The MCP tool directory.

Provides keyword search suggestions and related keyword metrics (e.g., search volume, difficulty, CPC) for specified queries or keyword lists, with options to filter, sort, and customize returned fields by country. Use doc tool first to get the real input schema.

Retrieves search volume metrics for a specified keyword broken down by country. Use doc tool first to get the real input schema.

Retrieves historical search volume data for a specified keyword within a given country and date range. Use doc tool first to get the real input schema.

Get channel information and videos on YouTube Kids

Search YouTube Kids for videos

Get video information on YouTube Kids

Perform write operations on repository labels. To set labels on issues, use the 'update_issue' tool.

Resolve an ISO 639-1 language code such as en, fr, or ja to its English name and native name when you need language metadata from a two-letter code.

Convert latitude/longitude coordinates to map tile coordinates (x, y) for a given zoom level. Uses the same web mercator projection as MapKit.

List all revisions of a law within a date range (법령 개정 이력 조회). Shows when and how a law was amended over time. Use the MST values from the results with article_amendment_diff to see specific article changes. Args: law_name: Law name (e.g., "근로기준법") date_from: Start date YYYYMMDD (default "20100101") date_to: End date YYYYMMDD (default "20261231") oc: Optional OC override type: Response format - "JSON" (default), "XML", or "HTML" Returns: List of revisions with dates, types, and MST identifiers Examples: >>> law_amendment_summary(law_name="근로기준법", date_from="20200101") >>> law_amendment_summary(law_name="민법", date_from="20150101", date_to="20251231")

Query specific article/paragraph by announcement date (공포일 기준 조·항·호·목 조회). **BEST TOOL for querying specific articles like "제174조", "제3조" etc.** This returns only the requested article/paragraph, avoiding large full-law responses. Args: id: Law ID (either id or mst is required) mst: Law serial number (MST) jo: Article number in XXXXXX format. Format: first 4 digits = article number (zero-padded), last 2 digits = branch suffix (00=main). Examples: "017400" (제174조), "017200" (제172조), "000300" (제3조), "001502" (제15조의2) hang: Paragraph number (6 digits, e.g., "000100" for 제1항) ho: Item number (6 digits, e.g., "000200" for 제2호) mok: Subitem (UTF-8 encoded, e.g., "다" for 다목) oc: Optional OC override (defaults to env var) type: Response format - "JSON" (default), "XML", or "HTML" Returns: Specific law section content Examples: Query 자본시장법 제174조: >>> law_josub(mst="279823", jo="017400", type="XML") Query 건축법 제3조 제1항: >>> law_josub(mst="276925", jo="000300", hang="000100", type="XML")

Search current laws by announcement date (공포일 기준 현행법령 검색). This tool searches Korean laws organized by announcement (publication) date. Args: query: Search keyword (law name or content) display: Number of results per page (max 100, default 20). **Recommend 50-100 for law searches (법령 검색) to ensure exact matches are found.** page: Page number (1-based, default 1) oc: Optional OC override (defaults to env var) type: Response format - "JSON" (default), "XML", or "HTML" sort: Sort order date: Announcement date (YYYYMMDD) org: Ministry/department code filter knd: Law type filter Returns: Search results with law list or error

Retrieve full law content by announcement date (공포일 기준 법령 본문 조회). Retrieves the complete text of a law organized by announcement (publication) date. **IMPORTANT: For specific article queries (e.g., "제174조"), ALWAYS use the `jo` parameter. Some laws (e.g., 자본시장법) have 400+ articles and the full response can exceed 1MB. Using `jo` returns only the requested article, which is much faster and cleaner.** Args: id: Law ID (either id or mst is required) mst: Law serial number (MST) lm: Law modification parameter ld: Law date parameter (YYYYMMDD) ln: Law number parameter jo: **REQUIRED for specific articles.** Article number in XXXXXX format. Format: first 4 digits = article number (zero-padded), last 2 digits = branch suffix (00=main). Examples: "017400" (제174조), "017200" (제172조), "000300" (제3조), "001502" (제15조의2) lang: Language - "KO" (Korean) or "ORI" (Original) oc: Optional OC override (defaults to env var) type: Response format - "JSON" (default), "XML", or "HTML" Returns: Full law content or specific article content Examples: Retrieve specific article (RECOMMENDED): >>> law_service(mst="279823", jo="017400", type="XML") # 자본시장법 제174조 Retrieve full law (WARNING: large response for some laws): >>> law_service(id="009682", type="XML")

Find all applicable laws, precedents, and interpretations for a situation (법률 종합 분석). Given a plain language description of a legal situation, this tool searches across multiple legal databases in one call: law articles, court precedents, legal interpretations, and citation networks. Args: situation: Plain language description (e.g., "직원이 고객 데이터를 USB에 담아갔다") display: Results per sub-query (default 5) oc: Optional OC override type: Response format - "JSON" (default), "XML", or "HTML" Returns: Comprehensive legal analysis with applicable laws, precedents, interpretations, and citations Examples: >>> legal_resolver(situation="집주인이 보증금을 안 돌려줘요") >>> legal_resolver(situation="회사에서 갑자기 해고당했어요", display=3)

Links two or more content items as translations of the same content by merging them into a single TranslationGroupId. After linking, the public slug-lookup endpoint returns them as siblings so consumers can emit hreflang alternates. All items must belong to the same brand. Existing siblings of any input item are pulled into the merged group automatically (transitive merge). If an input item already has a different language sibling that duplicates one of the other inputs, the call fails with a conflict.

