What can mcp-pre-commit do?

git-repository-state-inspection, pre-commit-hook-execution-and-management, staged-file-filtering-and-targeting, hook-configuration-parsing-and-inspection, hook-failure-diagnosis-and-reporting, mcp-tool-schema-generation-for-git-operations, commit-message-context-extraction

mcp-pre-commit

MCP ServerFree

MCP tool server for managing git repositories and pre-commit hooks

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

git-repository-state-inspection

Medium confidence

Inspects and reports the current state of git repositories including staged/unstaged changes, branch information, commit history, and file status. Works by executing git commands (git status, git log, git diff) through the MCP tool interface and parsing their output into structured data that LLM clients can consume and reason about.

Solves for

I need to understand what files have changed in my repository before running pre-commit hooksI want to check the current branch and recent commits to inform my development decisionsI need to see which files are staged vs unstaged to understand my working directory state

Best for

LLM-powered code review agents

AI assistants integrated into development workflows

Automated CI/CD systems that need repository context

Requires

git 2.0+

Node.js 14+

MCP client compatible with tool-calling protocol

Limitations

Requires a valid git repository initialized in the target directory

Performance degrades on repositories with very large histories (>10k commits) due to git log parsing

Cannot inspect submodules or worktrees without additional configuration

What makes it unique

Exposes git repository state as MCP tools that LLM clients can call directly, enabling AI agents to make context-aware decisions about code changes without requiring shell access or custom git parsing logic

vs alternatives

More lightweight than full git libraries (libgit2) while providing richer semantic information than raw shell command execution, specifically optimized for LLM reasoning about repository state

pre-commit-hook-execution-and-management

Medium confidence

Manages and executes pre-commit hooks defined in .pre-commit-config.yaml files through MCP tool calls. Parses hook configurations, resolves hook dependencies, executes hooks against staged files, and reports pass/fail status with detailed output. Integrates with the pre-commit framework by invoking pre-commit CLI commands and capturing structured results.

Solves for

I want to run pre-commit hooks on my staged changes to catch issues before committingI need to see which pre-commit hooks are configured and what they doI want to run specific hooks (e.g., only linters, not formatters) without running the full suite

Best for

Development teams using pre-commit framework for code quality gates

LLM agents that need to validate code changes before suggesting commits

CI/CD pipelines that want to delegate hook execution to MCP servers

Requires

pre-commit Python package (pip install pre-commit)

Python 3.6+

.pre-commit-config.yaml in repository root

Limitations

Requires .pre-commit-config.yaml file to exist in repository root

Hook execution time depends on hook complexity; some hooks (e.g., type checkers) may timeout on large codebases

Cannot modify hook behavior or skip hooks programmatically — only executes as configured

What makes it unique

Wraps the pre-commit framework as MCP tools, allowing LLM clients to trigger and inspect hook execution without direct shell access, while preserving the full pre-commit ecosystem (100+ community hooks) and configuration semantics

vs alternatives

Broader hook ecosystem than custom linting integrations (supports any pre-commit hook), while maintaining simpler deployment than running pre-commit as a separate service or CI stage

staged-file-filtering-and-targeting

Medium confidence

Identifies and filters staged files in a git repository by file type, path pattern, or hook scope. Uses git ls-files --cached and git diff --cached to determine which files are staged, then applies pattern matching (glob, regex, or file extension filters) to target specific subsets. Enables selective hook execution and analysis on only the files that changed.

Solves for

I want to run hooks only on Python files I've staged, not on all staged filesI need to identify which files in a specific directory have been modified and stagedI want to exclude certain file patterns (e.g., generated code, migrations) from hook execution

Best for

Monorepo environments where different file types need different hooks

Teams with selective code quality enforcement (e.g., strict rules for core, lenient for tests)

LLM agents that need to understand which files are in scope for a given operation

Requires

git 2.0+

valid git repository

Limitations

Pattern matching is limited to file paths and extensions; cannot filter by file content or semantic properties

Performance degrades with very large staged changesets (>1000 files)

Requires git index to be up-to-date; doesn't detect unstaged changes

What makes it unique

Provides MCP-native file filtering that respects git staging semantics, allowing LLM clients to reason about which files are in scope for operations without implementing git index parsing themselves

vs alternatives

More precise than running hooks on all repository files, while simpler than custom pre-commit hook implementations that would need to replicate this filtering logic

hook-configuration-parsing-and-inspection

Medium confidence

Parses .pre-commit-config.yaml files and exposes hook metadata (hook id, language, entry point, stages, files pattern, exclude pattern) as queryable MCP tool results. Uses YAML parsing to extract configuration and normalizes it into a structured format that LLM clients can inspect and reason about without needing to understand YAML syntax or pre-commit configuration semantics.

