Dataverse Harvard

Quality – / 100not scored

Not scored: fewer than three checks apply, usually because no remote answered a credential-free probe or the server is a local package.

Tool list unknown · tools last changed 2026-10-18 · checked 2026-10-20 13:17 UTC

Tool changes

• 2026-10-18dataverseremoved

  Dataverse (collection) metadata by alias or id.

• 2026-10-18dataset_filesremoved

  List all files in the latest version of a Harvard Dataverse dataset identified by its DOI persistent ID (e.g. "doi:10.7910/DVN/..."), returning file names, content types, sizes, and download URLs. For files Dataverse has TABULAR-INGESTED (CSV/DTA/SAV/POR under its ingest size limit), also returns exact COLUMN / VARIABLE NAMES and labels from Dataverse's DDI metadata (variables_by_file). If a dataset has no tabular-ingested files (common for very large files, or non-tabular formats), column names are not available from Dataverse at all — the response says so explicitly rather than silently omitting them.

• 2026-10-18datasetremoved

  Full metadata for one Harvard Dataverse dataset by its DOI persistent id: title, authors, abstract, subject, keywords, version and publication date. The id must come from search — DOIs cannot be guessed.

• 2026-10-18searchremoved

  Keyword search across Harvard Dataverse — the ~200k-dataset open research repository. Returns dataset titles, DOIs, authors, publication dates and descriptions; pass a returned DOI to dataset or dataset_files. Use to find research data behind a paper or topic (e.g. "climate", "survey experiment", "replication data").

• 2026-10-18forgetremoved

  Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

• 2026-10-18rememberremoved

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

• 2026-10-18scan_competitor_ai_presenceremoved

  Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

• 2026-10-18ai_visibility_checkremoved

  Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

• 2026-10-18generate_llms_txtremoved

  Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

• 2026-10-18classify_rowremoved

  Cheap categorization: given a company and a list of candidate labels, decide which labels apply — the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, evidence_url, quote, status: found|inferred|not_established} — "found" only when the model's quote is independently verified as a verbatim substring of the text used. Pass text directly (cheapest — no fetch) or entity.domain to fetch the homepage once as a fallback. No text and no domain means every label is not_established with no model call made.

Showing the latest 10. Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.