case-doha-record

Search 30,000+ decided U.S. DOHA security-clearance decisions, cited to the public record.

사용해야 할까요

품질 및 안전성

A
설명 품질
100%
스키마 완전성
83%
이름 품질
97%
오염 위험
100%
권한 일치
100%
프로토콜 준수
100%

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~951토큰 (도구 정의)
~690 B일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 0.74%)

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": {
    "case-doha-record": {
      "url": "https://www.clearancesearchengine.com/api/mcp"
    }
  }
}

원격 엔드포인트

https://www.clearancesearchengine.com/api/mcpstreamable-http

할 수 있는 일

도구 목록

도구 (7)

🟢 읽기 전용🟡 쓰기🔴 삭제⚪ 알 수 없음
🟢search_cases(query, guideline, outcome, level, year_from, ...)

Search 30,000+ decided public DOHA security-clearance decisions (1996 to present). Full-text query plus filters. Returns matching cases with outcome, date, guidelines, and a citable URL each.

입력 스키마

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Full-text search, e.g. \"gambling debts sports betting\""
    },
    "guideline": {
      "type": "string",
      "description": "Single guideline letter A-M (e.g. F = financial, B = foreign influence)"
    },
    "outcome": {
      "type": "string",
      "enum": [
        "granted",
        "denied"
      ],
      "description": "Final outcome filter"
    },
    "level": {
      "type": "string",
      "enum": [
        "hearing",
        "appeal"
      ],
      "description": "Decision level"
    },
    "year_from": {
      "type": "integer",
      "description": "Earliest decision year, e.g. 2020"
    },
    "year_to": {
      "type": "integer",
      "description": "Latest decision year"
    },
    "limit": {
      "type": "integer",
      "description": "Max results, 1-25 (default 10)"
    }
  }
}
🟢get_case(case_id)

Fetch one decided DOHA case in full: what was alleged, the judge's findings per allegation, per-guideline formal findings, outcome, judge, representation, and appeal history. Use case_id from search_cases.

입력 스키마

{
  "type": "object",
  "properties": {
    "case_id": {
      "type": "integer",
      "description": "The numeric case id from search_cases"
    }
  },
  "required": [
    "case_id"
  ]
}
🟢similar_cases(case_id, limit)

The most similar decided cases to a given case, ranked by how alike the ALLEGATIONS read (never by outcome). Same list shown on the case page.

입력 스키마

{
  "type": "object",
  "properties": {
    "case_id": {
      "type": "integer",
      "description": "The numeric case id"
    },
    "limit": {
      "type": "integer",
      "description": "Max results, 1-10 (default 5)"
    }
  },
  "required": [
    "case_id"
  ]
}
🟢get_statistics(by, guideline, year_from, year_to)

Grant/denial statistics over decided hearing-level DOHA cases, grouped by year or by guideline, always with sample sizes. Same population rules as the site's Insights page.

입력 스키마

{
  "type": "object",
  "properties": {
    "by": {
      "type": "string",
      "enum": [
        "year",
        "guideline"
      ],
      "description": "Grouping (default year)"
    },
    "guideline": {
      "type": "string",
      "description": "Optional single guideline letter A-M to scope to"
    },
    "year_from": {
      "type": "integer"
    },
    "year_to": {
      "type": "integer"
    }
  }
}
🟢get_timelines(year)

Measured DOHA timelines: median days from Statement of Reasons to hearing and to decision, from dates stated in the decisions themselves. Optionally scoped to one decision year.

입력 스키마

{
  "type": "object",
  "properties": {
    "year": {
      "type": "integer",
      "description": "Optional decision year to scope to"
    }
  }
}
🟢get_conduct_recency(guideline)

How much time had passed between the most recent conduct and the decision, measured from dates stated in the decisions, for the incident-type concerns (drugs, alcohol, criminal conduct, sexual behavior, protected information, IT misuse). Reports median years before favorable vs unfavorable decisions, with counts. Descriptive association, never a prediction.

입력 스키마

{
  "type": "object",
  "properties": {
    "guideline": {
      "type": "string",
      "description": "Optional single concern letter to scope to: H drugs, G alcohol, J criminal, D sexual behavior, K protected information, M IT misuse"
    }
  }
}
🟢get_candor_outcomes

How cases with a candor allegation (falsification, omission, or lack of candor) were resolved in the decided record: the favorable rate with vs without such an allegation, when the judge found a deliberate falsification vs when the applicant rebutted it, and when the applicant corrected the record before being confronted. Counts and denominators throughout. Descriptive, never a prediction.

입력 스키마

{
  "type": "object",
  "properties": {}
}

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