"Weights & Biases" matching MCP servers:

Enables querying W\&B Weave traces and creating W\&B Reports with text and visualizations through natural language.

Enables LLM agents to query Weights & Biases experiments, including listing projects, runs, metrics, plotting metrics, and retrieving run details.

Enables running ML experiments from a local laptop by coordinating GitHub for code, Kaggle for data and GPU training, Weights & Biases for tracking runs, and Google Drive for small artifacts.

An MCP server that lets AI agents create and manage LighterPack packing lists — add gear, track weights, organize categories, and share lists, all through natural conversation.

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.

Audits decisions for cognitive biases through an adversarial framework, exposing tools like analyze_decision, list_biases, and get_bias via MCP.

Read-only access to TrailWeights' ultralight gear corpus — verified weights, creator video reviews, pack templates, and semantic gear search. Auth: none.

Enables AI assistants to connect to a trading journal, analyze performance metrics, identify psychological biases, and receive continuous trading coaching.

Enables auditing scientific papers for methodological biases such as selection bias and p-hacking, and assessing citation credibility and research consensus.

Enables querying authoritative product data from GS1 Brasil's Verified by GS1 API via GTIN, including brand, description, classifications, images, weights, dimensions, and licensee details, with support for national and international bases and batch enrichment.

URL intelligence for AI agents. One URL in, structured security and data quality signals out across 7 dimensions. 13 tools, risk score 0-100 with 23 configurable weights.

A decision structure analysis engine that transforms emotional dilemmas into structured frameworks by identifying variables, constraints, and strategy paths. It helps users evaluate risk distributions and cognitive biases without offering subjective advice or definitive answers.

Enables enhanced decision-making through hierarchical LLM analysis, using three specialized critique agents (positive, neutral, negative) that analyze proposals in parallel and synthesize them into comprehensive, actionable insights. Helps overcome single-model biases by providing multi-perspective evaluation of complex ideas and proposals.

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 browsing the full ingredient lists of two build-your-own cereal mixers (müsli & granola and porridge), returning composition, allergens, prices, portion weights, dietary properties and per-100g nutrition values grouped by category. Supports filtering by category and an optional lighter mode that omits detailed composition and allergen data.

A personal calorie and protein counter MCP server for use inside claude.ai, enabling users to log meals from photos or text, track weight, and view an interactive dashboard.