Weights & Biases MCP Server

The Weights & Biases MCP Server bridges the gap between AI assistants and the rich ecosystem of experiment tracking, model monitoring, and collaborative analytics that W&B provides. By exposing a set of declarative tools over the Model Context Protocol, it allows an LLM to ask natural‑language questions such as “Show me the top 5 runs by accuracy in wandb-smle/hiring-agent-demo-public” or “How did the latency of my hiring agent predict traces evolve over the last months?” and receive structured, real‑time answers that are grounded in the underlying run data. This eliminates the need for developers to write custom API calls, SQL queries, or shell scripts; instead they can interact with their experiment data conversationally.

At its core the server offers six powerful tools that map directly to common W&B workflows. The query_wandb_tool lets an assistant pull runs, metrics, and hyperparameters; the create_wandb_report_tool programmatically generates visual reports that can be embedded in dashboards or shared with stakeholders; and the query_wandb_entity_projects tool lists all projects under an entity, making it easy to navigate large organizations. For LLM‑centric experiments, query_weave_traces_tool and count_weave_traces_tool provide analytics on trace latency, failure rates, and storage usage. Finally, query_wandb_support_bot gives instant access to W&B documentation and best‑practice guidance, turning the assistant into a first‑line support agent.

The server is particularly valuable for data scientists and ML engineers who routinely sift through dozens of runs, compare hyperparameter sweeps, or troubleshoot production latency spikes. In a typical scenario, an engineer can ask the assistant to “compare the decisions made by the hiring agent last month” and receive a W&B report that visualizes key metrics, or request “how many traces failed in the last 100 runs of grpo-cuda/axolotl-grpo” and get an immediate count. By integrating directly into the LLM’s tool‑use pipeline, the MCP Server removes friction from exploratory analysis, enabling rapid hypothesis testing and faster iteration cycles.

Because it operates over the MCP, the server fits seamlessly into existing AI‑centric workflows. Any LLM that supports tool calling—Claude, OpenAI GPT-4o, Gemini, or others—can invoke these W&B tools without modification. The server also enforces best practices: it requires explicit project and entity names to avoid ambiguous queries, encourages specificity to reduce hallucinations, and can validate that all relevant data has been retrieved before returning a final answer. These safeguards make conversational analytics both reliable and reproducible.

In summary, the Weights & Biases MCP Server transforms experiment data into conversational knowledge. By providing a rich set of declarative tools that map to everyday W&B operations, it empowers developers to ask questions in plain language and receive actionable insights instantly. This capability accelerates debugging, reporting, and collaboration across ML teams, making it a standout component in any AI‑powered data science stack.