PyGithub을 통해 GitHub API와 상호작용하는 도구를 제공하는 모델 컨텍스트 프로토콜 서버입니다. 이 서버를 통해 AI 어시스턴트는 이슈, 저장소, 풀 리퀘스트 관리와 같은 GitHub 작업을 수행할 수 있습니다.
모듈식 도구 아키텍처:
활성화/비활성화할 수 있는 구성 가능한 도구 그룹
도메인별 조직(이슈, 저장소 등)
파일 또는 환경 변수를 통한 유연한 구성
모듈식 설계로 관심사를 명확하게 분리
일관된 패턴으로 쉽게 확장 가능
완벽한 GitHub 이슈 관리:
문제 생성 및 업데이트
문제 세부 정보를 얻고 저장소 문제를 나열합니다.
댓글 추가, 나열, 업데이트 및 삭제
이슈 라벨 관리
담당자 및 이정표 처리
스마트 매개변수 처리:
선택적 매개변수에 대한 동적 kwargs 빌드
GitHub 객체에 대한 적절한 유형 변환
모든 입력 매개변수에 대한 검증
잘못된 입력에 대한 오류 메시지 지우기
강력한 구현:
PyGithub을 통한 객체 지향 GitHub API 상호 작용
중앙화된 GitHub 클라이언트 관리
적절한 오류 처리 및 속도 제한
MCP 도구를 통한 깔끔한 API 추상화
포괄적인 페이지 매김 지원
디버깅을 위한 자세한 로깅
Related MCP server: GitHub MCP Server
포괄적인 가이드는 docs/guides 디렉토리에서 확인할 수 있습니다.
error-handling.md: 오류 유형, 처리 패턴 및 모범 사례
security.md: 인증, 액세스 제어 및 콘텐츠 보안
tool-reference.md: 예제를 포함한 자세한 도구 설명서
PyGithub MCP 서버 사용에 대한 자세한 내용은 이 가이드를 참조하세요.
문제 생성
지엑스피1
문제 세부 정보 가져오기
{
"owner": "username",
"repo": "repository",
"issue_number": 1
}문제 업데이트
{
"owner": "username",
"repo": "repository",
"issue_number": 1,
"title": "Updated Title",
"body": "Updated description",
"state": "closed",
"labels": ["bug", "wontfix"]
}댓글 추가
{
"owner": "username",
"repo": "repository",
"issue_number": 1,
"body": "This is a comment"
}목록 코멘트
{
"owner": "username",
"repo": "repository",
"issue_number": 1,
"per_page": 10
}댓글 업데이트
{
"owner": "username",
"repo": "repository",
"issue_number": 1,
"comment_id": 123456789,
"body": "Updated comment text"
}라벨 추가
{
"owner": "username",
"repo": "repository",
"issue_number": 1,
"labels": ["enhancement", "help wanted"]
}라벨 제거
{
"owner": "username",
"repo": "repository",
"issue_number": 1,
"label": "enhancement"
}모든 작업은 선택적 매개변수를 지능적으로 처리합니다.
API 호출에는 제공된 매개변수만 포함됩니다.
기본 유형을 GitHub 객체로 변환합니다(예: 마일스톤 번호를 마일스톤 객체로 변환).
잘못된 매개변수에 대한 명확한 오류 메시지를 제공합니다.
해당되는 경우 자동으로 페이지 매김을 처리합니다.
가상 환경을 만들고 활성화하세요.
uv venv
source .venv/bin/activate종속성 설치:
uv pip install -e .MCP 설정에 서버를 추가합니다(예: claude_desktop_config.json 또는 cline_mcp_settings.json ):
{
"mcpServers": {
"github": {
"command": "/path/to/repo/.venv/bin/python",
"args": ["-m", "pygithub_mcp_server"],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "your-token-here"
}
}
}
}서버는 구성을 통해 도구 그룹을 선택적으로 활성화하거나 비활성화할 수 있도록 지원합니다. 이는 두 가지 방법으로 구성할 수 있습니다.
JSON 구성 파일을 만듭니다(예: pygithub_mcp_config.json ):
{
"tool_groups": {
"issues": {"enabled": true},
"repositories": {"enabled": true},
"pull_requests": {"enabled": false},
"discussions": {"enabled": false},
"search": {"enabled": true}
}
}그런 다음 환경에서 이 파일을 지정하세요.
export PYGITHUB_MCP_CONFIG=/path/to/pygithub_mcp_config.json또는 환경 변수를 사용하여 도구 그룹을 구성합니다.
export PYGITHUB_ENABLE_ISSUES=true
export PYGITHUB_ENABLE_REPOSITORIES=true
export PYGITHUB_ENABLE_PULL_REQUESTS=false기본적으로 issues 도구 그룹만 활성화됩니다. 자세한 구성 옵션은 README.config.md 참조하세요.
이 프로젝트에는 포괄적인 테스트 모음이 포함되어 있습니다.