Solves for

I want to see what hooks are configured in my project and what they doI need to understand which files each hook targets based on its configurationI want to check if a specific hook (e.g., 'black' formatter) is enabled in my project

Best for

LLM agents that need to understand project code quality policies

Onboarding tools that explain pre-commit setup to new developers

Configuration audit tools that verify hook consistency across teams

Requires

.pre-commit-config.yaml file in repository root

YAML parser (typically built into Node.js MCP runtime)

Limitations

Only reads static configuration; cannot detect runtime hook behavior or custom hook implementations

Does not validate hook configuration correctness (e.g., missing dependencies, invalid patterns)

Cannot inspect hooks defined in additional_dependencies or custom_hooks without executing them

What makes it unique

Exposes pre-commit configuration as queryable MCP data structures, allowing LLM clients to reason about code quality policies without parsing YAML or understanding pre-commit semantics

vs alternatives

Simpler than loading the full pre-commit framework just to inspect configuration, while providing richer semantic information than raw YAML parsing

hook-failure-diagnosis-and-reporting

Medium confidence

Captures and structures hook execution failures, including error messages, exit codes, and affected files. Parses hook output (stdout/stderr) to extract actionable error information and formats it for LLM consumption. Distinguishes between different failure modes (syntax errors, type errors, formatting issues) based on hook type and output patterns.

Solves for

I want to understand why a hook failed and what I need to fixI need to see which files caused a hook to failI want to get structured error information that an LLM can use to suggest fixes

Best for

LLM-powered code fix agents that need to understand and remediate hook failures

Developer tools that provide intelligent error explanations

CI/CD systems that need to report hook failures in a structured format

Requires

hook execution to have completed (failed or passed)

access to hook stdout/stderr output

Limitations

Error parsing is heuristic-based and may not capture all failure modes, especially for custom hooks

Cannot provide fixes for all hook types; some hooks (e.g., security scanners) require manual review

Failure diagnosis depends on hook output format; poorly formatted hook output may not be parsed correctly

What makes it unique

Transforms unstructured hook output into LLM-consumable failure reports with semantic understanding of different hook failure modes, enabling AI agents to reason about and fix code quality issues

vs alternatives

More actionable than raw hook output, while more general-purpose than hook-specific error handlers that would need to be implemented for each hook type

mcp-tool-schema-generation-for-git-operations

Medium confidence

Generates and exposes MCP tool schemas that define the interface for git and pre-commit operations. Implements the MCP tool protocol by defining tool names, descriptions, input schemas (JSON Schema), and output formats. Allows MCP clients to discover available operations and understand their parameters without hardcoding tool knowledge.

Solves for

I want to discover what git and pre-commit operations are available through this MCP serverI need to understand the parameters and return types for each operationI want to validate my tool calls against the schema before executing them

Best for

MCP client implementations that need to dynamically discover server capabilities

LLM agents that use tool calling with schema validation

Development tools that provide autocomplete or documentation for MCP operations

Requires

MCP client that supports tool schema discovery

JSON Schema validator (typically built into MCP runtime)

Limitations

Schema generation is static and reflects only the operations implemented by this server

Cannot express complex conditional logic or dependencies between parameters in JSON Schema

Schema updates require server restart; cannot dynamically add new tools at runtime

What makes it unique

Implements the MCP tool protocol to expose git and pre-commit operations as discoverable, schema-validated tools, enabling LLM clients to use these operations with type safety and without hardcoding tool knowledge

vs alternatives

More structured than raw function calling, while more flexible than pre-defined tool sets that cannot be extended or customized

commit-message-context-extraction

Medium confidence

Extracts contextual information from recent commits (commit messages, authors, timestamps, changed files) to provide LLM agents with repository history context. Parses git log output and structures commit metadata into a format suitable for LLM reasoning about code changes and development patterns. Enables agents to understand the intent and scope of recent work.

