The Zig MCP Server enhances AI capabilities with specialized Zig language tooling:
Code Optimization: Analyze and optimize Zig code across all optimization levels (Debug, ReleaseSafe, ReleaseFast, ReleaseSmall)
Compute Units Estimation: Estimate computational complexity, memory usage, and resource requirements
Code Generation: Generate Zig code from natural language descriptions with error handling, testing, and performance optimizations
Code Recommendations: Provide improvement suggestions for style, design patterns, safety, and performance
Access Resources: Serve official Zig documentation, standard library reference, and examples from popular GitHub repositories
Uses GitHub's API to access popular repositories, requiring a GitHub token for better API rate limits
The server itself is written in TypeScript, and the examples showing how to use the MCP tools are demonstrated using TypeScript syntax
Provides Zig language tooling, code analysis, and documentation access, including code optimization, compute unit estimation, code generation, and best practices recommendations
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., "@Zig MCP Server optimize this fibonacci function for ReleaseFast mode"
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.
Modern Zig AI 10x dev assistant with comprehensive build system support
A powerful Model Context Protocol (MCP) server that provides comprehensive Zig language assistance, including modern build system support, code optimization, and best practices guidance.
🏗️ Zig 0.15.2+ Support: Fully updated with modern b.path() and root_module patterns
📦 Enhanced Module System: Support for latest module system with root_module.addImport()
🔄 Migration Guidance: Automated detection and upgrade suggestions for legacy patterns
🔧 Enhanced Code Analysis: Improved optimization suggestions and modern pattern detection
🧪 Comprehensive Testing: 85+ test cases with full coverage reporting
⚡ Better Code Quality: Fixed all TypeScript compilation errors and linting issues
📚 Extended Documentation: Complete Zig 0.15.2+ build system guide with migration tips
Related MCP server: zig-mcp
generate_build_zig)Generate modern build.zig files with Zig 0.15.2+ patterns:
Cross-compilation support with latest target options
Modern dependency management with build.zig.zon
Test and documentation integration
Enhanced module system support
analyze_build_zig)Analyze existing build files and get modernization recommendations:
Detect deprecated patterns
Suggest Zig 0.15.2+ alternatives
Identify missing best practices
Module system migration guidance
generate_build_zon)Generate build.zig.zon files for modern package management:
Popular Zig packages catalog
Version management guidance
Best practices documentation
optimize_code)Enhanced with modern Zig patterns and build mode analysis:
Debug, ReleaseSafe, ReleaseFast, ReleaseSmall
Modern optimization suggestions
Zig 0.12+ pattern recommendations
// Example usage
{
"code": "const std = @import(\"std\");\n...",
"optimizationLevel": "ReleaseFast"
}estimate_compute_units)Estimates computational complexity and resource usage of Zig code:
Memory usage analysis
Time complexity estimation
Allocation patterns detection
// Example usage
{
"code": "const std = @import(\"std\");\n..."
}generate_code)Generates Zig code from natural language descriptions with support for:
Error handling
Testing
Performance optimizations
Documentation
// Example usage
{
"prompt": "Create a function that sorts an array of integers",
"context": "Should handle empty arrays and use comptime when possible"
}get_recommendations)Provides code improvement recommendations and best practices:
Style and conventions
Design patterns
Safety considerations
Performance insights
// Example usage
{
"code": "const std = @import(\"std\");\n...",
"prompt": "Improve performance and safety"
}Language Reference (zig://docs/language-reference)
Official Zig language documentation
Syntax and features guide
Best practices
Standard Library Documentation (zig://docs/std-lib)
Complete std library reference
Function signatures and usage
Examples and notes
Popular Repositories (zig://repos/popular)
Top Zig projects on GitHub
Community examples and patterns
Real-world implementations
To install Zig MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install zig-mcp-server --client claudeClone the repository:
git clone [repository-url]
cd zig-mcp-serverInstall dependencies:
npm installBuild the server:
npm run buildConfigure environment variables:
# Create a GitHub token for better API rate limits
# https://github.com/settings/tokens
# Required scope: public_repo
GITHUB_TOKEN=your_token_hereAdd to MCP settings:
{
"mcpServers": {
"zig": {
"command": "node",
"args": ["/path/to/zig-mcp-server/build/index.js"],
"env": {
"GITHUB_TOKEN": "your_token_here",
"NODE_OPTIONS": "--experimental-vm-modules"
},
"restart": true
}
}
}const result = await useMcpTool("zig", "optimize_code", {
code: `
pub fn fibonacci(n: u64) u64 {
if (n <= 1) return n;
return fibonacci(n - 1) + fibonacci(n - 2);
}
`,
optimizationLevel: "ReleaseFast"
});const result = await useMcpTool("zig", "estimate_compute_units", {
code: `
pub fn bubbleSort(arr: []i32) void {
var i: usize = 0;
while (i < arr.len) : (i += 1) {
var j: usize = 0;
while (j < arr.len - 1) : (j += 1) {
if (arr[j] > arr[j + 1]) {
const temp = arr[j];
arr[j] = arr[j + 1];
arr[j + 1] = temp;
}
}
}
}
`
});const result = await useMcpTool("zig", "generate_code", {
prompt: "Create a thread-safe counter struct",
context: "Should use atomic operations and handle overflow"
});const result = await useMcpTool("zig", "get_recommendations", {
code: `
pub fn main() !void {
var list = std.ArrayList(u8).init(allocator);
var i: u32 = 0;
while (true) {
if (i >= 100) break;
try list.append(@intCast(i));
i += 1;
}
}
`,
prompt: "performance"
});zig-mcp-server/
├── src/
│ └── index.ts # Main server implementation
├── build/ # Compiled JavaScript
├── package.json # Dependencies and scripts
└── tsconfig.json # TypeScript configuration# Development build with watch mode
npm run watch
# Production build
npm run buildnpm testFork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request
MIT License - see the LICENSE file for details.