# Run all tests
pytest
# Run tests with coverage report
pytest --cov
# Run specific test file
pytest tests/test_operations/test_issues.py
# Run tests matching a pattern
pytest -k "test_create_issue"참고: 현재 많은 테스트가 실패하여 조사 중입니다. 이는 알려진 문제이며, 현재 적극적으로 해결 중입니다.
MCP Inspector를 사용하여 개발 중에 MCP 도구를 테스트하세요.
source .venv/bin/activate # Ensure venv is activated
npx @modelcontextprotocol/inspector -e GITHUB_PERSONAL_ACCESS_TOKEN=your-token-here uv run pygithub-mcp-serverMCP Inspector의 웹 UI를 사용하여 다음을 수행할 수 있습니다.
사용 가능한 도구로 실험해보세요
실제 GitHub 저장소로 테스트
성공 및 오류 사례 확인
문서 작업 페이로드
tests/
├── unit/ # Fast tests without external dependencies
│ ├── config/ # Configuration tests
│ ├── tools/ # Tool registration tests
│ └── ... # Other unit tests
└── integration/ # Tests with real GitHub API
├── issues/ # Issue tools tests
└── ... # Other integration testssrc/
└── pygithub_mcp_server/
├── __init__.py
├── __main__.py
├── server.py # Server factory (create_server)
├── version.py
├── config/ # Configuration system
│ ├── __init__.py
│ └── settings.py # Configuration management
├── tools/ # Modular tool system
│ ├── __init__.py # Tool registration framework
│ └── issues/ # Issue tools
│ ├── __init__.py
│ └── tools.py # Issue tool implementations
├── client/ # GitHub client functionality
│ ├── __init__.py
│ ├── client.py # Core GitHub client
│ └── rate_limit.py # Rate limit handling
├── converters/ # Data transformation
│ ├── __init__.py
│ ├── parameters.py # Parameter formatting
│ ├── responses.py # Response formatting
│ ├── common/ # Common converters
│ ├── issues/ # Issue-related converters
│ ├── repositories/ # Repository converters
│ └── users/ # User-related converters
├── errors/ # Error handling
│ ├── __init__.py
│ └── exceptions.py # Custom exceptions
├── operations/ # GitHub operations
│ ├── __init__.py
│ └── issues.py
├── schemas/ # Data models
│ ├── __init__.py
│ ├── base.py
│ ├── issues.py
│ └── ...
└── utils/ # General utilities
├── __init__.py
└── environment.py # Environment utilities서버가 시작되지 않습니다:
MCP 설정에서 venv Python 경로 확인
모든 요구 사항이 venv에 설치되어 있는지 확인하세요.
GITHUB_PERSONAL_ACCESS_TOKEN이 설정되고 유효한지 확인하세요.
빌드 오류:
uv build와 함께 --no-build-isolation 플래그를 사용하세요.
Python 3.10+가 사용되고 있는지 확인하세요.
모든 종속성이 설치되었는지 확인하세요
GitHub API 오류:
토큰 권한 및 유효성 확인
자세한 오류 추적을 위해 pygithub_mcp_server.log를 검토하세요.
요금 제한이 초과되지 않았는지 확인하세요
파이썬 3.10+
MCP 파이썬 SDK
피단틱
파이기트허브
UV 패키지 관리자
MIT
Add a comment to an issue.
Args:
params: Parameters for adding a comment including:
- owner: Repository owner (user or organization)
- repo: Repository name
- issue_number: Issue number to comment on
- body: Comment text
Returns:
Created comment details from GitHub API
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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 action ('Add a comment') but doesn't mention authentication requirements, rate limits, error conditions, or what 'Created comment details from GitHub API' includes. For a write operation with zero annotation coverage, this leaves significant gaps in understanding the tool's 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?
The description is well-structured with clear sections (purpose, args, returns) and uses bullet points for parameters. It's appropriately sized for the tool's complexity. The only minor inefficiency is repeating 'Parameters for adding a comment' in both the main description and the args section.
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 write operation with no annotations, no output schema, and 4 parameters, the description is incomplete. It doesn't cover authentication needs, error handling, rate limits, or what the return value contains beyond 'Created comment details from GitHub API'. The agent lacks crucial information for reliable tool 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?
Schema description coverage is 0%, so the description must compensate. It lists all four parameters (owner, repo, issue_number, body) with brief explanations that match what would be in a good schema. However, it doesn't provide additional context like format requirements (e.g., owner must be valid GitHub user/org), constraints, or examples beyond the basic definitions.
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 'Add a comment to an issue' which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'update_issue_comment' or 'delete_issue_comment' by focusing on creation rather than modification or deletion. However, it doesn't explicitly differentiate from 'list_issue_comments' which is a read operation.
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. There's no mention of prerequisites (e.g., authentication requirements), when not to use it, or how it relates to sibling tools like 'update_issue_comment' or 'create_issue'. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add labels to an issue.
