Run a forward pass of a small-to-medium open-weight language model (via TransformerLens) on one prompt, and report which attention heads and MLP neurons were most responsible for its predicted next token: heads ranked by direct logit attribution, neurons ranked by activation magnitude at the final prompt position. Call this to answer 'why did the model predict X' for a specific prompt. It only works on models TransformerLens's HookedTransformer.from_pretrained supports (GPT-2, Pythia, Llama, Gemma, Qwen, and similar open-weight checkpoints), not closed-source APIs like OpenAI or Anthropic models. Read-only and deterministic for a given model, prompt, and top_k: it writes nothing except the model's own weights, which HuggingFace Hub downloads to a local cache (~/.cache/huggingface) the first time a given model name is requested (needs network access that one time; later calls for the same model run offline from cache). Runs on CPU by default and can be slow for large models. On failure (an unsupported model name, or a prompt longer than the model's context window) it returns a structured error object instead of raising, so the tool call itself never fails silently. Parameters: model (str) is any name HookedTransformer.from_pretrained accepts, e.g. 'gpt2' or 'EleutherAI/pythia-70m'; prompt (str) is the input text; top_k (int, default 10) caps how many top heads and neurons are returned. Example call: model='gpt2', prompt='The capital of France is Paris. The capital of Japan is', top_k=5. Returns JSON with schema_version, operation, model (resolved name, backend, device, and layer/head/dimension counts), prompt, predicted_token, predicted_token_id, top_neurons (list of {layer, neuron_index, activation}), and top_heads (list of {layer, head_index, logit_attribution}).

Dump raw per-layer activation summary statistics (shape, mean, std, min/max, and the max-activating sequence position) for one prompt run through an open-weight TransformerLens-supported model, covering every layer's residual stream, MLP neuron activations, and attention pattern. Call this when trace's top-k ranking isn't enough detail and you need the raw scale/shape of a specific hook point before deciding what to inspect further or patch with the patch tool. Same model constraint as trace: only models HookedTransformer.from_pretrained supports. Read-only and deterministic for a given model and prompt; the only side effect is HuggingFace Hub caching the model weights locally on first use of a given model name, which needs network access that one time. Runs on CPU by default. Output size scales with model depth since it returns stats for every layer, not a top-k subset, so it can be verbose for large models. On failure (unsupported model name, prompt too long for the context window) it returns a structured error object rather than raising. Parameters: model (str), any name HookedTransformer.from_pretrained accepts, e.g. 'gpt2'; prompt (str), the input text. Example call: model='gpt2', prompt='The capital of France is Paris. The capital of Japan is'. Returns JSON with schema_version, operation, model, prompt, n_tokens, and activations (list of {hook_name, layer, shape, mean, std, max_value, max_position, min_value}, one entry per hook point).

Zero-ablate one component (a single transformer block's layer plus a component type such as an attention output or MLP output) in an open-weight TransformerLens model's forward pass, and report how the predicted token and its logit changed relative to the unablated baseline. This is a minimal causal intervention: use it to test whether a component trace or circuit flagged as correlated with a prediction is actually causally responsible for it. Call it after trace or circuit has surfaced a candidate layer/component; it does not search for candidates itself. Read-only in the sense that it writes no files and the ablation only affects that single in-memory forward pass, nothing persists across calls; the same HuggingFace model-weight caching and CPU-by-default notes as trace apply. Deterministic for a given model, prompt, layer, and component. On failure it returns a structured error object instead of raising: an out-of-range layer raises LayerOutOfRangeError, an unsupported model name raises UnsupportedModelError, and a prompt exceeding the context window raises PromptTooLongError, all surfaced the same way. Parameters: model (str); prompt (str); layer (int), the zero-indexed transformer block to patch; component (str), one of resid_pre, resid_mid, resid_post, attn_out, mlp_out, mlp_post. Example call: model='gpt2', prompt='The capital of France is Paris. The capital of Japan is', layer=9, component='attn_out'. Returns JSON with schema_version, operation, model, prompt, layer, component, ablation_type ('zero'), baseline_predicted_token, baseline_predicted_token_id, baseline_top_logit, patched_predicted_token, patched_predicted_token_id, patched_top_logit, logit_delta, and prediction_changed (bool).

Sketch a best-effort automated circuit for one prompt on an open-weight TransformerLens model: ranks candidate attention heads and MLP neurons by direct logit attribution, then measures each candidate's individual causal effect via single-component zero-ablation, so the result reflects components that actually move the prediction, not just ones correlated with it. Call this when trace's correlational ranking isn't enough and you want a causal pass across multiple candidates without manually calling patch on each one. This is NOT full path-patching with clean/corrupted prompt pairs and does not capture interaction effects between components; the response's own method field restates this caveat so a caller doesn't have to trust prose alone. For rigorous transcoder-based circuit discovery on a fixed set of supported models, use a dedicated tool such as Anthropic's circuit-tracer instead. Read-only, with the same model-weight caching, network-on-first-use, and CPU-by-default behavior as trace; more expensive than trace since it runs one extra forward pass per candidate component being ablated. Deterministic for a given model, prompt, and top_k. On failure it returns a structured error object rather than raising. Parameters: model (str); prompt (str); top_k (int, default 10), how many top-attributed components to test via ablation. Example call: model='gpt2', prompt='The capital of France is Paris. The capital of Japan is', top_k=5. Returns JSON with schema_version, operation, model, prompt, predicted_token, predicted_token_id, components (list of {layer, component_type: 'head' or 'neuron', index, logit_drop_on_ablation}), and method (a string explaining the approximation).