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171,435 tools. Last updated 2026-06-04 03:20

"author:0x67108864" matching MCP tools:

  • 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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  • 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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  • Find specific PASSAGES inside books — returns page-level snippets with citation URLs. Use this when you want a quote or evidence on a topic across the whole library. ORIENTATION HINT: if the user has named a specific author or work, prefer get_book (returns a summary + chapter outline) over passage hunting — every book in the corpus has an AI-generated summary that is usually the right first read. Use search_translations when sweeping across many books for evidence of a theme. For finding which BOOKS cover a topic, use search_library. Query tips: single distinctive terms ("memory palace", "wax tablet") work best; multi-word natural-English queries ("unity of the intellect") may return fewer results because matching is term-based, not phrase-based. Each snippet has a snippet_type — "translation"/"ocr" means it is a verbatim extract from the source text; "summary" means it is AI-generated description (do not quote those as the author's words). Response includes total_matches, returned, and offset for pagination. Cross-cultural tip: for pre-modern or non-Western topics, search source-tradition vocabulary rather than modern English terms — e.g. for seminal economy search "jing" or "bindu" or "istimnāʾ", not "semen retention"; for female homoeroticism search "tribade" or "sahq", not "lesbian". The corpus is indexed via period translations that use tradition-internal terminology.
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  • 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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  • 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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  • 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 ![cover](url).
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  • Read and author HiveLearn communities, courses, events, quizzes, and certificates.

  • Search Google Scholar for academic papers, citations, and author profiles.

  • Conceptual / semantic passage search across the whole library. Use when the modern term won't literally appear in historical texts — e.g. "distributed cognition" maps to passages about active intellect, art of memory, wax tablet metaphors; "social contract" maps to pre-Hobbesian discussions of consent and authority. Ranks passages by cosine similarity on Gemini embeddings (768d), so paraphrases and conceptually adjacent phrasings match even when no keyword overlaps. ORIENTATION HINT: if the user named a specific author or work, prefer get_book (returns the book's AI summary + chapter outline) — semantic search is expensive and best reserved for cross-corpus discovery. Prefer search_translations for literal phrases or distinctive single terms; use search_concept when the concept matters more than the wording. Similarity calibration: 0.70+ is a strong match, 0.55–0.70 is worth reading but verify, below 0.55 is mostly conceptual drift. Set max_per_book to diversify results across many books rather than cluster on one source. Each passage carries a snippet_type — quote only "translation" snippets, never "summary". Cross-cultural tip: for pre-modern or non-Western topics, also try source-tradition vocabulary — e.g. for seminal economy try "jing preservation" or "bindu yoga" or "istimnāʾ"; for masturbation try "mollities" (Latin) or "hastamaithuna" (Sanskrit) or "shouyin" (Chinese). The corpus is indexed via period translations that use tradition-internal terminology, so adjacent/euphemistic terms often surface material that modern English keywords miss.
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  • 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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  • 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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  • Generate one chained-CRUD API test for a single resource. Behavior depends on the app's devloop_storage_mode (set this first via devloop_resolve_storage / devloop_set_storage_mode): * repo mode → returns a PLAYBOOK for you to walk. Steps: (1) run "keploy test-gen generate-from-code --app-dir <dir> --resource <name>" to scaffold the directory + empty config.yaml; (2) use your Write tool to author keploy/api-tests/<resource>/test.yaml using the schema returned by devloop_detect_app; (3) run "keploy test-gen run --test-dir keploy/api-tests --suite <Name>_CRUD --base-url <url> --ci" to verify the test parses and passes; (4) call devloop_mutation_demo next (auto, per the DEVLOOP instructions). * cloud mode → returns guidance to call the existing create_test_suite tool instead. The repo-mode playbook is NOT used in cloud mode. ARGUMENTS — you should already have these from your devloop_detect_app call: * app_id, resource, app_dir, base_url, framework, handler_files. If any are missing, call devloop_detect_app again. The tool does NOT generate the YAML body itself — you do, using the schema from devloop_detect_app's detection_playbook. This is intentional: ATG quality depends on the AI seeing the actual handler implementations (which it can read via its own tools) far better than a server-side generator could. Aim for ≤ 30 lines per test.yaml, idempotent mutating steps, chained extract/{{var}} flow.
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  • 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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  • 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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  • 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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  • 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 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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  • Fetch a single article from Psychiatry for Kids by slug. Returns title, body content, author, clinical reviewer, citations, and metadata.
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  • 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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  • 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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  • 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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  • 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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