local-llm-mcp
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- AlicenseAqualityAmaintenanceEnables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.21MIT
- AlicenseAqualityBmaintenanceEnables safe interaction with cloud LLMs by redacting sensitive entities into reversible placeholders, enforcing deterministic egress policies with human approval, and rehydrating responses so real data never leaves the process.4MIT
- AlicenseNot gradedqualityDmaintenanceExposes local Ollama instances as tools for Claude Code, allowing users to offload code generation, text drafting, and embedding tasks to local GPUs. It supports multi-turn conversations and model management through the Model Context Protocol.MIT
- FlicenseNot gradedqualityFmaintenanceA local DLP middleware that redacts sensitive information from prompts using local models before they reach external LLMs. It provides tools to sanitize queries, restore placeholders in responses, and manage a ledger of redactions to maintain data privacy.1-
- FlicenseBqualityBmaintenanceEnables AI agents to interact with local Ollama models for text generation and tool calling with prompt injection protection.5-
- AlicenseAqualityCmaintenanceEnables Claude Code to offload routine code generation and text processing tasks to a local Ollama LLM, saving Cloud API tokens and costs with automatic model selection and security features.11774Apache 2.0
TDQS
Scored across 3 tools
The three tools target distinct actions: listing models, redacting text, and asking the model. However, redact_text overlaps with ask_local's built-in output redaction, which could leave an agent unsure when to invoke redact_text directly versus relying on ask_local's automatic DLP.
All three names are verb-first and snake_case, which is predictable. list_local_models follows verb_adjective_noun while ask_local is verb_adjective and redact_text is verb_noun, a minor structural deviation but still readable and consistent in style.
Three tools is on the lean side but appropriate for a narrow local-LLM proxy whose purpose is model discovery, inference, and output DLP. Each tool earns its place, though the surface is minimal.
The core workflow (list models, ask, redact) is covered, but there are notable gaps: no model lifecycle operations (pull/delete), no conversation/session management, and no history handling. These are plausible for the scope but leave the surface thin for sustained use.