# case-doha-record MCP server

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

## Links
- Registry page: https://www.getdrio.com/mcp/io-github-klmlfl-case-doha-record
- Repository: https://github.com/klmlfl/dohasearch
- Website: https://www.clearancesearchengine.com/developers

## Install
- Endpoint: https://www.clearancesearchengine.com/api/mcp
- Auth: Not captured

## Setup notes
- Remote endpoint: https://www.clearancesearchengine.com/api/mcp

## Tools
- search_cases - 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. Endpoint: https://www.clearancesearchengine.com/api/mcp
- get_case - 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. Endpoint: https://www.clearancesearchengine.com/api/mcp
- similar_cases - 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. Endpoint: https://www.clearancesearchengine.com/api/mcp
- get_statistics - 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. Endpoint: https://www.clearancesearchengine.com/api/mcp
- get_timelines - 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. Endpoint: https://www.clearancesearchengine.com/api/mcp
- get_conduct_recency - 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. Endpoint: https://www.clearancesearchengine.com/api/mcp
- 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. Endpoint: https://www.clearancesearchengine.com/api/mcp

## Resources
Not captured

## Prompts
Not captured

## Metadata
- Owner: io.github.klmlfl
- Version: 1.0.0
- Runtime: Streamable Http
- Transports: HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: Jul 18, 2026
- Source: https://registry.modelcontextprotocol.io