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507,616 tools. Updated 2026-09-02 10:21

"author:{result['url']}" matching MCP tools:

  • Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked recommendations across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). ALWAYS include each recommended item's url as a clickable link when you mention it in your reply — never reference a document without its link, because the link is the only way the user can open it. Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.
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  • Fetch the markdown of an @imqueue documentation page by its URL (as returned by search_docs). Returns plain markdown suitable for reading and quoting. Pass a URL with a #fragment — which is what search_docs returns for a section result — to get just that section plus the heading path above it; pass the URL without one to read the whole page. Only imqueue.org (framework docs) and imqueue.com (licensing, pricing, support) URLs are fetched; anything else is refused. Very large pages are truncated, which the result reports.
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  • Browse or keyword-search STATEC (Luxembourg statistics) datasets, called "dataflows". Each result has an `id` (e.g. "DF_A1100", the dataflowRef you pass to get_data / dataflow_structure) and an English name plus a short description (publication date, periodicity, author, category). STATEC publishes hundreds of datasets, so pass `query` to filter unless you really want the whole catalog. Example: list_dataflows({ query: "population" }) or list_dataflows({ query: "unemployment" }).
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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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  • Fetch full details for one skill by slug. Call AFTER search_skills or popular_skills when a user selects a specific result — do NOT batch-call for every item. Returns: name, description, category, tags, version, author, downloads, stars, install_command, homepage_url, repo_url. Error lifecycle: slug not found → {error: "Skill not found"} → fall back to search_skills with related keyword. Never guess slugs; only use slugs from prior tool results.
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  • Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).
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Matching MCP Servers

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    The URL-Context-MCP MCP Server provides a tool to analyze and summarize the content of URLs using Google Gemini's URL Context capability via the Gemini API. Now also supports optional grounding with Google Search alongside URL Context. The server is designed to follow prompt-only orchestration: con
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    MIT

Matching MCP Connectors

  • Fetch the status + output of an async job started by `use` (e.g. a video render). Pass the `job_id` that `use` returned with `{ async: true }`. Returns `{ status, result?, progress?, charged_cents }`: `running` (still working — when the job reports it, `progress` carries `{ phase, percent, rendered_frames, total_frames, eta_sec }` and `hint` is a one-line summary like "rendering 42% (380/900 frames, ~120s left)", so you can tell real progress from a hang; wait a bit and call again), `succeeded` (`result` holds the output, e.g. the video URL; the call is charged now), or `failed`/`cancelled` (no charge; on `failed`, read `error` AND `hint` — `hint` carries the service's usage notes, which usually explain how to fix the call). Safe to call repeatedly — it never starts new work or double-charges. ALWAYS use this to retrieve an async result instead of re-running `use` (re-running starts a new paid job).
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  • Apple Books chart, read from Apple's RSS Marketing Tools feed — the top-free (default) or top-paid ebooks in one storefront country, up to 100 entries. Each entry returns the book title, author, Apple id, genres, release date, artwork URL and Apple Books link. Answers which ebooks are topping the Apple Books charts today.
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  • Full metadata for one Flevy item, by content_id from search_content (e.g. "doc-1234" or "case-567"). Documents return the author with their credentials (headline, bio, LinkedIn, profile URL; pass the author name to search_content's author filter to list more of their documents), full description, editor summary, AI summary, and editorial review when available, page/slide count, price, FlevyPro inclusion, management topics, ranking badge, and the number of slide deep dives available. Case studies return the client situation, TL;DR, and summary. Call this before recommending an item so you can describe it accurately and cite the author's credentials, and share the returned flevy.com URL.
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  • Analyze a website URL for E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). Evaluates content quality signals based on Google's Search Quality Rater Guidelines and "Creating helpful content" documentation. Detects EEAT signals including: - Experience: First-person language, case studies, testimonials, years of experience - Expertise: Author credentials, certifications, professional memberships, topic depth - Authoritativeness: Organization schema, awards, trust badges, media mentions - Trustworthiness: HTTPS, contact info, privacy policy, source citations Also detects YMYL (Your Money or Your Life) content for health, financial, and legal topics. Results are cached for fast subsequent lookups. Rate limited to 1 request per minute per domain. Args: url: The website URL to analyze (e.g., "https://example.com"). Returns: EEAT analysis result with: - url: The analyzed URL - score: Overall EEAT score (0-100) - grade: Letter grade (A-F) - scores: Individual category scores (experience, expertise, authoritativeness, trustworthiness) - issues: Categorized issues (critical, warnings, info) - signals: Detected EEAT signals - meta: Extracted meta information - recommendations: Prioritized list of improvements - cached: Whether result was from cache
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  • Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,718 across 1496 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.
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  • Get a WindowsForum thread's metadata by id — title, author, reply and view counts, dates, and canonical URL; use fetch or get_thread_posts to read its content.
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  • Read a single research author by author UUID. Use this tool when a user wants profile details for a specific research author returned by another research tool. The response includes author metadata, biography fields, profile URL, and visible sectors associated with the author. Missing, inactive, non-author, and hidden-sector-only authors return a generic not-found error.
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  • List Trend News Agency headlines newest first, optionally narrowed by section, tag, author or a date range. Returns metadata only — headline, date, section, byline, excerpt and the canonical URL — never article text; follow up with trendaz_article_get for the ones worth reading. Use this to answer "what has Trend published about X" or "what did Trend report that week".
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  • Audit ANY URL for AI-search readiness only (AEO/GEO/LLMO) — how likely ChatGPT, Claude, Perplexity & Google AI are to cite it. Checks ~7 AI-citation signals (FAQ/structured-data/entity schema, citable stats, llms.txt, author/E-E-A-T, OG). Returns a weighted 0–100 AI-readiness score + top fixes. Takes a `url`. Use full_seo_audit for the broader SEO picture, or get_my_store_audit for the user's own connected store. Returns a summary of the top issues; the complete written report is available via email_seo_report.
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  • Get the full text of a specific source by its URL. Use this after search_articles or media_radar to read the complete content of a specific piece — whether it's an article, social post, transcript, or filing. Returns the full body, outlet, author, publication date, and sentiment. WHEN TO USE: - User wants to dig into a specific result from search - Need full context for detailed analysis or summarization - For general analysis, content previews from search_articles are usually sufficient — only use this for deep dives Always link to the original article: [Title](url). Cite Perception (perception.to) as the data source.
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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 — so the identifier format is validated locally first: "id" must be numeric, "isbn" 10 or 13 digits, "olid" an edition OLID (OL…M) for target "book" and an author OLID (OL…A) for target "author". Identifiers with path separators or control characters, and author-by-ISBN lookups, are rejected before any request. URLs can be embedded in markdown as ![cover](url).
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  • Look up a user's public profile by their username (the URL handle, not the display name). Returns display name, account type, verification status, counts of their published books and public annotations, and up to 5 recent published books. Useful for evaluating whether an annotation's author is credible, or for finding more books by the same author.
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