"author:1052666" matching MCP tools:

• searchCollaborators

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".

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• get_deploy_result

  quantifyme

  Wait for a `one_shot` deploy to finish and return its final result. `one_shot` returns a job_token immediately and the LIVE CARD already streams progress and renders the interactive backtest chart itself. Call this ONCE with the token to get the final numbers as TEXT so you can summarize them — it does NOT render another card (no need for get_model_chart). It BLOCKS until the deploy finishes (or ~2.5 min); on timeout it returns ok:false + pending:true — call it again with the same token. IMPORTANT: if `source == "community"`, the deploy used a PRE-EXISTING strategy by `@author` — tell the user that, share the `live_url` as the Live dashboard link, and ask whether they'd like to GENERATE A CUSTOM strategy instead. Use the `note` field as your guide. Args: job_token: the token returned by `one_shot`. Returns: dict with: ok, stem, model, live_url, symbol, timeframe, channels (list), stats:{ret, wr, pf, n, mdd} (out-of-sample test-split metrics — SHOW THESE), source ("community" | "generated"), author (community username if any), author_url + strategy_url (render @author and "pre-existing strategy" as those Markdown links), community_id, suggest_custom (bool), and note (a ready instruction — follow it). On failure: {ok:false, error} (or {pending:true}).

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• orcid_resolve_researcher

  orcid-mcp-server

  Disambiguate an author name to a verified ORCID iD. Returns up to 5 ranked candidates with transparent disambiguation signals: name match type (exact/partial/other-name/none), institution overlap flag, and whether a DOI or PMID anchor was used in the query. A DOI or PMID anchor is near-deterministic — it filters to researchers who have linked that specific work to their ORCID record. Use this tool (not orcid_search_researchers) when the input is an ambiguous name that needs ranked disambiguation. No synthetic scores are used — raw signals only.

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• orcid_search_researchers

  orcid-mcp-server

  Search the ORCID registry using structured field parameters or raw Solr syntax. All provided structured params are ANDed together. The `query` field appends raw Solr syntax to the generated clause. Returns ORCID iDs with inline name and institution data — no follow-up profile fetches needed for basic disambiguation. For ranked disambiguation of an ambiguous author name, use orcid_resolve_researcher instead. The ORCID Public API caps results at 10,000 — use pagination for large result sets.

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• openlibrary_get_cover_url

  openlibrary-mcp-server

  Resolve a cover image URL for a book or author photo. Returns a direct HTTPS URL in the requested size (S/M/L). The Covers API always returns HTTP 200 — missing covers return a 1×1 placeholder GIF, not a 404. URLs can be embedded in markdown as .

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• scholarfetch_author_papers

  ScholarFetch

  Expand one author into a deduplicated paper list. This is the main author->paper traversal tool and supports research filters. Use `author_id` when you already know the exact author, or `author_name` plus `candidate_index` after `scholarfetch_author_candidates`. Supported comma-separated `filters`: year>=YYYY, year<=YYYY, year=YYYY, has:abstract, has:doi, has:pdf, venue:<text>, title:<text>, doi:<text>. If you pass `engines`, it must include `openalex`.