Args:
params: Parameters for adding labels including:
- owner: Repository owner (user or organization)
- repo: Repository name
- issue_number: Issue number
- labels: Labels to add
Returns:
Updated list of labels from GitHub API
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions the action ('Add labels') and return value ('Updated list of labels'), but lacks details on permissions needed, error conditions (e.g., invalid labels), whether it's idempotent, or rate limits. This is inadequate for a mutation 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 well-structured with clear sections for purpose, args, and returns. It's front-loaded with the core action, and each sentence serves a purpose without redundancy. However, the 'Args' section could be more integrated into the flow rather than as a separate list.
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 mutation tool with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It covers basic purpose and parameters but lacks critical context: error handling, authentication requirements, side effects (e.g., notifications), and detailed return format beyond 'Updated list of labels'.
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 0%, but the description compensates by listing all four parameters (owner, repo, issue_number, labels) with brief explanations. However, it doesn't add meaningful semantics beyond what's inferable from parameter names (e.g., format of 'owner', what 'labels' array contains), leaving gaps in understanding parameter usage.
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 ('Add') and resource ('labels to an issue'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'remove_issue_label' by specifying the additive action, though it doesn't explicitly contrast with other label-related tools that might exist.
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. While the description implies it's for adding labels to issues, it doesn't mention prerequisites (e.g., issue must exist), when to choose this over 'update_issue' which might also handle labels, or any constraints like label availability in the repository.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new branch in a GitHub repository.
Args:
params: Dictionary with branch parameters
- owner: Repository owner (username or organization)
- repo: Repository name
- branch: Name for new branch
- from_branch: Source branch (optional, defaults to repo default)
Returns:
MCP response with branch creation result
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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 it 'creates' a new branch, implying a mutation, but doesn't cover permissions needed, whether it's idempotent, error conditions, or rate limits. The mention of 'MCP response with branch creation result' is vague and doesn't explain what the result contains.
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 well-structured with clear sections for Args and Returns, and sentences are direct without unnecessary fluff. It could be slightly more concise by integrating the parameter list more seamlessly, but overall it's 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 the complexity of a GitHub branch creation tool with no annotations, no output schema, and nested parameters, the description is incomplete. It lacks details on authentication, error handling, return values, and how it fits with sibling tools, making it insufficient for reliable agent use.
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 lists specific parameters (owner, repo, branch, from_branch) with brief explanations, which adds meaningful context beyond the input schema's generic 'params' object with 0% coverage. However, it doesn't detail data types, constraints, or examples, leaving gaps in understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Create' and resource 'new branch in a GitHub repository', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'create_repository' or 'fork_repository' beyond the different resource type, which keeps it from 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., repository access), when not to use it, or how it relates to sibling tools like 'fork_repository' or 'create_repository' for similar creation tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new issue in a GitHub repository.
Args:
params_dict: Parameters for creating an issue including:
- owner: Repository owner (user or organization)
- repo: Repository name
- title: Issue title
- body: Issue description (optional)
- assignees: List of usernames to assign
- labels: List of labels to add
- milestone: Milestone number (optional)
Returns:
Created issue details from GitHub API
| Name | Required | Description | Default |
|---|---|---|---|
| params_dict | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that this is a creation/mutation operation ('Create a new issue') and mentions the GitHub API as the backend, but lacks details on permissions required, rate limits, error handling, or what 'Created issue details' includes. It provides basic behavioral context but misses critical operational traits.
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 well-structured with clear sections (Args, Returns) and uses bullet points for parameters, making it easy to scan. It's appropriately sized with no redundant information, though the 'Args' and 'Returns' headers could be 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 no annotations, no output schema, and a complex nested parameter structure, the description is moderately complete. It covers the purpose and parameters well but lacks details on return values (beyond a vague phrase), error conditions, authentication needs, or rate limits, which are crucial for a mutation tool in this context.
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 0%, so the description must compensate. It comprehensively lists 7 parameters within 'params_dict' with clear names and optionality notes, adding significant meaning beyond the generic schema. However, it doesn't specify data formats (e.g., string types for 'owner') or constraints, leaving some ambiguity.
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 ('Create a new issue') and resource ('in a GitHub repository'), distinguishing it from siblings like 'update_issue' or 'list_issues'. It uses precise language that leaves no ambiguity about the tool's function.
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 by specifying the resource (GitHub repository) but doesn't explicitly state when to use this tool versus alternatives like 'update_issue' or 'list_issues'. No guidance is provided on prerequisites, exclusions, or comparative contexts with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create or update a file in a GitHub repository.
Args:
params: Dictionary with file parameters
- owner: Repository owner (username or organization)
- repo: Repository name
- path: Path where to create/update the file
- content: Content of the file
- message: Commit message
- branch: Branch to create/update the file in
- sha: SHA of file being replaced (for updates, optional)
Returns:
MCP response with file creation/update result
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks critical behavioral details. It mentions the action but doesn't disclose permissions needed (e.g., write access), whether updates are destructive (overwrites existing files), rate limits, or error handling. The return value is vaguely described as 'result' without format or content specifics.
