace, set MCP_SERVER_SEMGREP_ALLOWED_ROOTS to a platform-delimited list of absolute paths.
Could you scan my source code in the /projects/my-application directory for potential security issues? That directory is already included in MCP_SERVER_SEMGREP_ALLOWED_ROOTS.Analyze the z-index values in the project's CSS files and identify inconsistencies and potential layer conflicts.Create a Semgrep rule that detects improper use of input sanitization functions.Show me only scan results related to SQL injection vulnerabilities.Find all "magic numbers" in the code and suggest replacing them with named constants.You can create custom rules for your project's specific needs. Here are examples of rules you can create:
rules:
- id: inconsistent-z-index
pattern: z-index: $Z
message: "Z-index $Z may not comply with the project's layering system"
languages: [css, scss]
severity: WARNINGrules:
- id: deprecated-import
pattern: import $X from 'old-library'
message: "You're using a deprecated library. Consider using 'new-library'"
languages: [javascript, typescript]
severity: WARNINGpnpm test├── src/
│ └── index.ts # Main entry point and all handler implementations
├── scripts/
│ └── check-semgrep.js # Semgrep detection and installation helper
├── build/ # Compiled JavaScript (after build)
└── tests/ # Unit testsDetailed information on using the tool can be found in:
USAGE.md - Detailed usage instructions
README_PL.md - Documentation in Polish
examples/ - Example fun and practical Semgrep rules - "The Hall of Code Horrors"
This project is licensed under the MIT License - see the LICENSE file for details.
🤖 Developed with the help of Claude Code and MCP Tools
stefanskiasan for the original inspiration
Anthropic for Claude and the MCP protocol
Semgrep for their excellent static analysis tool
Analyzes scan results
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file (must be within an allowed workspace root) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It only says 'Analyzes', implying a read operation, but does not state if results are modified, returned, or stored. No information about side effects, authorization needs, or output format is given.
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 sentence, which is concise but lacks structuring. It does not provide additional sections or details to aid understanding. The brevity is acceptable but not optimally informative.
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 absence of an output schema and the presence of sibling tools, the description is incomplete. It does not explain what the analysis returns or how it differs from compare_results or filter_results. The tool's functionality remains unclear.
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 provides a complete description for the single parameter (results_file) with context about allowed paths. Since schema coverage is 100%, the description's lack of parameter information is acceptable per guidelines. However, it adds no extra meaning beyond 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 states 'Analyzes scan results', which is a verb+resource, but it is vague. It does not specify what kind of analysis is performed (e.g., statistical, pattern detection, summary) and fails to distinguish from sibling tools like compare_results, filter_results, and export_results.
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?
There is no guidance on when to use this tool versus alternatives. No context, prerequisites, or exclusions are provided, leaving the agent without criteria for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compares two scan results
| Name | Required | Description | Default |
|---|---|---|---|
| old_results | Yes | Absolute path to older JSON results file | |
| new_results | Yes | Absolute path to newer JSON results file |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is minimal ('Compares two scan results') and provides no behavioral details beyond the name. With no annotations, it fails to disclose whether the tool is read-only, its side effects, return behavior, or required permissions.
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 sentence with no extra words, making it concise. However, it could be restructured to front-load more critical information without increasing length significantly.
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?
For a tool with no output schema and only two string parameters, the description does not explain what the comparison produces (e.g., diff output, boolean, list of changes). This leaves the agent unsure of the return value and behavior, making it incomplete.
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?
Both parameters are described in the input schema ('Absolute path to older JSON results file' and 'Absolute path to newer JSON results file'), achieving 100% schema coverage. The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.
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 'Compares two scan results' uses a verb ('compares') and resource ('scan results'), clearly indicating the tool's function. It is distinct from siblings like 'analyze_results' and 'filter_results', but lacks specificity on what the comparison entails (e.g., differences, similarities).
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?
No guidance is provided on when to use this tool versus alternatives such as 'analyze_results' or 'filter_results'. There is no mention of prerequisites, when-not-to-use, or explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Creates a new Semgrep rule
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | Yes | Absolute path for output rule file | |
| pattern | Yes | Search pattern for the rule | |
| language | Yes | Target language for the rule | |
| message | Yes | Message to display when rule matches | |
| severity | No | Rule severity (ERROR, WARNING, INFO) | WARNING |
| id | No | Rule identifier | custom_rule |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states 'Creates a new Semgrep rule' with no information about side effects (e.g., overwriting existing files), permissions, or error handling.
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 sentence, which is concise but lacks structure. It front-loads the action but provides no additional detail, making it barely adequate.
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 creates a file (output_path required) and has no output schema, the description should explain return behavior (e.g., success indication) or file naming. It does not, leaving significant gaps for an agent.
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?
Input schema has 100% coverage with clear parameter descriptions. The tool description adds no additional meaning beyond the schema, meeting the baseline for high coverage.
