DeepSource MCP Server
DeepSource MCP 서버
DeepSource와 통합되어 AI 어시스턴트에게 코드 품질 측정항목, 문제 및 분석 결과에 대한 액세스를 제공하는 MCP(Model Context Protocol) 서버입니다.
개요
DeepSource MCP 서버는 AI 어시스턴트가 모델 컨텍스트 프로토콜을 통해 DeepSource의 코드 품질 분석 기능과 상호 작용할 수 있도록 지원합니다. 이러한 통합을 통해 AI 어시스턴트는 다음과 같은 작업을 수행할 수 있습니다.
코드 메트릭 및 분석 결과 검색
접근 및 필터 문제
품질 상태 확인
시간 경과에 따른 프로젝트 품질 분석
Related MCP server: CodeAlive MCP
특징
DeepSource API 통합 : GraphQL API를 통해 DeepSource에 연결
MCP 프로토콜 지원 : AI 어시스턴트 통합을 위한 모델 컨텍스트 프로토콜 구현
품질 측정 항목 및 임계값 : 임계값을 사용하여 코드 품질 측정 항목을 검색하고 관리합니다.
보안 규정 준수 보고서 : OWASP Top 10, SANS Top 25 및 MISRA-C 규정 준수 보고서에 액세스하세요.
종속성 취약점 : 종속성에 대한 보안 취약점 정보에 액세스합니다.
TypeScript/Node.js : 유형 안전성과 최신 JavaScript 기능을 위해 TypeScript로 구축됨
크로스 플랫폼 : Linux, macOS 및 Windows에서 작동합니다.
강력한 오류 처리 : 네트워크, 인증 및 구문 분석 문제에 대한 포괄적인 오류 처리
Claude Desktop과 함께 사용
claude_desktop_config.json편집합니다:클로드 데스크톱 열기
Settings->Developer->Edit Config으로 이동하세요.아래 구성 중 하나를
mcpServers섹션에 추가하세요.
변경 사항을 적용하려면 Claude Desktop을 다시 시작하세요.
예제 쿼리
연결되면 AI 도우미는 다음과 같은 쿼리를 통해 DeepSource 데이터를 사용할 수 있습니다.
지엑스피1
이렇게 하려면 필터가 있는 project_issues 도구를 사용합니다.
{
"projectKey": "your-project-key",
"path": "src/",
"analyzerIn": ["javascript"],
"first": 10
}분석 실행을 필터링하려면:
Show me the most recent Python analysis runs이렇게 하려면 필터와 함께 project_runs 도구를 사용합니다.
{
"projectKey": "your-project-key",
"analyzerIn": ["python"],
"first": 5
}코드 품질 측정 항목:
What's my code coverage percentage? Is it meeting our thresholds?여기서는 quality_metrics 도구를 사용합니다.
{
"projectKey": "your-project-key",
"shortcodeIn": ["LCV", "BCV", "CCV"]
}보안 규정 준수 보고서의 경우:
Are we compliant with OWASP Top 10 security standards?이 경우 compliance_report 도구를 사용합니다.
{
"projectKey": "your-project-key",
"reportType": "OWASP_TOP_10"
}임계값을 설정하려면 다음을 수행하십시오.
Update our line coverage threshold to 80%update_metric_threshold 도구를 사용합니다.
{
"projectKey": "your-project-key",
"repositoryId": "repo-id",
"metricShortcode": "LCV",
"metricKey": "AGGREGATE",
"thresholdValue": 80
}환경 변수
서버는 다음과 같은 환경 변수를 지원합니다.
DEEPSOURCE_API_KEY(필수): 인증을 위한 DeepSource API 키LOG_FILE(선택 사항): 로그를 기록할 파일 경로입니다. 설정하지 않으면 로그가 기록되지 않습니다.LOG_LEVEL(선택 사항): 기록할 최소 로그 수준(DEBUG, INFO, WARN, ERROR). 기본값은 DEBUG입니다.