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 well-structured with a purpose statement followed by 'Args' and 'Returns' sections. It's front-loaded and efficient, though the 'Returns' section could be more specific (e.g., 'MCP response with...' is slightly vague). Every sentence adds value without redundancy.
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 annotations, 0% schema coverage, no output schema, and a complex mutation tool, the description is moderately complete. It covers purpose and parameters well but lacks behavioral transparency (permissions, side effects) and output details. For a GitHub file write operation, more context on authentication and consequences would be beneficial.
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 0%, and the input schema only shows a generic 'params' object. The description compensates fully by listing all 7 specific parameters (owner, repo, path, content, message, branch, sha) with clear meanings and notes on optionality for 'sha'. This adds essential semantics beyond the bare 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 specific action ('Create or update a file') and resource ('in a GitHub repository'), distinguishing it from sibling tools like 'get_file_contents' (read-only) or 'push_files' (batch operations). The verb+resource combination is precise and unambiguous.
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 file creation/update in GitHub but provides no explicit guidance on when to use this versus alternatives like 'push_files' (for multiple files) or 'get_file_contents' (for reading). No prerequisites, exclusions, or comparative context are mentioned, leaving usage decisions to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new GitHub repository.
Args:
params: Dictionary with repository creation parameters
- name: Repository name
- description: Repository description (optional)
- private: Whether the repository should be private (optional)
- auto_init: Initialize repository with README (optional)
Returns:
MCP response with created repository details
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
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 action ('Create') but fails to mention critical details like required permissions, rate limits, or whether the operation is idempotent. This leaves significant gaps for a mutation 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 well-structured with clear sections for Args and Returns, making it easy to parse. It is front-loaded with the core purpose, though the Returns section could be more informative given the lack of output schema.
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 creating a repository (a mutation with no annotations and no output schema), the description is incomplete. It lacks details on authentication, error handling, and the structure of returned details, leaving the agent with insufficient context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It effectively documents the nested parameters (name, description, private, auto_init) within 'params', adding meaningful semantics beyond the generic schema. However, it does not specify data types or constraints.
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 ('Create') and resource ('a new GitHub repository'), distinguishing it from siblings like 'fork_repository' or 'search_repositories'. It directly answers what the tool does without ambiguity.
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 like 'fork_repository' or 'get_repository'. The description lacks context about prerequisites, such as authentication or permissions, and does not mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete an issue comment.
Args:
params: Parameters for deleting a comment including:
- owner: Repository owner (user or organization)
- repo: Repository name
- issue_number: Issue number containing the comment
- comment_id: Comment ID to delete
Returns:
Empty response on success
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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 action ('Delete') which implies a destructive mutation, but doesn't mention critical behaviors: whether deletion is permanent, what permissions are required, if it's reversible, or any rate limits. The return statement adds minimal value ('Empty response on success'), but leaves error conditions and side effects undocumented.
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 well-structured and appropriately sized. It opens with the core purpose, then provides parameter documentation in a clear format, and concludes with return information. Every sentence serves a purpose with minimal waste. The only minor improvement would be front-loading more behavioral context before parameter details.
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 destructive mutation tool with no annotations and no output schema, the description is incomplete. It documents parameters adequately but fails to address critical context: authentication requirements, permission levels needed, whether deletions are permanent, error conditions, or how this differs from updating comments. The return statement is minimal ('Empty response on success') without explaining what constitutes success or how errors manifest.
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 0%, so the description must compensate. It lists all four parameters (owner, repo, issue_number, comment_id) with brief explanations that match the schema's property descriptions. However, it doesn't add meaningful semantics beyond what's already in the schema - no examples, format requirements, or constraints. The parameter documentation is adequate but not insightful.
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 ('Delete') and resource ('an issue comment'), making the purpose immediately understandable. It distinguishes this from sibling tools like 'update_issue_comment' or 'list_issue_comments' by specifying deletion rather than modification or retrieval. However, it doesn't explicitly contrast with all siblings, such as 'remove_issue_label' which also performs deletion operations.
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 prerequisites (e.g., needing appropriate permissions), when deletion is appropriate versus updating, or how it differs from related tools like 'update_issue_comment' for modifying comments. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fork an existing GitHub repository.
Args:
params: Dictionary with fork parameters
- owner: Repository owner (username or organization)
- repo: Repository name
- organization: Organization to fork to (optional)
Returns:
MCP response with forked repository details
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
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 action ('Fork') but lacks critical details: it doesn't specify if this requires GitHub authentication, what permissions are needed, whether it's a destructive or read-only operation, or any rate limits. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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 well-structured and appropriately sized. It starts with a clear purpose statement, followed by 'Args' and 'Returns' sections. Every sentence adds value, with no wasted words. It could be slightly more concise by integrating the parameter details more seamlessly, but overall it's 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 the complexity (a mutation tool forking repositories), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain return values in detail (beyond 'forked repository details'), authentication requirements, error conditions, or how it differs from sibling tools. For a tool with significant contextual needs, this falls short.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It lists three parameters (owner, repo, organization) with brief explanations, adding meaning beyond the schema's generic 'params' object. However, it doesn't cover all aspects (e.g., data types, constraints, optional vs. required status beyond 'optional' for organization), leaving some ambiguity. This partial compensation justifies a baseline score.
