"Weights & Biases" matching MCP servers:

An MCP server that enables LLMs to detect cognitive biases and logical fallacies in text, list supported biases, and provide links to relevant psychology tests.

Enables creating and managing packing slips for shipments, tracking cartons, contents, weights, chargeable weight, and shortfalls, with reports and packing slip generation.

Enables querying a Tonal fitness account for workout history with per-set weights, muscle readiness, strength scores, and lifetime stats through a remote MCP endpoint.

C++26 paper reference MCP with lookup_paper, fuzzy search, and compiler_status tools over a 216-paper ISO/IEC 14882:2026 corpus. Backs a Claude Code plugin that biases the model toward final-form C++26 idioms (reflection, contracts, std::execution senders, expansion statements, #embed).

Checks a social post against a platform's real character limit before it ships — X bills every URL at 23 characters and weights non-Latin script at 2, while Bluesky and Mastodon count grapheme clusters. Covers X, X Premium, Bluesky, LinkedIn, Threads, Mastodon, and Discord, with no credentials and no network access.

An MCP server for chemistry: it computes molecular weights and isotope distributions from chemical formulae and resolves compound name/formula lookups, backed by NIST atomic-weight data and a cached finder over multiple online and offline sources.

Enables auditing the AI models and SDKs in a repository, drafting evaluation examples, ranking candidates by cost per passing answer, and generating cheapest-first routing configurations, all through read-only local tools that never make network calls. Also estimates monthly token spend and local weights/KV-cache memory so users can pick and size the cheapest model that passes their own tests.

Enables users to look up Chinese GB standard steel section properties and calculate theoretical weights for hot-rolled H-beams, I-beams, channels, angles, C/Z purlins, hollow sections, welded I/box sections, plates, bars, pipes, and tubes. It also provides category listing, designation search, exact lookup, and material density tools.

Checks whether an open-weight LLM fits your GPU: VRAM for weights, KV cache and overhead at each quantisation, a tokens-per-second ceiling, the longest context that fits, and what would work instead when it doesn't. Reads any Hugging Face repo's config.json. Free, read-only, no API key.

Enables reading training history and planning complete workouts on a personal calendar, including exercises with videos, sets, reps, per-set weights, supersets, notes, and block instructions. It also supports searching the exercise library, copying or moving sessions, and retrieving performance stats, personal records, and feedback.

Enables zero-dependency INT8 symmetric quantization of LLM weights and activations on a per-tensor or per-channel basis, reporting SNR and MSE reconstruction telemetry along with TTFT/TPOT latency and jitter metrics. Also exposes edge inference primitives such as PagedAttention block allocation, radix prefix caching, and speculative decoding verification through the Model Context Protocol.

Enables users to record probabilistic judgments in an immutable ledger, automatically score them against outcomes, and analyze systematic biases across six classification layers. It supports natural-language interaction for logging predictions, checking randomness, and auditing data integrity while avoiding advice or replacing user judgment.