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 uses a specific verb 'Creates' and resource 'a new Semgrep rule', making the core action clear. It naturally distinguishes from siblings which focus on analysis, comparison, and listing, not creation.
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?
No guidance on when to use this tool versus alternatives. The description does not indicate prerequisites (e.g., rule syntax knowledge) or situations where other tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Exports scan results in various formats
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file | |
| output_file | Yes | Absolute path to output file | |
| format | No | Output format (json, sarif, text) | text |
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 fails to mention whether the tool overwrites existing files, requires network access, or produces any side effects. The agent cannot infer safety or error conditions from the description alone.
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 sentence, which is concise but lacks structure. It does not front-load critical information like required parameters or output behavior. The brevity is acceptable but not optimal for usability.
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 no output schema, the description should indicate what the tool returns (e.g., success message, file path). It also does not mention error handling or performance implications. The tool is simple, but the description remains incomplete for fully autonomous invocation.
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?
All 3 parameters are described in the schema with high coverage (100%). The description adds no extra context beyond 'exports scan results in various formats'—it does not elaborate on parameter constraints like valid file paths or format specifics. Baseline 3 is appropriate since schema does the work.
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 'Exports scan results in various formats' clearly indicates the action (export) and resource (scan results) and mentions format variability. However, it does not differentiate from sibling tools like analyze_results or compare_results, which might also output results. The description could be more specific about the exact nature of the export.
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?
No guidance is provided on when to use this tool versus alternatives, such as analyze_results or filter_results. There are no mentions of prerequisites or context in which export is appropriate. The agent is left without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filters scan results by various criteria
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file | |
| severity | No | Filter by severity (ERROR, WARNING, INFO) | |
| rule_id | No | Filter by rule ID | |
| path_pattern | No | Filter by file path pattern (regex) | |
| language | No | Filter by programming language | |
| message_pattern | No | Filter by message content (regex) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It does not disclose whether the tool modifies the original file, requires authentication, or has side effects. The filtering behavior (e.g., AND vs OR logic) is not explained.
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?
Very short single sentence, efficient but lacking critical details. It is concise but not optimally informative for a 6-parameter tool.
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?
With 6 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return format, behavior when no matches, or how it differs from sibling tools like export_results.
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?
Schema coverage is 100% with parameter descriptions, so the description adds minimal value beyond the schema. It does not clarify how multiple filters interact, which leaves ambiguity for the agent.
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 states it filters scan results, which is clear but lacks specificity about the resource (e.g., scan results file) and does not differentiate from sibling tools like analyze_results or compare_results.
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?
No guidance on when to use this tool versus alternatives (e.g., analyze_results for aggregation, compare_results for comparison). No when-not-to-use or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists available Semgrep rules
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Programming language for rules (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description lacks any behavioral details such as authentication needs, rate limits, or whether it returns full rule details or just names.
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?
Single sentence, concise and front-loaded with essential information. No wasted words.
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 no output schema and one optional parameter, the description provides minimal context. It doesn't clarify what information is returned (e.g., rule names only or full definitions).
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?
Schema coverage is 100%, and the description adds no extra meaning beyond the schema's parameter description. Baseline 3 is appropriate.
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 'lists' and resource 'Semgrep rules', distinguishing it from siblings like 'create_rule' and 'scan_directory'.
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?
No guidance on when to use this tool versus alternatives like 'filter_results' or 'analyze_results'. Does not specify when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Performs a Semgrep scan on a directory
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Absolute path to the directory to scan (must be within an allowed workspace root) | |
| config | No | Semgrep configuration (e.g. "auto" or absolute path to rule file) | auto |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states the action without disclosing side effects, permissions, or output behavior.
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?
Single sentence is concise but lacks structure or front-loading of key details. Could be expanded to include usage context.
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?
No output schema and no annotations; description does not explain return values, side effects, or prerequisites, making it incomplete for a scan tool.
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?
Schema description coverage is 100%; both 'path' and 'config' are described in the schema. Description adds no extra meaning beyond 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?
Clear verb+resource: 'Performs a Semgrep scan on a directory' distinguishes from siblings like analyze_results or list_rules.
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?
No guidance on when to use this tool vs alternatives (e.g., analyze_results) or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool targets a distinct operation in the Semgrep workflow: scanning, analyzing, comparing, filtering, exporting results, and managing rules. No two tools appear to perform the same function.
All tool names follow a consistent verb_noun pattern with lowercase and underscores (e.g., scan_directory, list_rules). The verbs are imperative and the nouns are predictable.
Seven tools is well-scoped for a code scanning server, covering the main operations without being excessive or insufficient.
The tool set covers core workflows: scanning, result analysis, comparison, filtering, export, and rule creation/listing. Missing update/delete for rules but this is a minor gap.
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