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: 'Fork an existing GitHub repository.' It specifies the verb ('Fork') and resource ('GitHub repository'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'create_repository' or 'search_repositories', which is why it doesn't earn a 5.
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 prerequisites (e.g., authentication needs), when to choose forking over cloning or creating a new repository, or any exclusions. This leaves the agent without context for tool selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get contents of a file in a GitHub repository.
Args:
params: Dictionary with file parameters
- owner: Repository owner (username or organization)
- repo: Repository name
- path: Path to file/directory
- branch: Branch to get contents from (optional)
Returns:
MCP response with file content data
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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. It states the action 'Get contents' but lacks details on behavioral traits such as authentication requirements, rate limits, error handling, or whether it's read-only (implied but not confirmed). This leaves significant gaps for a tool interacting with an external API like GitHub.
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 well-structured and appropriately sized, with a clear opening sentence followed by organized 'Args' and 'Returns' sections. Every sentence adds value without redundancy, making it efficient and easy to parse.
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 a GitHub API tool with no annotations and no output schema, the description is partially complete. It covers the purpose and parameters well but lacks details on behavioral aspects, return format specifics, and usage context, leaving room for improvement in guiding an AI agent fully.
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 substantial meaning beyond the input schema, which has 0% coverage and only indicates a generic 'params' object. It explicitly lists and describes four parameters (owner, repo, path, branch), including their purposes and optionality, effectively compensating for the poor schema documentation.
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' and the resource 'contents of a file in a GitHub repository', making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'get_repository' or 'get_issue', which might retrieve different types of data from GitHub, so it misses full sibling differentiation.
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. For example, it does not mention when to choose this over 'get_repository' for file content or 'list_commits' for commit details, 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.
Get details about a specific issue.
Args:
params: Parameters for getting an issue including:
- owner: Repository owner (user or organization)
- repo: Repository name
- issue_number: Issue number to retrieve
Returns:
Issue details from GitHub API
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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. It states this is a read operation ('Get details'), which implies it's non-destructive, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or what 'details' include. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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?
The description is well-structured with clear sections (Args, Returns) and front-loaded the core purpose. It's concise with no wasted words, though the 'Returns' section is vague ('Issue details from GitHub API') and could be more informative. Overall, it's efficient but not perfectly optimized.
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 annotations, no output schema, and low schema coverage (0%), the description is incomplete. It covers the basic purpose and parameters but lacks behavioral context, return value details, and usage guidelines. For a tool interacting with an external API (GitHub), this leaves the agent under-informed about how to invoke it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It lists the three parameters (owner, repo, issue_number) with brief explanations, adding meaning beyond the bare schema. However, it doesn't provide examples, format details (e.g., GitHub username conventions), or constraints, leaving room for ambiguity. With low schema coverage, this is minimally adequate.
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 details about a specific issue.' It uses a specific verb ('Get') and resource ('issue'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'list_issues' or 'update_issue', which would be helpful for agent selection.
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 'list_issues' (for multiple issues) or 'update_issue' (for modifying issues), nor does it specify prerequisites or exclusions. The agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get details about a GitHub repository.
Args:
params: Dictionary with repository parameters
- owner: Repository owner (username or organization)
- repo: Repository name
Returns:
MCP response with repository details
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
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 retrieves repository details but fails to mention critical aspects like whether it's read-only, requires authentication, has rate limits, or what the response format entails (e.g., JSON structure, error handling). This leaves significant gaps in understanding the tool's 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?
The description is appropriately sized and front-loaded, starting with the core purpose followed by structured sections for arguments and returns. Each sentence serves a clear function, with no redundant information, though the 'Returns' section is vague ('MCP response with repository details') and could be more specific.
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 (interacting with GitHub API), lack of annotations, no output schema, and minimal schema coverage, the description is incomplete. It doesn't cover authentication needs, error cases, response structure, or usage context, making it inadequate for an agent to reliably invoke the tool without additional assumptions.
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 substantial value beyond the input schema, which has 0% coverage and only specifies a generic 'params' object. It clarifies that 'params' is a dictionary with 'owner' and 'repo' keys, providing essential semantic details that the schema lacks. However, it doesn't explain data types or constraints for these 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 purpose as 'Get details about a GitHub repository' with a specific verb ('Get') and resource ('GitHub repository'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'search_repositories' or 'get_file_contents', which also retrieve GitHub data but for different resources or with different scopes.
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. The description lacks any mention of prerequisites, context for usage, or comparisons to sibling tools such as 'search_repositories' (for broader searches) or 'get_issue' (for specific issue details), leaving the agent without direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List commits in a GitHub repository.