로깅을 포함한 구성 예:
{
"mcpServers": {
"deepsource": {
"command": "npx",
"args": [
"-y",
"deepsource-mcp-server@1.1.0"
],
"env": {
"DEEPSOURCE_API_KEY": "your-deepsource-api-key",
"LOG_FILE": "/tmp/deepsource-mcp.log",
"LOG_LEVEL": "DEBUG"
}
}
}
}도커
{
"mcpServers": {
"deepsource": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"DEEPSOURCE_API_KEY",
"-e",
"LOG_FILE=/tmp/deepsource-mcp.log",
"-v",
"/tmp:/tmp",
"sapientpants/deepsource-mcp-server"
],
"env": {
"DEEPSOURCE_API_KEY": "your-deepsource-api-key",
"LOG_FILE": "/tmp/deepsource-mcp.log"
}
}
}
}엔피엑스
{
"mcpServers": {
"deepsource": {
"command": "npx",
"args": [
"-y",
"deepsource-mcp-server@1.1.0"
],
"env": {
"DEEPSOURCE_API_KEY": "your-deepsource-api-key"
}
}
}
}사용 가능한 도구
DeepSource MCP 서버는 다음과 같은 도구를 제공합니다.
projects: 사용 가능한 모든 DeepSource 프로젝트 나열매개변수:
필수 매개변수 없음
project_issues: 필터링을 사용하여 DeepSource 프로젝트의 이슈를 가져옵니다.매개변수:
projectKey(필수) - DeepSource 프로젝트의 고유 식별자페이지 매김 매개변수:
offset(선택 사항) - 페이지 매김을 위해 건너뛸 항목 수first(선택 사항) - 반환할 항목 수(기본값은 10)after(선택 사항) - 앞으로 페이지 매김을 위한 커서before(선택 사항) - 역방향 페이지 매김을 위한 커서last(선택 사항) - 'before' 커서 전에 반환할 항목 수(기본값: 10)
필터링 매개변수:
path(선택 사항) - 특정 파일 경로로 문제 필터링analyzerIn(선택 사항) - 특정 분석기(예: ["python", "javascript"])로 문제를 필터링합니다.tags(선택 사항) - 태그로 문제 필터링
project_runs: 필터링을 사용하여 DeepSource 프로젝트에 대한 분석 실행 목록매개변수:
projectKey(필수) - DeepSource 프로젝트의 고유 식별자페이지 매김 매개변수:
offset(선택 사항) - 페이지 매김을 위해 건너뛸 항목 수first(선택 사항) - 반환할 항목 수(기본값은 10)after(선택 사항) - 앞으로 페이지 매김을 위한 커서before(선택 사항) - 역방향 페이지 매김을 위한 커서last(선택 사항) - 'before' 커서 전에 반환할 항목 수(기본값: 10)
필터링 매개변수:
analyzerIn(선택 사항) - 특정 분석기(예: ["python", "javascript"])로 실행되는 필터
run: runUid 또는 commitOid로 실행된 특정 분석을 가져옵니다.매개변수:
runIdentifier(필수) - 실행을 식별하기 위한 runUid(UUID) 또는 commitOid(커밋 해시)
recent_run_issues: 페이지네이션 지원을 통해 특정 브랜치에서 실행된 가장 최근의 분석에서 이슈를 가져옵니다.매개변수:
projectKey(필수) - DeepSource 프로젝트의 고유 식별자branchName(필수) - 가장 최근 실행을 가져올 브랜치 이름페이지 매김 매개변수:
first(선택 사항) - 반환할 문제 수(기본값은 10)after(선택 사항) - 앞으로 페이지 매김을 위한 커서last(선택 사항) - 커서 전에 반환할 문제 수(기본값: 10)before(선택 사항) - 역방향 페이지 매김을 위한 커서
보고:
지점에서 가장 최근에 실행된 내용에 대한 정보
프로젝트의 현재 문제(참고: 문제는 실행별이 아닌 저장소 수준입니다)
커서 및 페이지 상태를 포함한 페이지 매김 정보
실행 및 분기에 대한 메타데이터
dependency_vulnerabilities: DeepSource 프로젝트에서 종속성 취약점을 가져옵니다.매개변수:
projectKey(필수) - DeepSource 프로젝트의 고유 식별자페이지 매김 매개변수:
offset(선택 사항) - 페이지 매김을 위해 건너뛸 항목 수first(선택 사항) - 반환할 항목 수(기본값은 10)after(선택 사항) - 앞으로 페이지 매김을 위한 커서before(선택 사항) - 역방향 페이지 매김을 위한 커서last(선택 사항) - 'before' 커서 전에 반환할 항목 수(기본값: 10)
quality_metrics: 필터링을 사용하여 DeepSource 프로젝트에서 품질 지표 가져오기매개변수:
projectKey(필수) - DeepSource 프로젝트의 고유 식별자shortcodeIn(선택 사항) - 특정 단축 코드(예: ["LCV", "BCV"])로 메트릭 필터링
다음과 같은 메트릭을 반환합니다.
