MCP 지원 클라이언트에서 Sentrul 리서치, 규정 준수 및 추적 조회 에이전트를 실행하세요. 하나의 게이트웨이 뒤에서 257개의 프로덕션 에이전트가 작동합니다.
버전: 0.1.0 | SDK: @modelcontextprotocol/sdk@1.29.0 | 라이선스: MIT
~/Library/Application Support/Claude/claude_desktop_config.json에 다음 스니펫을 추가하세요:
{
"mcpServers": {
"sentrul": {
"command": "npx",
"args": ["-y", "sentrul-mcp-server"],
"env": { "SENTRUL_API_KEY": "optional — free tier auto-provisions" }
}
}
}Claude Desktop을 재시작하세요. 서버는 처음 사용할 때 무료 티어 키를 자동으로 프로비저닝하므로 별도의 가입이 필요하지 않습니다.
Related MCP server: enterprise-knowledge-integrator
sentrul_researchURL을 가져와 콘텐츠에 대해 Sentrul의 리서치 에이전트를 실행합니다. 요약, 엔티티, 주장 및 공개 Langfuse 추적 URL을 반환합니다.
예시 프롬프트
"https://example.com/report 를 조사하고 주요 주장을 요약해 줘"
"https://arxiv.org/abs/2401.00001 에 대해 심층 리서치를 수행해 줘"
sentrul_compliance_scan일반적인 규정 준수 패턴(PII, PCI, HIPAA, GDPR 데이터)에 대해 텍스트를 스캔합니다. 결과와 심각도를 반환합니다. 법적 조언이 아닙니다.
예시 프롬프트
"이 문서에서 PII 및 PCI 위반 사항을 스캔해 줘: [텍스트 붙여넣기]"
"다음 개인정보 처리방침에서 GDPR 데이터 패턴을 확인해 줘"
sentrul_trace_lookupID를 통해 공개 Langfuse 추적을 검색합니다. 검사를 위해 구조화된 추적 페이로드를 반환합니다.
예시 프롬프트
"Sentrul에서 추적 clm8abc123xyz를 가져와 줘"
"추적 ID abc-def-456에 대한 관찰 트리(observation tree)를 보여 줘"
변수 | 필수 | 기본값 | 설명 |
| 아니요 | 자동 프로비저닝 | Sentrul API 키입니다. 설정하지 않으면 무료 티어를 사용합니다. |
| 아니요 |
| Sentrul 게이트웨이 기본 URL을 재정의합니다(스테이징 / 로컬 개발용). |
SENTRUL_API_KEY가 설정되지 않은 경우, 서버는 다음을 수행합니다:
~/.sentrul/mcp-key에서 캐시된 키를 확인합니다.
캐시가 없으면 POST /v1/mcp/keys/anonymous를 호출하여 속도 제한이 있는 무료 키를 자동으로 프로비저닝합니다.
향후 실행을 위해 키를 ~/.sentrul/mcp-key(모드 0600)에 저장합니다.
MCP 클라이언트 설정에서 SENTRUL_API_KEY를 설정하여 언제든지 업그레이드할 수 있습니다.
셸 실행 없음 — 서버는 셸 명령을 실행하거나 하위 프로세스를 생성하지 않습니다.
PII 로깅 없음 — 환경 변수와 API 키는 로그나 표준 출력(stdout)에 기록되지 않습니다.
읽기 전용 — 세 가지 도구 모두 실행 시 읽기 전용이며, 사용자 파일이나 외부 상태에 쓰기를 수행하지 않습니다.
파일 시스템 쓰기 — 유일한 쓰기 작업은 ~/.sentrul/mcp-key(키 캐시)에만 수행됩니다. 해당 디렉터리 외부에는 아무것도 기록되지 않습니다.
공급망 — 배포 시 2FA가 적용된 npm 계정 + sigstore 인증을 사용합니다(사용 가능한 경우 SECURITY.md 참조).
pnpm install
pnpm run build # tsc → dist/
pnpm test # vitest run
# Smoke test — should print 3 tools in the tools/list response:
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | node dist/index.js홈페이지: https://sentrul.com
MIT 라이선스 — Copyright (c) 2026 Sentrul
Scan text for common compliance patterns (PII, PCI, HIPAA, GDPR data). Returns findings + severity. Not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| frameworks | No |
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 the tool 'Returns findings + severity,' which gives some insight into output behavior, but lacks details on error handling, rate limits, authentication needs, or performance characteristics. For a scanning tool with 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 highly concise and front-loaded, consisting of two efficient sentences that convey core functionality and a disclaimer. Every sentence earns its place with no wasted words, making it easy 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?
Given the tool's complexity (scanning for compliance patterns), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the structure of returned findings, severity levels, error cases, or limitations. For a tool with two parameters and behavioral uncertainty, more context is needed to be fully helpful.