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• verifyCitation

  Scholar Sidekick

  Verify a claimed citation against the resolved record at its identifier. Detects the dominant AI-driven fabrication pattern documented by Topaz et al. (Lancet 2026): a real, resolvable identifier (DOI / PMID / PMCID / arXiv / etc.) paired with a title that does NOT correspond to the paper at that identifier. Use when the user pastes a citation and asks 'is this real?' or 'check this DOI' — most fabricated citations resolve cleanly under doi.org but their cited title and the resolved title disagree. Single citation per call. Required: `title` plus exactly one identifier (doi, pmid, pmcid, isbn, arxiv, issn, ads, or whoIrisUrl). Optional refinements: author (first-author family name), year, container (journal). Set `screenWithLlm: true` to invoke the Stage 3 LLM screen on low-confidence mismatches (catches informal-abbreviation false positives); LLM access is gated to authenticated first-party keys and paid RapidAPI tiers — anonymous callers get 400 LLM_SCREEN_FORBIDDEN. Returns: { verdict: 'matched' | 'mismatch' | 'not_found' | 'ambiguous', confidence: 'high' | 'medium' | 'low', matched: <resolved record or null>, mismatches: [{field, claimed, resolved, similarity}], candidates: [{item, registries, score}] (when title-search ran), _provenance: {stages_run, resolved_via, registries_searched, llm_screen} }. Verdict semantics: 'matched' = claim agrees with resolved record; 'mismatch' = identifier resolves but title does not match (Topaz fabrication pattern); 'ambiguous' = identifier resolves to one paper but the claimed title matches a DIFFERENT paper found via title-search (CITADEL 'citation error' subtype — wrong identifier for a real paper); 'not_found' = neither the identifier nor the title resolves anywhere. No sibling tool overlaps: resolveIdentifier returns metadata for a known-good identifier; verifyCitation is the only tool that cross-checks claimed title vs resolved metadata. Read-only and idempotent — safe to retry. Works anonymously for the non-LLM path; the Stage 3 LLM screen requires authentication — set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) or use a paid RapidAPI tier. SCHOLAR_API_KEY also raises your rate limit.

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• open_document

  trident

  Opens a live Trident document and returns its full contents as Trident markup DSL — the human-readable text format used to author diagrams. Use this to READ and UNDERSTAND the diagram: its structure, labels, connections, and layout. Do NOT rely on this to enumerate entity IDs for programmatic use — the DSL can be very large and the output may be truncated. To get a complete, structured list of all entity IDs and counts, use get_document_summary instead. Requires a valid access token.

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• notes_recall

  dialogbrain

  Recall notes from your notebook. By default returns only your own notes (all scopes, newest first). Pass filter_agent_id=<int> to read another agent's notebook, or filter_agent_id="all" (or "*") to read across every agent in the workspace. Pass scope to narrow to global/thread/person. Each result includes agent_id and agent_name of the author.

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• find_strategy

  quantifyme

  Find an existing PROVEN strategy that matches a plain-English idea, so you can offer the user a choice — deploy the existing one, or generate a fresh custom one. Mirrors the quantifyme.ai landing experience: "Found <X> by @<author> (WR/PF) — Use it / Generate fresh". CALL THIS FIRST when a user describes a strategy idea. Then present the match (if any) and ASK which they want: • Use it → one_shot(community_id=<match.community_id>) — deploys the exact proven strategy (free, no generation). • Generate fresh → one_shot(prompt="<their description>") — Claude writes a brand-new custom strategy for them. If there's no match, just offer to generate fresh. Args: description: the user's strategy idea in plain English (e.g. "buy EURUSD 15min when RSI < 30, sell when RSI > 70"). symbol: optional pair to constrain the match (EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD). timeframe: optional granularity to constrain the match (1min/5min/15min/1h). Returns: dict with: - match: the best existing strategy, or null. When present: {community_id, title, username, wr, pf, ret, n_trades, symbol, timeframe}. Pass community_id to one_shot to deploy it unchanged. - description: echoed back — pass as one_shot(prompt=...) to generate fresh. - suggestion: a ready-to-show sentence offering the user the choice.

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• get_article

  library

  Fetch a single article from Psychiatry for Kids by slug. Returns title, body content, author, clinical reviewer, citations, and metadata.

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• request_payout

  bookstore4agents

  Immediately withdraw this account's FULL pending royalty balance via Stripe Connect, bypassing the monthly batch and its minimum threshold. This MOVES MONEY and the recipient bears the transfer fee. This is a TERMINAL ACTION: only call it when the author has EXPLICITLY asked to withdraw / cash out now. Do NOT call it just to check the balance — use payout_balance for that. Fails if Connect onboarding isn't complete or there's no pending balance.

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• connect_onboard

  bookstore4agents

  Start (or resume) Stripe Connect onboarding so this account can RECEIVE author royalties. Returns a one-time onboarding_url the human author must open in a browser to complete KYC. Required before a book can be published: an author with no payouts-enabled Connect account can save drafts but their books stay in draft until onboarding finishes. Payouts stay disabled until Stripe verifies the details — poll connect_status afterward.