Fetch one company's LinkedIn page: name, description, industry, employee count, headquarters, website, founding year, specialities, and the URN/numeric id you need for linkedin_search_people company filters. identifier accepts a company URL, the slug after /company/ (e.g. 'microsoft'), or a website domain like 'microsoft.com'; numeric ids and URNs are search-filter inputs, not fetch identifiers. Domains are resolved to a company and verified against that company's website: a domain identifier always QUOTES base+4 credits (set max_credits accordingly), and the 4-credit resolution surcharge is refunded at settlement when the domain was resolved before, so known domains settle at the base price. A domain that cannot be verified to a company returns INVALID_INPUT with the closest matches instead of a guessed company. Costs 4 credits base. Do not guess a slug from a brand name: slugs are vanity strings and a famous name can belong to an unrelated company's page (linkedin.com/company/anthropic is a small investment fund, not the AI lab). When you only know the company's name, pass its website domain instead -- the verified form -- and sanity-check the returned industry and description against what you expected. This tool does not search by name: if you only have an approximate company name, use linkedin_search_people's current_company filter with keywords (the filter resolves names) or give the exact slug. For the company's posts, use linkedin_get_posts with the same identifier (URL, slug, or website domain all work there too).

Fetch the recent LinkedIn posts of one person or one company. identifier accepts a profile or company URL, a slug, a person URN, or a company website domain like 'microsoft.com'; the entity type is detected automatically. A domain resolves to its verified company first, exactly like linkedin_get_company: it QUOTES base+4 credits (set max_credits accordingly) and the surcharge is refunded at settlement for already-known domains, so they settle at the base price. Company URNs and numeric company ids are search-filter inputs, not fetch identifiers: use the company slug, URL, or domain here. Returns one page of posts (text, created_at, author, likes, comments_count, shares, is_repost, url) with a cursor for older posts. Costs 4 credits per page. Use this for 'what has X been posting', voice-of-company research, or activity checks before outreach. Not for reading one specific post you already have a URL for, and not for keyword search across LinkedIn; neither is supported in v1.

Fetch one person's LinkedIn profile. identifier accepts a profile URL, the slug after /in/ (e.g. 'williamhgates'), or a urn:li:fsd_profile URN; URLs are cleaned automatically. Always returns the overview (name, headline, location, current position, follower counts) plus up to 2 requested sections from about|experience|education|skills at no extra cost; each section beyond 2 adds 2 credits (max 4 sections). Costs 4 credits base. If you only have a name, use linkedin_search_people first; this tool does not search. Results from linkedin_search_people with is_anonymous=true cannot be fetched here; treat them as 'someone matching this exists' and stop. Companies belong to linkedin_get_company.

Identify what a LinkedIn URL points at before fetching it. Give any LinkedIn profile, company, or post URL (utm params, www/m subdomains, trailing slashes are fine); get back {type: person|company|post, id, handle, canonical_url}. For profile URLs, id is the stable person URN; for company URLs, id is the stable company URN; for post URLs, id is the activity URN extracted from the URL. For people, use the returned handle or id with linkedin_get_profile or linkedin_get_posts. For companies, use the returned HANDLE with linkedin_get_company or linkedin_get_posts; the company URN/id is a linkedin_search_people filter input, not a fetch identifier. Costs 2 credits. Skip this tool when you already have a slug, URN, or clean URL: linkedin_get_profile and linkedin_get_company accept those directly, so resolving first would waste 2 credits. Not for non-LinkedIn URLs; it returns INVALID_INPUT for those.

Find people on LinkedIn by keywords and filters. The right tool when you have a name, role, or 'who is the X at Y' question without a profile URL. Pass keywords (free text: name, title, or both) and any of first_name, last_name, title, school, current_company, past_company. If keywords is omitted it is derived from the name or title filters; school or company filters alone are rejected with INVALID_INPUT, so include keywords with those. Company filters accept a company name, slug, numeric id, or URN; names are resolved for you. Costs 10 credits including the first 10 results; each further 10 results add 1 credit (limit max 30; trial keys max 10). A cursor page is a NEW call priced the same way by its own limit, so one limit=30 call is much cheaper than three limit=10 pages; prefer a larger limit over paginating. Do NOT combine a company NAME filter with limit=30: name resolution spends one of the call's three internal fetches, so that combination is rejected. With a company name keep limit<=20; for limit=30 pass the company's numeric id or URN (linkedin_get_company returns both). Returns name, position, location, urn, public_identifier per result, a cursor for the next page, and total_matches. Results with is_anonymous=true are private profiles; do not pass them to linkedin_get_profile. For one known person with a URL/slug, call linkedin_get_profile directly instead.

Fetch a URL and return the content of the page. If you are unable to fetch the page content, might be worth trying to render the JavaScript content.

Search the web in real time using Linkup to retrieve current information, facts, and news from trusted sources. Use this tool for: real-time data (weather, stocks, sports scores, events), breaking news, current events, recent research, product information, up-to-date prices, schedules, and any information not available in your knowledge base. Returns comprehensive content from the most relevant sources.

Get a list of all available voice archetypes with their names and descriptions

Get a list of available copywriting frameworks and their descriptions for a specific social media network

List email aliases (forwarding addresses) and their destinations. Optionally filter by domain.