Solves for

I want to understand the context of recent changes to inform my code review or suggestionsI need to see what files were changed in recent commits to understand the scope of workI want to extract commit messages to understand developer intent and reasoning

Best for

LLM-powered code review agents that need historical context

Commit message suggestion tools that learn from project history

Development analytics tools that analyze commit patterns

Requires

git 2.0+

valid git repository with commit history

Limitations

Limited to commit metadata; cannot analyze commit diffs or code changes without additional git operations

Performance degrades on repositories with very large histories (>10k commits)

Commit message quality varies; poorly written messages provide limited context

What makes it unique

Structures git commit history as queryable context for LLM agents, enabling AI systems to reason about code changes and development intent without requiring developers to manually provide historical context

vs alternatives

More lightweight than full code archaeology tools, while providing richer semantic information than raw git log output

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with mcp-pre-commit, ranked by overlap. Discovered automatically through the match graph.

Extension52

GitDoc

Automatically commit/push/pull changes on save, so you can edit a Git repo like a multi-file, versioned document.

error-gated-commit-preventionautomatic-commit-on-file-saveselective-activation-by-file-pattern

3 shared capabilities

MCP Server24

AgentQL

** - Enable AI agents to get structured data from unstructured web with [AgentQL](https://www.agentql.com/).

pre-commit hook enforcement for code qualityci/cd pipeline integration and automated testing

2 shared capabilities

MCP Server46

Git MCP Server

Manage local Git repositories, commits, and branches via MCP.

repository status inspection with staged/unstaged diff detectionfile staging and unstaging with content validation

2 shared capabilities

MCP Server35

pal-mcp-server

The power of Claude Code / GeminiCLI / CodexCLI + [Gemini / OpenAI / OpenRouter / Azure / Grok / Ollama / Custom Model / All Of The Above] working as one.

pre-commit hook integration for automated code quality checks

1 shared capability

MCP Server37

Boucle-framework

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

git repository safety enforcement (git-safe and branch-guard hooks)

1 shared capability

Extension31

VSCode SVN - AI智能版本控制

🚀 全平台SVN智能插件：基于原生命令行工具，支持Windows/macOS/Linux，内置AI提交日志生成，可视化差异对比，100%开源透明。无需TortoiseSVN，轻量级高性能！

file-level and directory-level commit staging with filtering

1 shared capability

Best For

✓LLM-powered code review agents
✓AI assistants integrated into development workflows
✓Automated CI/CD systems that need repository context
✓Development teams using pre-commit framework for code quality gates
✓LLM agents that need to validate code changes before suggesting commits
✓CI/CD pipelines that want to delegate hook execution to MCP servers
✓Monorepo environments where different file types need different hooks
✓Teams with selective code quality enforcement (e.g., strict rules for core, lenient for tests)

Known Limitations

⚠Requires a valid git repository initialized in the target directory
⚠Performance degrades on repositories with very large histories (>10k commits) due to git log parsing
⚠Cannot inspect submodules or worktrees without additional configuration
⚠Requires .pre-commit-config.yaml file to exist in repository root
⚠Hook execution time depends on hook complexity; some hooks (e.g., type checkers) may timeout on large codebases
⚠Cannot modify hook behavior or skip hooks programmatically — only executes as configured

Requirements

git 2.0+Node.js 14+MCP client compatible with tool-calling protocolpre-commit Python package (pip install pre-commit)Python 3.6+.pre-commit-config.yaml in repository rootvalid git repository.pre-commit-config.yaml file in repository root

Input / Output

Accepts: repository path (string), optional git command flags (string), optional hook filter (string, e.g., 'linting' or specific hook id), optional file paths to run hooks against (array of strings), filter pattern (string, glob or regex), optional file type filter (string, e.g., '.py', '.js'), hook execution result object (structured data), hook configuration metadata, none (schema is generated by server), optional number of commits to retrieve (integer, default 10), optional branch name (string, default 'HEAD')

