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747,961 tools. Updated 2026-11-05 17:45

"A search for development-related topics or tools" 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 full content for one id returned by `search`: a rental listing (rental:<uuid>), a plan set (plan:<uuid>) or a published page (page:<route>). Returns {id, title, text, url, metadata}. Always cite the url. Hosts that can call specific tools should prefer get_rental or the resources.
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  • Use this when the user asks what Alicia or digitalgarden.si has written about a subject (for example Houdini, Three.js, WebGPU, glTF/USD, Gaussian splats, AI coding agents, or the garden itself). Ranks published notes by keyword match on title, topics, summary and full text, in English and Chinese, and returns short cards (no full text). Follow up with get_article to read one. Do not use it for general web search, news, or topics unrelated to Alicia's published notes. An empty articles array means no published note matches. Scores are relative to this query and catalog, not confidence percentages; check titles and read the full note before presenting a match as relevant. Every result includes canonicalUrl and author Alicia (Person @id https://digitalgarden.si/#alicia). Cite the canonical URL when quoting. digitalgarden.si is an interactive 3D garden, not a knowledge-base wiki.
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  • Use this when the user asks what Alicia or digitalgarden.si has written about a subject (for example Houdini, Three.js, WebGPU, glTF/USD, Gaussian splats, AI coding agents, or the garden itself). Ranks published notes by keyword match on title, topics, summary and full text, in English and Chinese, and returns short cards (no full text). Follow up with get_article to read one. Do not use it for general web search, news, or topics unrelated to Alicia's published notes. An empty articles array means no published note matches. Scores are relative to this query and catalog, not confidence percentages; check titles and read the full note before presenting a match as relevant. Every result includes canonicalUrl and author Alicia (Person @id https://digitalgarden.si/#alicia). Cite the canonical URL when quoting. digitalgarden.si is an interactive 3D garden, not a knowledge-base wiki.
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  • Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Search the World Bank lending portfolio — the individual loans, credits, and grants the Bank finances — by free text, country, region, status, sector names, financing window (IBRD, IDA, Grants, Other), and board approval date. For classified education commitments use sector: ["Education"], rather than a free-text education query. Returns the project ID, name, borrowing country, region, status, board approval and closing dates, the commitment amount in USD with its IBRD, IDA, and grant parts, financing windows, major sectors, and a link to the project page. This is the operations catalogue, not the statistics catalogue: use it for "what is the World Bank funding in Kenya", "which climate adaptation projects are active", or "how much was committed to education in South Asia since 2020". For development statistics and time series, use worldbank_search_indicators and worldbank_get_data instead. Countries take the ISO3 or ISO2 codes the other tools take (BRA or BR), and multi-country operations carry a World Bank regional code such as 3A; WDI aggregates such as SSF or WLD are rejected, since the portfolio lists operations by economy — use region for those. Every filter is an exact match upstream and combines with the others by AND, so a narrow search can legitimately return nothing; when it does, the response says whether the country codes matched anything on their own.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    B
    maintenance
    Provides AI agents with real-time trending topics from Weibo, Baidu, Zhihu, Google Trends, and HackerNews/StackExchange community intel, enabling content and marketing agents to stay aware of current conversations before creating content.
    MIT

Matching MCP Connectors

  • The topics in a set of documents in one call, with each document's topic mixture. Nothing stored.

  • Crypto scam checks (tokens, sites, wallets), chain reads, web page to Markdown, email checks.