Analyze a build.zig file and provide modernization recommendations
| Name | Required | Description | Default |
|---|---|---|---|
| buildZigContent | Yes | Content of the build.zig file to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It states the tool analyzes and provides recommendations, but does not clarify if it is read-only, modifies files, or requires authentication. The behavior is left ambiguous.
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 concise sentence that efficiently communicates the core function. No filler or redundancy. It is front-loaded with the verb and resource.
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 lack of an output schema and annotations, the description is insufficient. It does not specify the format or scope of recommendations, nor any side effects. The tool's complexity is low, but completeness is lacking.
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% for the single parameter. The tool description adds no extra meaning beyond what the schema already provides. Baseline of 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 identifies the tool as analyzing a build.zig file and providing modernization recommendations. It uses a specific verb-resource pair and distinguishes itself from sibling tools like generate_build_zig or optimize_code.
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 usage guidelines are provided. The description does not specify when to use this tool over alternatives, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate computational complexity and resource usage with detailed analysis
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Zig code to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden but only states 'with detailed analysis', failing to disclose behavior like side effects, safety, or cost implications.
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, no redundancy, immediately conveys purpose. Highly efficient.
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 low complexity (1 param, no output schema), the description is adequate but lacks details about output format or granularity, which would aid full 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?
Schema coverage is 100% for the single parameter 'code'. The description adds broad context ('computational complexity') but doesn't enhance parameter meaning beyond the schema's description.
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 a specific action: estimating computational complexity and resource usage. It distinguishes from siblings (e.g., generate, analyze, optimize) by focusing on estimation with detailed analysis.
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. No context about prerequisites or exclusions, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a modern build.zig file with best practices
| Name | Required | Description | Default |
|---|---|---|---|
| projectName | No | Name of the project | my-project |
| projectType | No | Type of project to generate | executable |
| zigVersion | No | Target Zig version | 0.15.2 |
| dependencies | No | List of dependencies to include |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the tool overwrites files, returns content, or requires permissions. The verb 'generate' implies creation but details are missing.
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 that states the purpose, but it is too brief for a tool with four parameters and no output schema. It earns its place but lacks necessary detail.
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 no annotations, the description fails to explain return values, side effects, or behavioral aspects. Essential context for an agent to invoke the tool correctly is missing.
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 input schema covers 100% of parameters with descriptions, so the description adds no additional meaning beyond what is already in the schema. Baseline score of 3 applies.
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 generates a 'modern build.zig file', which is specific and distinguishes it from siblings like generate_build_zon (for .zon files) and analyze_build_zig (analysis).
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 (e.g., generate_build_zon) or any prerequisites, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a build.zig.zon file for dependency management
| Name | Required | Description | Default |
|---|---|---|---|
| projectName | No | Name of the project | my-project |
| dependencies | No | List of dependencies with their URLs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only states what the tool generates. It does not disclose whether it creates or overwrites files, if it requires any permissions, or what side effects occur. The description fails to compensate for the lack of annotations.
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, concise sentence that clearly communicates the tool's purpose. No unnecessary words or repetition.
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 low complexity (2 parameters, no nested objects, no output schema), the description adequately states the purpose but lacks behavioral context such as whether a file is created or overwritten, or the return value.