Produces: structured JSON with file status, commit metadata, diff summaries, hook execution results (pass/fail per hook), detailed error messages and diffs, structured JSON with hook metadata and output, array of staged file paths (strings), file metadata (size, type, change type), structured JSON array of hook configurations, hook metadata (id, language, entry, stages, files, exclude, additional_dependencies), structured failure report (JSON), affected files list, error message summaries, suggested remediation steps (when available), MCP tool schema (JSON Schema format), tool metadata (name, description, input/output types), structured array of commit metadata, commit messages (strings), author information, changed files per commit

UnfragileRank

Adoption15%(30% weight)

Quality16%(25% weight)

Ecosystem50%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

7 capabilities

Visit mcp-pre-commit→

Package Details

npm

Registry

1.0.0

Version

Weekly Downloads

About

MCP tool server for managing git repositories and pre-commit hooks

Alternatives to mcp-pre-commit

IntelliCode50Extension

AI-assisted development

Compare →

GitHub Copilot Chat53Extension

AI chat features powered by Copilot

Compare →

GitHub Copilot52Extension

Your AI pair programmer

Compare →

Claude Code for VS Code52Extension

Claude Code for VS Code: Harness the power of Claude Code without leaving your IDE

Compare →

Are you the builder of mcp-pre-commit?

Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.

Claim this artifact →Verification via email

Get the weekly brief

New tools, rising stars, and what's actually worth your time. No spam.

Data Sources

mcp registry

Looking for something else?

Search →

Capabilities7 decomposed

git-repository-state-inspection

Medium confidence

Solves for

Best for

LLM-powered code review agents

AI assistants integrated into development workflows

Automated CI/CD systems that need repository context

Requires

git 2.0+

Node.js 14+

MCP client compatible with tool-calling protocol

Limitations

Requires a valid git repository initialized in the target directory

Performance degrades on repositories with very large histories (>10k commits) due to git log parsing

Cannot inspect submodules or worktrees without additional configuration

What makes it unique

vs alternatives

More lightweight than full git libraries (libgit2) while providing richer semantic information than raw shell command execution, specifically optimized for LLM reasoning about repository state

pre-commit-hook-execution-and-management

Medium confidence

Solves for

Best for

Development teams using pre-commit framework for code quality gates

LLM agents that need to validate code changes before suggesting commits

CI/CD pipelines that want to delegate hook execution to MCP servers

Requires

pre-commit Python package (pip install pre-commit)

Python 3.6+

.pre-commit-config.yaml in repository root

Limitations

Requires .pre-commit-config.yaml file to exist in repository root

Hook execution time depends on hook complexity; some hooks (e.g., type checkers) may timeout on large codebases

Cannot modify hook behavior or skip hooks programmatically — only executes as configured

What makes it unique

vs alternatives

Broader hook ecosystem than custom linting integrations (supports any pre-commit hook), while maintaining simpler deployment than running pre-commit as a separate service or CI stage

staged-file-filtering-and-targeting

Medium confidence

Solves for

Best for

Monorepo environments where different file types need different hooks

Teams with selective code quality enforcement (e.g., strict rules for core, lenient for tests)

LLM agents that need to understand which files are in scope for a given operation

Requires

git 2.0+

valid git repository

Limitations

Pattern matching is limited to file paths and extensions; cannot filter by file content or semantic properties

Performance degrades with very large staged changesets (>1000 files)

Requires git index to be up-to-date; doesn't detect unstaged changes

What makes it unique

Provides MCP-native file filtering that respects git staging semantics, allowing LLM clients to reason about which files are in scope for operations without implementing git index parsing themselves

vs alternatives

More precise than running hooks on all repository files, while simpler than custom pre-commit hook implementations that would need to replicate this filtering logic

hook-configuration-parsing-and-inspection

Medium confidence

Solves for

Best for

LLM agents that need to understand project code quality policies

Onboarding tools that explain pre-commit setup to new developers

Configuration audit tools that verify hook consistency across teams

Requires

.pre-commit-config.yaml file in repository root

YAML parser (typically built into Node.js MCP runtime)

Limitations

Only reads static configuration; cannot detect runtime hook behavior or custom hook implementations

Does not validate hook configuration correctness (e.g., missing dependencies, invalid patterns)

Cannot inspect hooks defined in additional_dependencies or custom_hooks without executing them