  • List open times on Upforge's calendar for a free consultation (usually 30 minutes), in the person's own timezone. Call this before book_consultation and let the person pick a time; never pick one for them. Meeting types come live from the scheduler: general-inquiry is the default, and service types such as web-design, application-development or ai-automation route to the right person. Bookings need about a day's notice, so the search starts tomorrow.
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  • Keyword search in gol24.uz news titles and summaries, newest first. Use it for topics, events or names that club_news and person_news cannot resolve ("Ballon d'Or", "Champions League draw", "national team"). For one specific club or person prefer club_news / person_news - they are more precise. News text is in Uzbek (Latin script), so search Uzbek spellings: "Oltin to'p", "Chempionlar ligasi", "terma jamoa", "Mbappe". Matching is a case-insensitive substring match on title and summary over the last `days` days (default 30, max 90). Returns up to `limit` items (default 10, max 20) with the same fields as latest_news. An empty list means no match - try a shorter or Uzbek-spelled keyword. Rate limit: 60 requests per minute.
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  • Sourced facts about a country's tax system: when and why its GST/VAT was introduced, what it replaced, and memorable, true stories (court cases, elections, odd rulings) — each fact linked to its own source. Omit country for a primer on what a consumption tax is plus famous tax stories from history. Pass topic to keep only facts about one tax (each fact lists its topics). Use it for background, explainers, teaching or a conversation opener; for any rate or amount use tax_rate_lookup, compute_gst_vat or the estimate tools, because facts are dated stories, not live figures. Countries without curated facts return the list of countries that have them rather than an invented one.
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  • Search the PUBLIC Awareness knowledge graph — 1,000+ sourced scientific papers. Use this when the user (especially a researcher or a research agent) asks for peer-reviewed findings: "latest evidence on X", "highly-cited papers about Y", "what does the literature say about Z". This is NOT a memory tool — it does not read the user's private memory, and it needs no per-user API key, so it works even when Awareness memory is not configured (the MCP connection itself still needs its own token). Coverage: condensed-matter, quantum-information, materials, machine-learning, renewable-energy, chemistry, biology, medicine, neuroscience, computer-science (2022-2026, cited >= 50), plus a business supply/demand domain. What comes back (anonymous preview mode): title, snippet (preview), confidence, source_ref (DOI), citation_count, domain/topic, and a detail page URL. Full content is NOT in the preview — tell the user it is paid retrieval via POST /api/v1/public/retrieve with an API key (see https://awareness.market/en/knowledge for the KaaS product). Args: query: Natural-language research question or keywords (2-1000 chars). domain: "science" (default) or "business". topic: Optional sub-topic slug, e.g. "medicine", "condensed-matter", "supply-chain". Empty = search all topics in the domain. top_k: Max results, 1-20 (default 5; anonymous callers are capped at 3). include_graph: When true, also return same-topic related items. Returns: {"results": [{id, title, snippet, confidence, source_ref, citation_count, domain, topic, url}], "related": [...], "total": int, "mode": str} `mode` is "preview" (anonymous) or "full" (authenticated).
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  • Amazon search ideas around a seed, from Amazon's related searches, with monthly Amazon search volume. Use when: "what else do people search on Amazon around X", building an Amazon keyword list for a listing or ads. Not for: Google keyword ideas, use seo_search_keywords. Volume for keywords you already have, use amazon_get_keyword_metrics. Returns: List. rows[]: keyword, volume. Cost: 5 + 5 per 100 rows. Default 100 rows = 10 credits. Cached 7 days. Example: {"seed": "standing desk", "limit": 50}
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  • Save which projects the user wants watched for new tools, e.g. 'my Next.js SaaS: auth, deploy, payments'. Use after subscribe, or when the user says they started/finished a project or changed interests. Replaces the whole list each time, so send all projects. Inputs: `key`; `projects` [{name, interests: short tags}]; `mode` 'by_interests' (only matching new tools) or 'all_new' (every new tool). Returns ok, project count, mode. Only whats_new reads this; it never changes search or recommendations.
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  • Live spot price of gold (XAU) or silver (XAG) in USD per troy ounce from gold-api.com (aggregated free spot feed, no key). This is a spot quote with a timestamp, not the LBMA London AM/PM fixing and not vault holdings; the route name is kept for the owner. Related tools on this server, for a different need: energy_ammonia_fertilizer_spot when you need Monthly US producer price indexes for ammonia and nitrogen; energy_cme_agri_spot_futures when you need Monthly world reference prices for corn, wheat and soybeans. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.
