mcp-server

Your company's brain for AI agents. Cited, permission-aware knowledge across every system.

사용해야 할까요

품질 및 안전성

A
설명 품질
100%
스키마 완전성
97%
이름 품질
100%
오염 위험
100%
권한 일치
100%
프로토콜 준수
100%

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~787토큰 (도구 정의)
~1.2 KB일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 0.61%)

이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.

설치

원클릭 설치

`claude_desktop_config.json` 파일에 다음을 추가하세요:

{
  "mcpServers": {
    "mcp-server": {
      "url": "https://mcp.quelvio.com/http"
    }
  }
}

원격 엔드포인트

https://mcp.quelvio.com/httpstreamable-http

할 수 있는 일

도구 목록

도구 (3)

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🟢query_knowledge(query, mode, max_sources, domain)

Search the company's connected knowledge across every source — Drive, SharePoint, Confluence, Slack, Notion — with cited synthesized answers, lifecycle awareness, and refusal-on-weak-context. Returns a written answer with [n] citations plus the ranked source chunks. Modes: `fast` (1,500 kT — retrieval-only, no synthesis), `standard` (12,500 kT — default; synthesized answer over the top retrieval set), `deep` (25,000 kT — wider retrieval + premium synthesis for complex questions). Pick the cheapest tier that answers the question. Responses are capped at 25,000 output tokens per Claude Connectors policy; if truncated, structured metadata carries `truncated: true` and `query_id` so the agent can call `get_source_detail` for full provenance.

입력 스키마

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural-language query (1–2000 characters). Be specific — results are ranked by authority + relevance, not keyword overlap."
    },
    "mode": {
      "type": "string",
      "description": "fast | standard | deep. Defaults to `standard`. `fast` returns chunks only (no synthesized answer); `standard` and `deep` return a synthesized answer with [n] citations + the source chunks.",
      "enum": [
        "fast",
        "standard",
        "deep"
      ]
    },
    "max_sources": {
      "type": "number",
      "description": "Number of source chunks to return (1–20, default 5). The 25K token cap may force fewer results regardless of this value.",
      "minimum": 1,
      "maximum": 20
    },
    "domain": {
      "type": "string",
      "description": "Optional taxonomy domain filter (e.g. 'engineering.platform'). Use `list_domains` to discover valid values for the tenant."
    }
  },
  "required": [
    "query"
  ]
}
🟢list_domains(coverage_filter)

List the taxonomy domains the company has indexed — with document counts, expert counts, and coverage levels — so an agent can decide whether to query before spending a Knowledge Token. Returns one row per domain with the canonical `taxonomy_domain` slug, document/chunk counts, expert count, coverage level (expert | partial | none), the single_expert risk flag, and the top contributor by authority. Use the slug as the `domain` filter on a follow-up `query_knowledge` call. Zero Knowledge Tokens consumed.

입력 스키마

{
  "type": "object",
  "properties": {
    "coverage_filter": {
      "type": "string",
      "description": "Optional comma-separated subset of expert,partial,none. Default: all three. Unknown tokens 400."
    }
  },
  "required": []
}
🟢get_source_detail(query_id)

Return per-chunk source provenance for a previous query — document path, lifecycle state, embedding timestamp, contributor, last-updated — useful for verifying a citation or surfacing trust signals to a downstream system. Pass a `query_id` returned by an earlier `query_knowledge` call. Returns 404 if the query_id is unknown OR belongs to a different tenant (indistinguishable to prevent info-leak). Zero Knowledge Tokens consumed.

입력 스키마

{
  "type": "object",
  "properties": {
    "query_id": {
      "type": "string",
      "description": "UUID returned in the structured-metadata block of a prior `query_knowledge` response. Tenant-scoped — cross-tenant 404."
    }
  },
  "required": [
    "query_id"
  ]
}

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