What makes it unique

Exposes pre-commit configuration as queryable MCP data structures, allowing LLM clients to reason about code quality policies without parsing YAML or understanding pre-commit semantics

vs alternatives

Simpler than loading the full pre-commit framework just to inspect configuration, while providing richer semantic information than raw YAML parsing

hook-failure-diagnosis-and-reporting

Medium confidence

Solves for

I want to understand why a hook failed and what I need to fixI need to see which files caused a hook to failI want to get structured error information that an LLM can use to suggest fixes

Best for

LLM-powered code fix agents that need to understand and remediate hook failures

Developer tools that provide intelligent error explanations

CI/CD systems that need to report hook failures in a structured format

Requires

hook execution to have completed (failed or passed)

access to hook stdout/stderr output

Limitations

Error parsing is heuristic-based and may not capture all failure modes, especially for custom hooks

Cannot provide fixes for all hook types; some hooks (e.g., security scanners) require manual review

Failure diagnosis depends on hook output format; poorly formatted hook output may not be parsed correctly

What makes it unique

Transforms unstructured hook output into LLM-consumable failure reports with semantic understanding of different hook failure modes, enabling AI agents to reason about and fix code quality issues

vs alternatives

More actionable than raw hook output, while more general-purpose than hook-specific error handlers that would need to be implemented for each hook type

mcp-tool-schema-generation-for-git-operations

Medium confidence

Solves for

Best for

MCP client implementations that need to dynamically discover server capabilities

LLM agents that use tool calling with schema validation

Development tools that provide autocomplete or documentation for MCP operations

Requires

MCP client that supports tool schema discovery

JSON Schema validator (typically built into MCP runtime)

Limitations

Schema generation is static and reflects only the operations implemented by this server

Cannot express complex conditional logic or dependencies between parameters in JSON Schema

Schema updates require server restart; cannot dynamically add new tools at runtime

What makes it unique

vs alternatives

More structured than raw function calling, while more flexible than pre-defined tool sets that cannot be extended or customized

commit-message-context-extraction

Medium confidence

Solves for

Best for

LLM-powered code review agents that need historical context

Commit message suggestion tools that learn from project history

Development analytics tools that analyze commit patterns

Requires

git 2.0+

valid git repository with commit history

Limitations

Limited to commit metadata; cannot analyze commit diffs or code changes without additional git operations

Performance degrades on repositories with very large histories (>10k commits)

Commit message quality varies; poorly written messages provide limited context

What makes it unique

vs alternatives

More lightweight than full code archaeology tools, while providing richer semantic information than raw git log output

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Alternatives to mcp-pre-commit

IntelliCode50Extension

AI-assisted development

Compare →

GitHub Copilot Chat53Extension

AI chat features powered by Copilot

Compare →

GitHub Copilot52Extension

Your AI pair programmer

Compare →

Claude Code for VS Code52Extension

Claude Code for VS Code: Harness the power of Claude Code without leaving your IDE

Compare →

mcp-pre-commit

Capabilities7 decomposed

git-repository-state-inspection

pre-commit-hook-execution-and-management

staged-file-filtering-and-targeting

hook-configuration-parsing-and-inspection

hook-failure-diagnosis-and-reporting

mcp-tool-schema-generation-for-git-operations

commit-message-context-extraction

Related Artifactssharing capabilities

GitDoc

AgentQL

Git MCP Server

pal-mcp-server

Boucle-framework

VSCode SVN - AI智能版本控制

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to mcp-pre-commit

Are you the builder of mcp-pre-commit?

Get the weekly brief

Data Sources

mcp-pre-commit

Capabilities7 decomposed

git-repository-state-inspection

pre-commit-hook-execution-and-management

staged-file-filtering-and-targeting

hook-configuration-parsing-and-inspection

hook-failure-diagnosis-and-reporting

mcp-tool-schema-generation-for-git-operations

commit-message-context-extraction

Related Artifactssharing capabilities

GitDoc

AgentQL

Git MCP Server

pal-mcp-server

Boucle-framework

VSCode SVN - AI智能版本控制

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to mcp-pre-commit

Are you the builder of mcp-pre-commit?

Get the weekly brief

Data Sources