라인 커버리지(LCV)
지점 커버리지(BCV)
문서 범위(DCV)
중복 코드 비율(DDP)
각 메트릭에는 현재 값, 임계값 및 통과/실패 상태가 포함됩니다.
update_metric_threshold: 특정 품질 측정 항목에 대한 임계값을 업데이트합니다.매개변수:
projectKey(필수) - DeepSource 프로젝트의 고유 식별자repositoryId(필수) - GraphQL 저장소 IDmetricShortcode(필수) - 업데이트할 메트릭의 단축 코드metricKey(필수) - 메트릭의 언어 또는 컨텍스트 키thresholdValue(선택 사항) - 새 임계값 또는 임계값을 제거하려면 null을 지정합니다.
예: 80% 라인 적용 임계값 설정: metricShortcode="LCV", metricKey="AGGREGATE", thresholdValue=80
update_metric_setting: 품질 지표에 대한 설정을 업데이트합니다.매개변수:
projectKey(필수) - DeepSource 프로젝트의 고유 식별자repositoryId(필수) - GraphQL 저장소 IDmetricShortcode(필수) - 업데이트할 메트릭의 단축 코드isReported(필수) - 메트릭을 보고해야 하는지 여부isThresholdEnforced(필수) - 임계값을 적용해야 하는지 여부(검사에 실패할 수 있음)
compliance_report: DeepSource 프로젝트에서 보안 규정 준수 보고서를 받습니다.
매개변수:
projectKey(필수) - DeepSource 프로젝트의 고유 식별자reportType(필수) - 가져올 규정 준수 보고서 유형( OWASP Top 10 , SANS Top 25 또는 MISRA-C )
다음을 포함한 포괄적인 보안 규정 준수 데이터를 반환합니다.
범주 및 심각도별 보안 문제 통계
준수 상태(합격/불합격)
시간 경과에 따른 변화를 보여주는 추세 데이터
보안 태세 개선을 위한 분석 및 권장 사항
개발
저장소를 복제합니다.
git clone https://github.com/sapientpants/deepsource-mcp-server.git
cd deepsource-mcp-server종속성 설치:
pnpm install프로젝트를 빌드하세요:
pnpm run buildClaude Desktop 구성
{
"mcpServers": {
"deepsource": {
"command": "node",
"args": [
"/path/to/deepsource-mcp-server/dist/index.js"
],
"env": {
"DEEPSOURCE_API_KEY": "your-deepsource-api-key"
}
}
}
}필수 조건
Node.js 20 이상
pnpm 10.7.0 이상
Docker(컨테이너 빌드용)
스크립트
pnpm run build- TypeScript 코드 빌드pnpm run start- 서버 시작pnpm run dev- 개발 모드로 서버 시작pnpm run test- 테스트 실행pnpm run lint- ESLint 실행pnpm run format- Prettier로 코드 포맷하기
문제 해결
디버그 로깅 활성화
문제가 발생하는 경우 디버그 로깅을 활성화하여 자세한 정보를 확인하세요.