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  • Keyword search across FDA regulations — US Food & Drug Administration rules in 21 CFR. Answers "what FDA regulations cover X", "the FDA regulation / rule about X", "find the FDA requirement for X". Great for topics: good manufacturing practice (GMP / cGMP), quality system regulation, medical device labeling, drug labeling, nutrition facts / food labeling, new drug applications, dietary supplements, cosmetics, biologics, controlled substances, current good manufacturing practice for drugs and devices. Returns matching FDA regulations with citation (21 CFR), heading, excerpt, and source URL. This searches FDA REGULATIONS (regulatory text); for FDA DATA (drug labels, adverse events, recalls) use the openfda tools. Example: fda_search({ query: "medical device labeling" }); fda_search({ query: "good manufacturing practice", limit: 15 }). Keyless.
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  • The full text of one help article as Markdown, with links to related articles. Pass the id from help_search, or a keyword: an exact title wins, a keyword several articles share returns them to pick from, anything else gets the best search hit.
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  • Ranked related listings with per-item reasons. Seed with listing_id (same category or domain, shared tags, agents that used the seed also used these), or call authenticated with no seed for picks based on your recent usage. Not a keyword search: use search_catalog for that.
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  • Get care plan material for a specific NANDA-style nursing diagnosis: its definition, related factors (the "related to" clause), defining characteristics (the "as evidenced by" clause), SMART goals, interventions, and the conditions where it is a priority. Use when a nursing student asks about a diagnosis rather than a disease, for example "risk for infection", "acute pain", "impaired gas exchange", "ineffective coping" or "risk for falls", or asks how to write a three-part diagnosis or an AEB statement. Educational reference, not medical advice.
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  • Read one map document by the id a search result handed you. Ids: object:<name>, finding:<id> or finding:<finding_key> (stable across rescans), process:<object type>, metric:<name>, statement:<id> (run-scoped: search again after a rescan), playbook:<id>. It is the second half of the search/fetch pair that connector clients such as ChatGPT require, and returns id, title, text, url and metadata with the direct tools' data flattened to text. If your client can call the direct tools, use them instead (structured, no search first): get_object for an object you can name, get_process for a process, get_metric for a metric, list_findings for findings by status. Use fetch for a result search handed you, or a finding_key.
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  • Analyze the structural shape of the winning AI answer across several related keywords/topics in one call: does each lead with a list, how long is the opening, how many sources it cites. It covers a topic cluster in one call, for content planning across related pieces. Read-only: no side effects, safe to retry. Costs 1 quota unit per keyword in the batch (free tier is 30 units/month shared across all the metered tools, so up to 30 keywords total that period if nothing else is used). A per-keyword provider error doesn't fail the whole batch - that keyword's entry just carries an "error" field instead. Returns: {"results" (list, one {"keyword", ...same shape as analyze_citation_structure, or "error"} per keyword, in the order given), "summary": {"topics_analyzed", "topics_requested", "list_led_count", "avg_sources_cited", "avg_community_pct" (average source_mix.community_pct across the analyzed topics)}}. Args: keywords: topics/queries to analyze, e.g. ["how to reduce churn", "churn rate benchmarks", "reduce customer churn saas"]. Max 10. country: market to read the answers in, e.g. "Italy". Defaults to "United States". language: language code, e.g. "it". Defaults to "en". engine: "chat_gpt" (default), "gemini" or "perplexity", as in analyze_citation_structure. One answer per topic.
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  • Google Trends related topics for a keyword. Returns the topics and named entities associated with a keyword in a given country, as top topics scored 0-100 relative to each other and rising topics with percentage growth, each carrying its entity type. Google Trends topic discovery for market research and content planning. [$0.03/call]. Params — keyword: the search term; geo: ISO-3166 alpha-2 country code, e.g. US, GB, DE, JP (213 countries supported); timeframe: time window. Each response reports its own bucket size in `granularity`: past_30_days and past_90_days return a daily series, past_12_months and past_5_years weekly, windows under a day hourly (2004_present|past_12_months|past_30_days|past_4_hours|past_5_years|past_7_days|past_90_days|past_day|past_hour) Example params: {'keyword': 'bitcoin', 'geo': 'US'}
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  • Billable API calls can incur substantial charges. Never call before a successful batch_quote for the same tools and arguments. Execute only when that quote response reports auto_execute_eligible=true for these calls, or when explicit user authorization covers that cost and risk. auto_execute_eligible waives only the extra cost-confirmation step; it does not override a user prohibition on execution, a narrower scope, or a tighter budget limit. Re-quote if tools, arguments, or scope change. Execute 1 to 20 independent published-tool calls; never silently retry. A missing or failed quote is not free. Use tool names exactly as search returns them, without a client prefix such as mcp__aisa__. Reuse the session_id returned by Search in all subsequent tool calls for this task. If omitted, a new session is created.
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    Destructive
    OAuth