Args:
params: Dictionary with commit parameters
- owner: Repository owner (username or organization)
- repo: Repository name
- page: Page number (optional)
- per_page: Results per page (optional)
- sha: Branch name or commit SHA (optional)
Returns:
MCP response with list of commits
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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 mentions the tool lists commits but fails to describe key behaviors such as pagination details (implied by 'page' and 'per_page' but not explained), authentication requirements, rate limits, or what the response format includes (e.g., commit details). This is a significant gap for a tool with no 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 core purpose in the first sentence, followed by structured sections for Args and Returns. There's minimal waste, though the Returns section could be more informative given the lack of output schema.
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 (GitHub API interaction), no annotations, no output schema, and low schema description coverage, the description is incomplete. It lacks details on authentication, error handling, response structure, and behavioral traits, making it inadequate for a tool with nested parameters and no structured support.
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 semantic details for parameters (owner, repo, page, per_page, sha) beyond the input schema, which has 0% description coverage and only shows a generic 'params' object. However, it doesn't fully compensate by explaining data types, constraints, or example values, leaving some ambiguity in parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with 'List commits in a GitHub repository,' specifying the verb (list) and resource (commits in a GitHub repository). However, it doesn't explicitly differentiate from sibling tools like 'list_issues' or 'list_issue_comments,' which are also list operations but for different resources, so it lacks sibling differentiation.
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 any prerequisites, context for usage, or comparisons to sibling tools like 'get_repository' or 'search_repositories,' leaving the agent without explicit usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List comments on an issue.
Args:
params: Parameters for listing comments including:
- owner: Repository owner (user or organization)
- repo: Repository name
- issue_number: Issue number
- since: Filter by date (optional)
- page: Page number (optional)
- per_page: Results per page (optional)
Returns:
List of comments from GitHub API
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
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. While 'List comments' implies a read-only operation, the description doesn't specify authentication requirements, rate limits, pagination behavior (beyond listing parameters), error conditions, or what format the 'List of comments from GitHub API' actually contains. For a tool with 6 parameters and no annotation coverage, this is insufficient.
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 well-structured with clear sections (Args, Returns) and uses bullet points for parameters. It's appropriately sized for a tool with multiple parameters. The first sentence states the purpose clearly. However, the 'Args' section could be more concise by integrating parameter descriptions into the main text rather than a separate bulleted list.
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 read-only listing tool with 6 parameters and no output schema, the description is moderately complete. It covers the purpose and parameters adequately but lacks behavioral context (authentication, rate limits, pagination details) and doesn't describe the return value format beyond 'List of comments from GitHub API'. Given the complexity and lack of annotations/output schema, it should provide more operational guidance.
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 provides a comprehensive parameter list with brief explanations for all 6 parameters (owner, repo, issue_number, since, page, per_page). Since schema description coverage is 0% (the schema has no descriptions at the top level), this description adds significant value by documenting all parameters. However, it doesn't provide format details (like ISO 8601 for 'since') or constraints (like 'max 100' for per_page) that appear 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 with 'List comments on an issue' - a specific verb (list) and resource (comments on an issue). It distinguishes from siblings like 'get_issue' (which retrieves issue metadata) or 'add_issue_comment' (which creates comments). However, it doesn't explicitly differentiate from 'update_issue_comment' or 'delete_issue_comment' which also involve comments.
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 when to use 'list_issue_comments' versus 'get_issue' (which might include some comments), or when to use it versus 'search_repositories' for broader searches. There's no context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List issues from a GitHub repository.
Args:
params: Parameters for listing issues including:
- owner: Repository owner (user or organization)
- repo: Repository name
- state: Issue state (open, closed, all)
- labels: Filter by labels
- sort: Sort field (created, updated, comments)
- direction: Sort direction (asc, desc)
- since: Filter by date
- page: Page number for pagination
- per_page: Number of results per page (max 100)
Returns:
List of issues from GitHub API
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
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 mentions that it returns a 'List of issues from GitHub API,' which implies a read-only operation, but doesn't detail pagination behavior (e.g., how to handle multiple pages), rate limits, authentication requirements, or error handling. The description adds basic context but lacks depth for a tool with multiple parameters and no 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 well-structured and appropriately sized, with a clear purpose statement followed by organized sections for 'Args' and 'Returns.' Each sentence earns its place by providing essential information. However, it could be slightly more concise by integrating the parameter details more seamlessly, and the 'Args' section is somewhat verbose but necessary given the parameter count.
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 (9 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and parameters well but lacks details on behavioral aspects like pagination, error handling, or authentication. Without an output schema, it briefly mentions the return type ('List of issues from GitHub API') but doesn't specify the structure or fields of the issues, leaving gaps for an AI agent to infer.
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 value beyond the input schema, which has 0% schema description coverage (no descriptions in the schema properties). It provides a clear breakdown of all parameters (owner, repo, state, labels, sort, direction, since, page, per_page) with brief explanations and constraints (e.g., 'max 100' for per_page). This fully compensates for the schema's lack of documentation, making parameter meanings explicit and actionable.
