system-one-mcp
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- AlicenseNot gradedqualityAmaintenanceExposes three typed-judgment tools over MCP stdio, letting a host ask yes/no, choice, and score questions and receive calibrated probabilities rather than prose. Runs entirely offline against a mock provider by default, with live TypeSafe or OpenRouter routes and a disclosed, per-feature egress contract when explicitly enabled.66 npmApache 2.0
- AlicenseNot gradedqualityAmaintenanceExposes three stdio tools that ask typed judgment questions — yes/no, choice, and score — and return calibrated probabilities instead of prose, over any MCP-speaking host. Runs on an offline mock by default, with every network transmission named before it happens and credential-gated live routes to TypeSafe or OpenRouter.66 npmApache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables any MCP host to obtain typed judgments — yes/no, choice, and score — with calibrated probabilities instead of prose, exposed as three tools over stdio. It runs fully offline with synthetic answers by default, and only transmits state to TypeSafe or OpenRouter when live routing is explicitly configured, reporting exactly what leaves at startup.66 npmApache 2.0
- AlicenseAqualityBmaintenanceEnables any stdio MCP client to send evidence and allowed answers to a locally running Nimble model through Ollama and receive typed, validated results as choices, probabilities, or rubric scores. Supports three question types and includes diagnostics that check configuration, endpoint, and model availability without running inference.22MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI tools to uniformly discover, inspect, and call tools, prompts, and resources from multiple upstream MCP servers through a small set of fixed MCP tools, over stdio or HTTP.2MIT
- AlicenseNot gradedqualityAmaintenanceExposes three stdio tools that answer typed questions — yes/no, choice, and score — with calibrated probabilities instead of prose, letting any MCP host query a judgment model without a chat interface. Runs fully offline on a synthetic mock by default, with named, per-feature egress disclosure and redaction only when a live provider is explicitly configured.66 npmApache 2.0
TDQS
Scored across 2 tools
The two tools serve clearly distinct purposes: one enumerates available models and the other runs an evaluation with a chosen model. There is no overlap in resource or action, so an agent cannot realistically confuse them.
Both names share the 'systemone_' snake_case prefix, which is predictable and readable. The minor deviation is that 'evaluate' is a bare verb without a noun object unlike 'list_models', but the set still feels coherent.
Two tools is thin even for a narrow evaluation-model domain; there is no separate model-detail, configuration, or result-retrieval tool. It is borderline rather than mismatched, since the core discover-then-evaluate flow is present.
The surface covers the minimal loop of listing models and evaluating a question, but lacks supporting operations such as fetching model metadata, setting backend configuration, or retrieving/batching prior evaluations. Agents can work around this only by relying on external configuration.