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• openlibrary_get_work

  openlibrary-mcp-server

  Fetch a work by Open Library Work ID (OL…W). Returns title, description, subjects, cover IDs, and linked author IDs for follow-up lookups. Works represent the abstract book concept independent of any specific edition. Note: author names are not included — use openlibrary_get_author or openlibrary_search_books for names.

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• get_book

  Source Library

  Get a book's AI-generated summary, chapter list, edition metadata, DOI, and page counts. THIS IS THE RIGHT FIRST CALL whenever the user has named a specific author or work — the summary is typically a multi-paragraph orientation covering the book's argument, structure, and significance, often answering the question without any further searching. Pair with get_book_text to read selected chapters, or search_within_book to locate passages inside it.

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• discovery_analyze

  Disco

  Run Disco on tabular data to find novel, statistically validated patterns. This is NOT another data analyst — it's a discovery pipeline that systematically searches for feature interactions, subgroup effects, and conditional relationships nobody thought to look for, then validates each on hold-out data with FDR-corrected p-values and checks novelty against academic literature. This is a long-running operation. Returns a run_id immediately. Use discovery_status to poll and discovery_get_results to fetch completed results. Use this when you need to go beyond answering questions about data and start finding things nobody thought to ask. Do NOT use this for summary statistics, visualization, or SQL queries. Public runs are free but results are published. Private runs cost credits. Call discovery_estimate first to check cost. Private report URLs require sign-in — tell the user to sign in at the dashboard with the same email address used to create the account (email code, no password needed). Call discovery_upload first to upload your file, then pass the returned file_ref here. Args: target_column: The column to analyze — what drives it, beyond what's obvious. file_ref: The file reference returned by discovery_upload. analysis_depth: Search depth (1=fast, higher=deeper). Default 1. visibility: "public" (free) or "private" (costs credits). Default "public". title: Optional title for the analysis. description: Optional description of the dataset. excluded_columns: Optional JSON array of column names to exclude from analysis. column_descriptions: Optional JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious (e.g. {"col_7": "patient age", "feat_a": "blood pressure"}). author: Optional author name for the report. source_url: Optional source URL for the dataset. use_llms: Slower and more expensive, but you get smarter pre-processing, summary page, literature context and pattern novelty assessment. Only applies to private runs — public runs always use LLMs. Default false. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

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• createMock

  Keploy

  POST /apps/{appId}/recordings/{testSetId}/mocks — Author one mock under a recording — Insert a single mock into the given test set. When `branch_id` is supplied, the mock lands on that branch's overlay (`branch_sandbox_ops`) and only surfaces to main on merge. Without `branch_id` the mock writes straight to main — same behaviour as the recording-driven agent path. Authoring shape — pick ONE: - **`mock_yaml`** (PREFERRED) — paste the canonical mock YAML envelope (`version` / `kind` / `name` / `spec` with the per-kind payload, exactly as it lives in `mocks.yaml` on disk). The server decodes via OSS DecodeMocks so kind- specific Spec contents (`req`, `resp`, `metadata`, …) round-trip without field-name loss. This is the only path that preserves payloads pasted from existing mocks. - **`mock`** — typed OSS Mock JSON object. Brittle: the OSS struct uses PascalCase JSON tags (`Metadata`, `Req`, `Res`), so lowercase canonical keys are silently dropped. Use only when authoring programmatically from typed Go shapes. When both are sent, `mock_yaml` wins. Requires scope: `write`.

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• list_books

  Source Library

  Browse the catalog by metadata — filter by author/title fragment, language, category, or translation recency. Returns books with title, author, language, year, and translation progress. Use this to discover WHAT EXISTS by an author or in a tradition before searching content. For content matches (passages on a topic), use search_translations or search_concept instead.

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• search_papers

  Arxiv

  Search arXiv preprints. Plain text searches all fields; use prefixes for targeted queries: au:hinton (author), ti:transformer (title), abs:diffusion (abstract), cat:cs.AI (category), all:quantum (any field). Combine with AND/OR/ANDNOT, e.g., "ti:transformer AND cat:cs.LG". Returns id, title, authors, abstract, categories, published date, PDF URL.

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• searchPapers

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".

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