LOG_FILE환경 변수를 로그를 기록할 파일 경로로 설정합니다.LOG_LEVEL``DEBUG로 설정합니다(기본값입니다)자세한 오류 정보는 로그 파일을 확인하세요.
구성 예:
{
"mcpServers": {
"deepsource": {
"command": "npx",
"args": ["-y", "deepsource-mcp-server@1.1.0"],
"env": {
"DEEPSOURCE_API_KEY": "your-api-key",
"LOG_FILE": "/tmp/deepsource-mcp.log",
"LOG_LEVEL": "DEBUG"
}
}
}
}그런 다음 로그 파일을 확인하세요.
tail -f /tmp/deepsource-mcp.log일반적인 문제
인증 오류 :
DEEPSOURCE_API_KEY가 올바르고 필요한 권한이 있는지 확인하세요.로그가 나타나지 않음 :
LOG_FILE경로가 쓰기 가능한지, 상위 디렉토리가 존재하는지 확인하십시오.도구 오류 : 자세한 오류 메시지와 스택 추적을 보려면 로그 파일을 확인하세요.
특허
MIT
Available Tools
10 toolscompliance_reportA
Get security compliance reports from a DeepSource project
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to identify the project | |
| reportType | Yes | Type of compliance report to fetch |
Output Schema
| Name | Required | Description |
|---|---|---|
| key | Yes | |
| title | Yes | |
| currentValue | Yes | |
| status | Yes | |
| securityIssueStats | Yes | |
| trends | No | |
| analysis | Yes | |
| recommendations | Yes |
TDQS
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 only says 'Get', implying a read operation, but does not disclose any side effects, permissions, rate limits, or output behavior beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words. It is front-loaded with the verb and resource, making it 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 presence of an output schema and 100% parameter coverage, the description is minimally adequate. However, it lacks behavioral transparency and usage context, which are not compensated by other fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% – both 'projectKey' and 'reportType' are described in the input schema. The description adds no additional semantic information, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'security compliance reports from a DeepSource project'. It distinguishes this tool from siblings like 'dependency_vulnerabilities' and 'project_issues', which focus on different data.
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 explicit guidance on when to use this tool versus alternatives. The description implies it's for compliance reports but does not specify when not to use it or mention other tools for similar purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dependency_vulnerabilitiesB
Get dependency vulnerabilities from a DeepSource project
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch vulnerabilities for | |
| first | No | Number of items to retrieve (forward pagination) | |
| after | No | Cursor to start retrieving items after (forward pagination) | |
| last | No | Number of items to retrieve (backward pagination) | |
| before | No | Cursor to start retrieving items before (backward pagination) | |
| page_size | No | Number of items per page (alias for first, for convenience) | |
| max_pages | No | Maximum number of pages to fetch (enables automatic multi-page fetching) |
Output Schema
| Name | Required | Description |
|---|---|---|
| vulnerabilities | Yes | |
| pageInfo | Yes | |
| totalCount | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states a read operation but omits details like pagination behavior, potential errors, or authentication needs, despite the schema hinting at pagination.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. However, it is very brief and could be restructured to include more context efficiently.
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 (7 parameters, output schema, no annotations), the description is too minimal. It fails to explain the tool's purpose in a broader workflow or set expectations about pagination and project key 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 coverage is 100%, so the baseline is 3. The description adds no extra meaning beyond what the schema already provides for each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get'), the resource ('dependency vulnerabilities'), and the scope ('from a DeepSource project'). This distinctly differentiates it from sibling tools like compliance_report or project_issues.
