case-doha-record
Search 30,000+ decided U.S. DOHA security-clearance decisions, cited to the public record.
Sollte ich dies verwenden
Qualität und Sicherheit
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"case-doha-record": {
"url": "https://www.clearancesearchengine.com/api/mcp"
}
}
}Remote-Endpunkte
https://www.clearancesearchengine.com/api/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (7)
🟢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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"type": "object",
"properties": {}
}Community
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