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 has 0% description coverage, so the description must compensate. It implies the 'text' parameter is the input to scan and lists compliance frameworks (PII, PCI, HIPAA, GDPR) that relate to the 'frameworks' parameter. However, it doesn't fully explain parameter semantics, such as allowed values for 'frameworks' or format expectations for 'text.' The baseline is 3 due to some added meaning but incomplete coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Scan text for common compliance patterns (PII, PCI, HIPAA, GDPR data).' It specifies the verb ('scan'), resource ('text'), and scope ('compliance patterns'). However, it doesn't explicitly differentiate from sibling tools like 'sentrul_research' or 'sentrul_trace_lookup,' which prevents 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 minimal usage guidance. It mentions what the tool does but offers no explicit advice on when to use it versus alternatives, nor does it mention prerequisites or exclusions. The disclaimer 'Not legal advice' is helpful but doesn't constitute usage guidelines for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a URL and run Sentrul's research-agent on the content. Returns summary, entities, claims, and a public Langfuse trace URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| depth | No | quick |
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 fetching a URL and running a research-agent, which implies external network calls and processing, but lacks details on permissions, rate limits, error handling, or what 'quick' vs. 'full' depth entails. It states the return values but not their format or potential side effects.
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 efficiently conveys the core action, inputs, and outputs without redundancy. It is front-loaded with the main purpose and appropriately sized for the tool's complexity.
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 has no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It covers the basic purpose and outputs but lacks details on behavioral traits, parameter meanings, and usage context. For a tool with external dependencies and processing, more information is needed to be fully helpful.
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 does not explicitly mention parameters, but with 0% schema description coverage, it must compensate. It implies the 'url' parameter by stating 'Fetch a URL' and hints at 'depth' through 'run Sentrul's research-agent on the content' (suggesting analysis depth). However, it doesn't detail the enum values ('quick', 'full') or their effects, leaving some semantic gaps.
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 specific verbs ('fetch', 'run') and resources ('URL', 'Sentrul's research-agent'), and distinguishes it from siblings by specifying the research-agent focus (vs. compliance_scan or trace_lookup). It explicitly lists the outputs (summary, entities, claims, Langfuse trace URL).
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 the sibling tools (sentrul_compliance_scan, sentrul_trace_lookup). It mentions the tool's function but offers no context about alternatives, prerequisites, or exclusions, leaving the agent to infer usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve a public Langfuse trace by ID. Returns the structured trace payload for inspection in the client.
| Name | Required | Description | Default |
|---|---|---|---|
| trace_id | 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 a 'public' trace and returns a 'structured trace payload', but lacks details on permissions, rate limits, error handling, or what 'public' entails (e.g., access controls). This leaves significant gaps for a tool that likely involves data access.
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 with two sentences that are front-loaded and efficient. It avoids unnecessary details, though it could be slightly more structured (e.g., separating purpose from output).
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 low complexity (1 parameter, no output schema, no annotations), the description is somewhat complete but lacks depth. It covers the basic action and output intent, but misses behavioral aspects like authentication needs or error cases, making it adequate but with clear gaps.
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 minimal meaning beyond the input schema, which has 0% coverage. It implies 'trace_id' is used to look up a trace, but doesn't specify format, source, or constraints (e.g., length, pattern). With low schema coverage, the description doesn't fully compensate, so it meets the baseline for adding some context.
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 ('Retrieve') and resource ('public Langfuse trace by ID'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'sentrul_compliance_scan' or 'sentrul_research', which might have overlapping domains but different functions.
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 the sibling tools. It mentions 'for inspection in the client' but doesn't clarify scenarios, prerequisites, or exclusions, leaving usage context implied at best.
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.
v0.1.0sentrul_compliance_scansentrul_researchsentrul_trace_lookupScored across 3 tools
Each tool has a clearly distinct purpose with no overlap. sentrul_compliance_scan analyzes text for compliance patterns, sentrul_research processes web content for research insights, and sentrul_trace_lookup retrieves specific trace data. An agent can easily differentiate between scanning, researching, and trace lookup functions.
All tool names follow a consistent 'sentrul_' prefix with descriptive suffixes (compliance_scan, research, trace_lookup). The naming pattern is uniform and predictable, using snake_case throughout without any deviations or mixed conventions.
With only 3 tools, the set feels thin for a server named 'sentrul-mcp-server', which suggests a broader scope for compliance and research operations. While each tool is distinct, the count is borderline low, potentially limiting functionality for agents working in this domain.
The tools cover scanning, research, and trace lookup, but there are notable gaps for a compliance/research domain. Missing operations include updating or managing scans, configuring research parameters, or handling trace modifications, which could lead to dead ends in agent workflows.
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