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 context about prerequisites, exclusions, or comparative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
project_issuesB
Get issues from a DeepSource project with filtering capabilities
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch issues for | |
| path | No | Filter issues by file path | |
| analyzerIn | No | Filter issues by analyzer shortcodes | |
| tags | No | Filter issues by tags | |
| first | No | Number of items to retrieve (forward pagination) | |
| after | No | Cursor to start retrieving items after (forward pagination) | |
| last | No | Number of items to retrieve (backward pagination) | |
| before | No | Cursor to start retrieving items before (backward pagination) | |
| page_size | No | Number of items per page (alias for first, for convenience) | |
| max_pages | No | Maximum number of pages to fetch (enables automatic multi-page fetching) |
Output Schema
| Name | Required | Description |
|---|---|---|
| issues | Yes | |
| pageInfo | Yes | |
| pagination | No | User-friendly pagination metadata |
| totalCount | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose pagination behavior, rate limits, or what the response contains. Without annotations, the description fails to inform about key behavioral 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?
Single sentence, front-loaded, but too brief; could be expanded to include key details without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 10 parameters including pagination, the description is insufficient; doesn't explain pagination cursor usage or filtering capabilities beyond the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage, so baseline is 3. Description adds no extra meaning beyond what the schema already provides.
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?
Description clearly states verb 'Get' and resource 'issues from a DeepSource project', distinguishing it from sibling tools like compliance_report and dependency_vulnerabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs. alternatives, such as other issue-related tools (e.g., recent_run_issues). Lacks explicit context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
projectsA
List all available DeepSource projects. Returns a list of project objects with "key" and "name" properties.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| projects | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility. It transparently describes a read-only listing operation with no side effects. While simple, it fully discloses the behavior without omission.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action ('List all available DeepSource projects') and follows with concise details. No extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, an output schema, and a straightforward task (listing projects), the description is complete. It covers the purpose and return format, and no additional context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Since the input schema has no parameters (100% coverage), the description adds value by specifying the return structure (key and name properties). This exceeds the baseline of 4 for zero-parameter tools.
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 it lists all available DeepSource projects and specifies the return properties (key and name). This distinguishes it from sibling tools like compliance_report which focus on specific aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as compliance_report or project_issues. The description simply states the functionality without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quality_metricsB
Get quality metrics from a DeepSource project with optional filtering
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch quality metrics for | |
| shortcodeIn | No | Optional filter for specific metric shortcodes |
Output Schema
| Name | Required | Description |
|---|---|---|
| metrics | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It only indicates a read operation ('Get') but does not disclose side effects, authentication requirements, rate limits, or any behavioral traits beyond that.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence of 11 words. It is highly concise with no superfluous information, earning a top score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with 2 parameters and an output schema, so the description partially covers what is needed. However, it lacks context on prerequisites, how quality metrics relate to other sibling tools, or any performance implications. With output schema present, return values are covered, but overall completeness is average.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with descriptions, so the description adds minimal value ('optional filtering' is already implied by shortcodeIn). Baseline 3 is appropriate since the schema already explains the parameters adequately.
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', the resource 'quality metrics', and the source 'DeepSource project'. It also mentions optional filtering, which adds clarity. However, it does not elaborate on what 'quality metrics' entail (e.g., code quality metrics), missing an opportunity to differentiate from siblings like dependency_vulnerabilities or project_issues.
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 its siblings. The description mentions optional filtering but does not explain when filtering is appropriate or when alternative tools (e.g., for issues or vulnerabilities) should be used instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recent_run_issuesB
Get issues from the most recent analysis run on a specific branch
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch issues for | |
| branchName | Yes | Branch name to fetch the most recent run from | |
| first | No | Number of items to retrieve (forward pagination) | |
| after | No | Cursor to start retrieving items after (forward pagination) | |
| last | No | Number of items to retrieve (backward pagination) | |
| before | No | Cursor to start retrieving items before (backward pagination) | |
| page_size | No | Number of items per page (alias for first, for convenience) | |
| max_pages | No | Maximum number of pages to fetch (enables automatic multi-page fetching) |
Output Schema
| Name | Required | Description |
|---|---|---|
| run | Yes | |
| issues | Yes | |
| pageInfo | Yes | |
| totalCount | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. However, it only states a read-like operation without mentioning it is read-only, does not discuss authentication, rate limits, or pagination behavior. The description adds minimal value beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 9 words, extremely concise and front-loaded. Every word is necessary, and there is no redundant information.