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: 'List issues from a GitHub repository.' It specifies the verb ('List') and resource ('issues from a GitHub repository'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_issue' (which fetches a single issue) or 'list_issue_comments' (which lists comments on an issue), though the name 'list_issues' inherently suggests a bulk operation.
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_issue' for retrieving a single issue or 'search_repositories' for broader searches, nor does it specify prerequisites such as authentication or repository access. Usage is implied only through the parameter list, with no explicit context or exclusions provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Push multiple files to a GitHub repository in a single commit.
Args:
params: Dictionary with file parameters
- owner: Repository owner (username or organization)
- repo: Repository name
- branch: Branch to push to
- files: List of files to push, each with path and content
- message: Commit message
Returns:
MCP response with file push result
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only mentions the single-commit behavior. It doesn't disclose critical behavioral traits like authentication requirements, error handling, rate limits, whether it overwrites existing files, or what 'file push result' contains. The description is insufficient for a mutation 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?
Perfectly structured with a clear purpose statement followed by organized Args and Returns sections. Every sentence adds value with zero redundancy. The two-sentence format is 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?
For a complex mutation tool with 5 effective parameters, no annotations, and no output schema, the description provides basic purpose and parameter semantics but lacks behavioral context, error information, and output details. It's minimally adequate but has significant gaps given the tool's complexity.
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 has 0% description coverage with only one undocumented 'params' object. The description compensates by detailing all 5 nested parameters (owner, repo, branch, files, message) with clear semantics, though it doesn't specify data formats or constraints for 'files' list structure.
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 ('push multiple files') and resource ('to a GitHub repository'), distinguishing it from sibling tools like create_or_update_file (single file) or list_commits (read-only). The verb 'push' combined with 'in a single commit' provides precise scope.
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 batch file operations but doesn't explicitly state when to use this vs. alternatives like create_or_update_file (for single files) or create_branch (for branch creation). No guidance on prerequisites or exclusions is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Remove a label from an issue.
Args:
params: Parameters for removing a label including:
- owner: Repository owner (user or organization)
- repo: Repository name
- issue_number: Issue number
- label: Label to remove
Returns:
Empty response on success or error if label doesn't exist
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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. It states the action is a removal (implying mutation) and mentions error handling for non-existent labels, but lacks critical behavioral details: required permissions (e.g., write access to the repo), whether the operation is reversible, rate limits, or what 'Empty response on success' entails (e.g., HTTP 204). For a mutation tool with zero annotation coverage, this is insufficient.
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 well-structured with clear sections (purpose, Args, Returns) and uses bullet points for parameters. It's front-loaded with the core purpose. The Args section could be more concise by integrating parameter details directly, but overall it 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 tool's complexity (mutation operation with 4 parameters), lack of annotations, and no output schema, the description is incomplete. It covers the basic action and parameters but misses behavioral context (permissions, reversibility), detailed error cases, and output specifics. For a mutation tool in this context, it should provide more guidance to ensure safe and correct usage.
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 0%, so the description must compensate. It lists all four parameters (owner, repo, issue_number, label) with brief explanations in the Args section, adding meaning beyond the bare schema. However, it doesn't provide format details (e.g., owner as username/organization string), constraints, or examples, leaving gaps in parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Remove a label from an issue') with the exact resource ('issue') and distinguishes it from sibling tools like 'add_issue_labels' and 'update_issue'. It uses a precise verb ('Remove') that leaves no ambiguity about the operation.
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 prerequisites (e.g., the issue must exist, the label must be present), compare it to sibling tools like 'update_issue' for label management, or specify error conditions beyond a generic mention of 'error if label doesn't exist'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for GitHub repositories.
Args:
params: Dictionary with search parameters
- query: Search query
- page: Page number for pagination (optional)
- per_page: Results per page (optional)
Returns:
MCP response with matching repositories
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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. While it mentions pagination parameters (page, per_page), it doesn't describe rate limits, authentication requirements, error conditions, or what the 'MCP response' contains (e.g., structure, fields). For a search tool with zero annotation coverage, this leaves significant gaps in understanding its 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?
The description is well-structured with clear sections (Args, Returns) and uses bullet points for parameters. It's front-loaded with the core purpose. However, the 'Returns' section is vague ('MCP response with matching repositories'), and some sentences could be more precise (e.g., specifying what 'query' supports).
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 (search operation with pagination), lack of annotations, no output schema, and low schema coverage (0%), the description is incomplete. It doesn't explain the response format, error handling, or important constraints like rate limits or query syntax. For a tool with one nested parameter object, more context is needed for effective use.
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 parameter details beyond the schema: it explains that 'params' is a dictionary containing 'query', 'page', and 'per_page'. However, schema description coverage is 0%, and the description doesn't cover all potential parameters (e.g., sort, order, language filters mentioned in GitHub's API). It partially compensates but doesn't fully bridge the coverage gap.
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: 'Search for GitHub repositories.' This is a specific verb+resource combination that distinguishes it from siblings like get_repository (which fetches a single repository) or create_repository (which creates new ones). However, it doesn't explicitly differentiate from list_issues or list_commits, which are also search/list operations but on different resources.