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%, so the schema already documents both parameters (projectName, dependencies). The description adds no extra meaning beyond the schema; baseline score of 3 applies.
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 action (Generate) and the resource (build.zig.zon file for dependency management). It distinguishes itself from sibling tools like generate_build_zig, which generates a different file type (build.zig).
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 implies usage for dependency management but does not explicitly state when to use this tool vs alternatives like generate_build_zig. No when-not conditions or alternative tool names are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate modern Zig code from natural language descriptions
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Natural language description of desired code | |
| context | No | Additional context or requirements |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden, but it only states the generative action. It fails to disclose side effects, idempotency, error behavior, or safety implications, which is essential for a code generation tool.
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, concise sentence with no filler. It is front-loaded with the key action and resource. However, it could be restructured to include brief usage context without losing conciseness.
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 code generation tool with no output schema, the description is insufficient. It does not explain the return format, code quality, or limitations, leaving the agent without critical information for safe 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?
The input schema covers both parameters with descriptions, achieving 100% coverage. The tool description does not add any extra meaning beyond what the schema states, so it meets the baseline but provides no additional semantic value.
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 identifies the tool's primary function: generating modern Zig code from natural language. The verb 'Generate' and resource 'Zig code' are specific, and the source 'natural language descriptions' distinguishes it from sibling tools that analyze, estimate, or optimize code.
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 about when to use this tool versus alternatives. It does not mention prerequisites, limitations, or typical use cases, leaving the agent to infer context from sibling names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive, multi-dimensional code analysis with 10+ specialized analyzers covering style, safety, performance, concurrency, metaprogramming, testing, build systems, interop, metrics, and modern Zig patterns
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Zig code to analyze | |
| prompt | No | Natural language query for specific recommendations (performance, safety, maintainability, concurrency, architecture, etc.) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It lists analyzers but does not disclose whether the tool is read-only, has side effects, requires authentication, or has usage limits. This leaves ambiguity for the agent.
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 that is informative and front-loaded with the main purpose. While it lists many analyzers, it remains relatively concise and avoids unnecessary verbosity.
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 of the tool with many analyzers and no output schema, the description should explain the output format or expected results. It only describes input but not output, leaving the agent uncertain about what to expect.
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 descriptions for both parameters. The description adds context about the analyzers but does not enhance parameter semantics beyond the schema. Baseline score of 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 tool provides comprehensive, multi-dimensional code analysis with 10+ specialized analyzers, covering a wide range of aspects. It distinguishes from sibling tools like optimize_code or generate_code, which focus on different tasks.
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 implies usage for getting code recommendations but does not explicitly contrast with siblings or specify when not to use. It mentions a natural language query parameter, hinting at flexible queries, but lacks direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Optimize Zig code for better performance with modern patterns
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Zig code to optimize | |
| optimizationLevel | No | Optimization level to target | ReleaseSafe |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only mentions the purpose. It doesn't state whether the tool returns optimized code, modifies input, has side effects, or requires valid code. Critical gaps for a transformation tool.
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. However, it lacks front-loading of critical information and structure, but is appropriately sized for the tool's simplicity.
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 no annotations, the description should explain what the tool returns (e.g., optimized code, error messages) and any constraints. It fails to provide sufficient context 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?
Schema description coverage is 100%, so the baseline is 3. The description adds no extra meaning beyond what the schema already provides for the two parameters.
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 action ('Optimize') and resource ('Zig code') with a specific goal ('better performance with modern patterns'). It effectively distinguishes from siblings like 'generate_code' or 'analyze_build_zig'.
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., when to optimize vs generate code). The description lacks context for selecting it over siblings.
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. Dates show when Glama detected each change.
v1.0.1analyze_build_ziggenerate_build_ziggenerate_build_zonget_recommendations1 field changedInput schema / properties / prompt / descriptionPrevious value: -"Natural language query for specific recommendations"New value: +"Natural language query for specific recommendations (performance, safety, maintainability, concurrency, architecture, etc.)"optimize_code1 field changedInput schema / properties / optimizationLevel / defaultAdded value: +"ReleaseSafe"v1.0.0estimate_compute_unitsgenerate_codeget_recommendationsoptimize_codeMost tools are clearly distinct, but estimate_compute_units may overlap with the performance analysis included in get_recommendations, introducing slight ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., analyze_build_zig, generate_code), making them predictable and easy to understand.
With 7 tools covering code generation, build management, analysis, and optimization, the server is well-scoped for its purpose without being too few or excessive.
The tool set covers core Zig development tasks, but lacks explicit tools for test generation or documentation, which are minor gaps given the comprehensive get_recommendations tool.
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