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 8 parameters including pagination, and the presence of an output schema, the description is minimal. It does not explain that only the latest run is considered, nor does it describe the order or filtering. Adequate for basic understanding but incomplete for nuanced 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 100%, so the input schema already explains all parameters. The description does not add new meaning to any parameter beyond what the schema provides. Baseline score is 3 as description provides no additional semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves issues from the most recent analysis run on a specific branch. The verb 'Get' and resource 'issues' are specific, and the scope 'most recent analysis run on a specific branch' distinguishes it from siblings like 'project_issues' which likely list all issues.
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 such as 'project_issues' or 'runs'. It does not mention when not to use it or provide context about prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
runB
Get a specific analysis run by its runUid or commitOid
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to identify the project | |
| runIdentifier | Yes | The run identifier (runUid or commitOid) | |
| isCommitOid | No | Flag to indicate whether the runIdentifier is a commitOid (default: false) |
Output Schema
| Name | Required | Description |
|---|---|---|
| run | Yes | |
| analysis | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a read-only operation ('Get'), which is consistent with the expected behavior. With no annotations provided, the description adequately conveys that it is a retrieval tool, but it does not disclose error handling or behavior when the run is not found.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that conveys the essential purpose. No unnecessary words, making it easy for the agent to parse 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?
With an output schema present, the description does not need to explain return values. The description is sufficient for a simple getter tool, though it could be improved by mentioning that it returns a single run object. Overall, it is reasonably complete for 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?
Schema coverage is 100%, so the schema already documents the parameters. The description adds minimal value by restating that runIdentifier can be runUid or commitOid, but this is already encoded in the schema and the isCommitOid parameter. No additional semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a specific analysis run using either runUid or commitOid. The verb 'Get' and the resource 'analysis run' are explicit. However, it does not explicitly differentiate from the sibling tool 'runs', which likely lists all runs, so clarity is good but not perfect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'runs' or other tools. There is no mention of prerequisites or context, leaving the agent to infer usage from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
runsA
List analysis runs for a DeepSource project with filtering
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to fetch runs for | |
| analyzerIn | No | Filter runs by analyzer shortcodes | |
| first | No | Number of items to retrieve (forward pagination) | |
| after | No | Cursor to start retrieving items after (forward pagination) | |
| last | No | Number of items to retrieve (backward pagination) | |
| before | No | Cursor to start retrieving items before (backward pagination) | |
| page_size | No | Number of items per page (alias for first, for convenience) | |
| max_pages | No | Maximum number of pages to fetch (enables automatic multi-page fetching) |
Output Schema
| Name | Required | Description |
|---|---|---|
| runs | Yes | |
| pageInfo | Yes | |
| totalCount | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It does not mention pagination behavior, rate limits, or what happens on invalid projects. The schema includes cursor parameters (first, after, last, before, page_size, max_pages), but the description omits any behavioral context like automatic pagination or cursor-based pagination.
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?
A single, clear sentence with no fluff. Every word is necessary and earns its place.
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 8 parameters and no annotations, the description is minimal but combined with the schema is adequate. However, it lacks context on pagination behavior and does not differentiate from sibling tool 'run'. Output schema exists to describe return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with all parameters described. The description adds only 'with filtering', which is vague and does not provide additional meaning beyond the schema. Baseline score of 3 is appropriate as schema does the heavy lifting.
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?
Description clearly states the tool lists analysis runs for a DeepSource project with filtering. It uses a specific verb (list) and resource (analysis runs), and distinguishes from sibling tools like 'run' (likely single run retrieval) and 'project_issues'.