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 when to prefer search_repositories over get_repository (for single repo lookup) or list_issues (for issue searches), nor does it specify prerequisites like authentication needs or rate limits. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update an existing issue.
Args:
params: Parameters for updating an issue including:
- owner: Repository owner (user or organization)
- repo: Repository name
- issue_number: Issue number to update
- title: New title (optional)
- body: New description (optional)
- state: New state (optional)
- labels: New labels (optional)
- assignees: New assignees (optional)
- milestone: New milestone number (optional)
Returns:
Updated issue details from GitHub API
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions that parameters are optional and returns 'Updated issue details from GitHub API,' but lacks details on permissions needed, error handling, rate limits, or what happens when optional fields are omitted. For a mutation tool with zero annotation coverage, this is insufficient.
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 well-structured with clear sections for 'Args' and 'Returns,' and each sentence adds value. It could be slightly more concise by integrating the parameter list more tightly, but overall it's efficient and front-loaded with the core purpose.
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 (mutation with multiple parameters), lack of annotations, and no output schema, the description is moderately complete. It covers parameters well but misses behavioral aspects like authentication needs or error cases. It's adequate for basic use but has gaps for robust agent operation.
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 value beyond the input schema, which has 0% schema description coverage. It lists all parameters with brief explanations (e.g., 'New title (optional)'), clarifying optionality and basic semantics that the schema lacks. This fully compensates for the schema's deficiency.
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: 'Update an existing issue.' It specifies the verb ('update') and resource ('issue'), but doesn't differentiate from sibling tools like 'update_issue_comment' or 'add_issue_labels' beyond the basic resource type.
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 like 'add_issue_labels' or 'remove_issue_label' for partial updates, or 'create_issue' for new issues. The description only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update an issue comment.
Args:
params: Parameters for updating a comment including:
- owner: Repository owner (user or organization)
- repo: Repository name
- issue_number: Issue number containing the comment
- comment_id: Comment ID to update
- body: New comment text
Returns:
Updated comment details from GitHub API
| Name | Required | Description | Default |
|---|---|---|---|
| params | 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. While 'Update' implies a mutation, the description doesn't cover critical traits: whether this requires authentication, if it's idempotent, rate limits, error conditions (e.g., invalid comment_id), or what 'Updated comment details' includes. This is inadequate for a mutation 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 well-structured and front-loaded: it starts with the core purpose, then lists parameters clearly in bullet points, and ends with return information. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly.
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 mutation tool with 5 parameters, no annotations, and no output schema), the description is partially complete. It covers the purpose and parameters adequately but lacks behavioral details (e.g., auth, errors) and output specifics. This is the minimum viable for basic use but leaves gaps for robust agent operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, but the description compensates by listing all parameters (owner, repo, issue_number, comment_id, body) with brief explanations in the 'Args' section. This adds meaningful context beyond the bare schema, though it doesn't detail formats (e.g., string constraints) or provide examples. With 0% coverage, this is strong but not exhaustive.
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: 'Update an issue comment.' It specifies the verb ('Update') and resource ('issue comment'), making the action unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'add_issue_comment' or 'delete_issue_comment', which would be needed for a score of 5.
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 prerequisites (e.g., needing edit permissions), contrast with 'add_issue_comment' for new comments, or specify scenarios where updating is appropriate versus deleting and recreating. This leaves the agent without usage context.
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.
add_issue_commentadd_issue_labelscreate_branchcreate_issuecreate_or_update_filecreate_repositorydelete_issue_commentfork_repositoryget_file_contentsget_issueget_repositorylist_commitslist_issue_commentslist_issuespush_filesremove_issue_labelsearch_repositoriesupdate_issueupdate_issue_commentScored across 19 tools
Most tools have distinct purposes targeting specific GitHub resources and actions, like issues, files, repositories, and branches. However, there is some overlap between 'create_or_update_file' and 'push_files' as both handle file modifications, which could cause confusion. All other tools are clearly separated by their target resources.
All tool names follow a consistent snake_case pattern with clear verb_noun structures, such as 'create_issue', 'list_commits', and 'update_issue_comment'. The naming is uniform across all 19 tools, making them predictable and easy to understand for an agent.
With 19 tools, the count is slightly high but reasonable for a comprehensive GitHub API server covering issues, repositories, files, and branches. It provides extensive functionality without being overwhelming, though it borders on being heavy compared to typical well-scoped servers.
The tool set offers complete CRUD and lifecycle coverage for key GitHub domains, including issues (create, get, update, delete, comment, label), repositories (create, get, fork, search), files (create/update, get, push), and branches (create). There are no obvious gaps, enabling agents to handle common workflows effectively.
Access the GitHub API, enabling file operations, repository management, search functionality, and…
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Manage repositories, users, releases, and automate GitHub workflows
Read and write KukGit repositories, files, issues and pull requests from an AI assistant.
