obsify
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Alternatives to obsify
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Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables AI coding assistants to audit datasets against DAMA-DMBOK quality dimensions, sanitize PII, generate differential-privacy synthetic twins, compress prompt context, and export dbt schema tests and interactive HTML reports. It supports development against sensitive data with zero real records exposed to external LLMs.1MIT
- AlicenseNot gradedqualityDmaintenanceLet LLMs analyze sensitive data safely by querying a tokenized, join-preserving copy of the database, with fail-closed PII scanning and provable numeric equivalence.MIT
- FlicenseNot gradedqualityBmaintenanceEnables agents to encrypt and sanitize sensitive data such as credentials, credit card numbers, and addresses locally before dispatching prompts to cloud LLMs, then rehydrate the masked values client-side. It runs deterministically with zero external dependencies over MCP JSON-RPC stdio, so no plaintext PII leaves the user's machine.7-
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to access only task-relevant data with secrets hidden and personal details pseudonymized, while enforcing destination policies to prevent data leakage even under prompt injection.Apache 2.0
- AlicenseAqualityBmaintenanceSelf-hosted governance layer between an AI assistant and your data: allow/deny policy, deterministic PII masking, row caps, and a hash-chained audit log with an Ed25519-signed receipt for every access, verifiable offline.4120 npm3MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to browse and read files from allowlisted folders while names, IDs, contacts, and secrets are replaced with consistent tokens locally before the agent ever sees them. The agent's final answer can then be restored to the original values locally, with no write tools and no file contents leaving the machine.37 npmMIT
TDQS
Scored across 5 tools
Each tool has a clear, distinct purpose: make_synthetic_twin creates a fake dataset, run_on_real executes code against real data, scan_pii identifies PII locations, redact_text masks PII in text, and verify_value_free checks for forbidden terms. No two tools overlap in what they accomplish.
Most tools follow a verb_noun pattern (make_synthetic_twin, scan_pii, redact_text, verify_value_free), but run_on_real breaks the pattern with a prepositional phrase. The style is consistent (all snake_case, verbs first) but the deviation is noticeable.
With 5 tools, the count is well-scoped for a focused PII-handling server. Each tool covers a necessary step in the workflow, and the count is within the typical 3-15 range, though a few additional helpers could be justified (e.g., a check for twin accuracy).
The tool surface covers the core lifecycle: protect data (scan, redact, verify) and enable safe analysis (twin, run on real). Minor gaps exist, such as no tool to validate the synthetic twin's fidelity against the real file, and verify_value_free lacks a positive counterpart, but agents can work around these.