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?
Description implies usage for listing filtered runs, but does not explicitly state when not to use or mention alternatives. Context is clear, but no exclusions or comparisons to siblings are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_metric_settingC
Update the settings for a quality metric
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to identify the project | |
| repositoryId | Yes | Repository GraphQL ID | |
| metricShortcode | Yes | Code for the metric to update | |
| isReported | Yes | Whether the metric should be reported | |
| isThresholdEnforced | Yes | Whether the threshold should be enforced |
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| projectKey | Yes | |
| metricShortcode | Yes | |
| settings | Yes | |
| message | Yes | |
| next_steps | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It only says 'Update', implying mutation, but fails to state side effects, authorization requirements, or error conditions (e.g., what happens if the metric doesn't exist). This is insufficient for an agent to safely invoke the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no unnecessary words. It is front-loaded with the key verb and resource, making it efficient for scanning.
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?
Despite the existence of an output schema, the description is too minimal. It does not explain the broader context of updating metric settings, such as the effect on reporting or enforcement, or how it relates to other metric tools. The five required parameters are left unexplained beyond the schema, which is insufficient for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema; it merely restates the generic 'settings' without explaining how the boolean parameters affect the metric. The agent must rely entirely on the parameter descriptions 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 action ('Update') and the resource ('settings for a quality metric'), which is sufficient to understand the basic purpose. However, it does not distinguish from the sibling tool 'update_metric_threshold', which might update a specific threshold value, so specificity is slightly lacking.
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 'update_metric_threshold'. The description does not mention context, prerequisites, or scenarios where this tool is appropriate, leaving the agent to infer usage from the schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_metric_thresholdC
Update the threshold for a specific quality metric
| Name | Required | Description | Default |
|---|---|---|---|
| projectKey | Yes | DeepSource project key to identify the project | |
| repositoryId | Yes | Repository GraphQL ID | |
| metricShortcode | Yes | Code for the metric to update | |
| metricKey | Yes | Context key for the metric | |
| thresholdValue | No | New threshold value, or null to remove |
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| projectKey | Yes | |
| metricShortcode | Yes | |
| metricKey | Yes | |
| thresholdValue | No | |
| message | Yes | |
| next_steps | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full burden of disclosing behavioral traits. It only states 'update' which implies mutation but does not mention authorization needs, idempotency, side effects on other metrics, or whether setting threshold to null removes it. The description adds minimal value beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with only 8 words, containing no fluff or repetition. It efficiently communicates 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 complexity of the tool (5 parameters, 4 required, mutation with no annotations) and the presence of an output schema, the description is too brief. It lacks details on the effect of null thresholdValue, the meaning of metricShortcode values, and the expected outcome. The existing output schema partially mitigates the need for return value explanation, but the description should provide more operational 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?
The input schema already provides full descriptions for all 5 parameters (100% coverage). The description does not add any additional meaning or context beyond what is in the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action (update) and the object (threshold for a specific quality metric). It is specific enough to distinguish from sibling tools like update_metric_setting or quality_metrics, though it does not explicitly call out the distinction.
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, nor are there any prerequisites or when-not-to-use conditions stated. The description simply states what it does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Scored across 10 tools
Most tools have clearly distinct purposes (projects vs runs vs issues vs metrics vs security). However, project_issues and recent_run_issues both deal with issues and could cause initial confusion, though descriptions clarify the difference.
Tool names follow a predictable pattern: query tools are named after the resource (noun or noun phrase, e.g., projects, runs, quality_metrics) and mutation tools use verb_resource (e.g., update_metric_setting). This is consistent and readable.
10 tools is well-scoped for a code analysis server, covering core areas (projects, runs, issues, metrics, compliance, dependencies) without being overwhelming or too sparse.
The tool set covers most essential operations (list, get, update for key resources) but lacks branch listing (needed for recent_run_issues) and triggering analysis runs, which are notable gaps for a comprehensive interface.
Maintenance
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