Tool search 101,857 tools · 5,224 live servers

List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report.

Start an autonomous web research task. The agent plans sub-questions, searches the web, reads the sources and writes a report with citations — this is real research, not a single model call, and takes 2-5 minutes. Returns a job_id immediately; poll check_job to get the report. Use this when you need sourced, current information rather than what a model already knows. Powered by gpt-researcher (29k stars) hosted at AI NetCafé.

Get the status or result of a job started by deep_research. Poll every 20-30 seconds until status is "done" or "error".

Fetch the exact schema(s) for tool(s) you ALREADY know by name — e.g. names a skill/playbook gave you (getTestReportFull, updateTestCase, listTestReports, getMock, getTestCase, getApp, listApps, ...). Returns ONLY the named tools: no fuzzy matching, no unrelated tools. Use this instead of search_tools whenever you know the name — it is exact and cheap. Use search_tools only to DISCOVER names you do not already know. Returns {"tools": [{name, description, inputSchema}, ...], "not_found": [names...]}.

Run any Keploy tool by name. Most tools are hidden from the default list. Know the name from a skill? get its schema with get_tool_schema; don't know the name? discover it with search_tools. Then call invoke_tool with that name and an "arguments" object matching the tool's inputSchema. Example: invoke_tool {"name": "getTestReportFull", "arguments": {"appId": "...", "testRunId": "...", "fields": ["status"]}}

DISCOVER tool names you do NOT already know, by keyword. Most Keploy tools are hidden from the default tool list to save context. If you ALREADY know the exact name (e.g. a skill named it), call get_tool_schema instead — it is exact and far cheaper than this fuzzy search. Returns {"matches": [{name, description, inputSchema}, ...], "total_catalog": N}. Search by intent words, e.g. "test report", "mock patch", "update test case", "cloud replay branch", "record".

Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.

Read a webpage's full content as clean markdown. Use after web_search_exa when highlights are insufficient or to read any URL. Best for: Extracting full content from known URLs. Batch multiple URLs in one call. Returns: Clean text content and metadata from the page(s).

Search docs, API reference, examples, and migration notes for RevoGrid.

Search runnable or live RevoGrid examples only.

Search docs, API reference, examples, and migration notes for RevoGrid.

Search runnable or live RevoGrid examples only.

Search recipes by keyword across titles, descriptions, tags, and full source code. Use for any iOS, SwiftUI, or backend topic — e.g. subscription, authentication, camera, animation, chart, onboarding, paywall, infrastructure.

Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query. This tool returns up to 10 high-quality content chunks (each max 500 tokens), extracted from Microsoft Learn and other official sources. Each result includes the article title, URL, and a self-contained content excerpt optimized for fast retrieval and reasoning. Always use this tool to quickly ground your answers in accurate, first-party Microsoft/Azure knowledge. ## Follow-up Pattern To ensure completeness, use microsoft_docs_fetch when high-value pages are identified by search. The fetch tool complements search by providing the full detail. This is a required step for comprehensive results.

Search for code snippets and examples in official Microsoft Learn documentation. This tool retrieves relevant code samples from Microsoft documentation pages providing developers with practical implementation examples and best practices for Microsoft/Azure products and services related coding tasks. This tool will help you use the **LATEST OFFICIAL** code snippets to empower coding capabilities. ## When to Use This Tool - When you are going to provide sample Microsoft/Azure related code snippets in your answers. - When you are **generating any Microsoft/Azure related code**. ## Usage Pattern Input a descriptive query, or SDK/class/method name to retrieve related code samples. The optional parameter `language` can help to filter results. Eligible values for `language` parameter include: csharp javascript typescript python powershell azurecli al sql java kusto cpp go rust ruby php

Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

Reads an Apollo documentation page by slug in chunks. Use slugs returned by ApolloDocsSearch.

Searches official Apollo documentation for GraphQL, GraphOS, Apollo Router, Apollo Client, MCP Server, schema design, deployment, and Connectors. Returns URLs, slugs, and excerpts.

Retrieves full documentation content for Svelte 5 or SvelteKit sections. Supports flexible search by title (e.g., "$state", "routing") or file path (e.g., "cli/overview"). Can accept a single section name or an array of sections. Before running this, make sure to analyze the users query, as well as the output from list-sections (which should be called first). Then ask for ALL relevant sections the user might require. For example, if the user asks to build anything interactive, you will need to fetch all relevant runes, and so on. Before calling this tool, try to implement Svelte components using your own knowledge and the `svelte-autofixer` tool, since calling this tool is token intensive.

Search read-only HeyClaude registry entries by query, category, exact tag, and skill platform compatibility.

Answer 'what should I use to do X' in one call. Given a plain-language task (and optional platform/category), returns the best-match HeyClaude entries ranked by fit — each with why it fits, trust summary, disclosed safety/privacy notes, and an inline install block — plus a topPick and a consolidated installPlan. Unlike workflow.plan it does not force category diversity; it returns the genuinely best matches. Collapses the search → compare → detail → asset loop into a single answer-shaped response.

Search generated registry artifacts for likely duplicate entries before a user opens a submission PR.

Full-text search across all published tax and accounting skills. Find, lookup, query, or discover skills by keyword, tax concept, deduction type, form number, or regulation (e.g. 'home office deduction', 'crypto capital gains', 'reverse charge', 'Schedule C', '60-day reporting'). Optionally limit to one jurisdiction. Use this when you don't know the exact skill slug. Set content_type='method' to discover step-by-step METHODS (guided procedures — what you EXECUTE via start()) instead of reference material; each method carries a review_status (accountant-reviewed | source-cited).

Query individual tax RULES/FACTS (rates, thresholds, rules, definitions, tables) ACROSS jurisdictions and metadata, and get back a bundled markdown rule set the user can save and run locally. Unlike get_skill (one whole skill), this assembles a cross-cutting SET — e.g. 'VAT rates in MT, IE and DE', 'all income-tax thresholds for 2025', or 'rules mentioning reverse charge'. By default returns ALL matching rules, each tagged with its verification status; pass status:'verified' for accountant-/research-verified only. Call `list_rule_facets` first to see the queryable values.

Returns the metadata you can filter on with `search_rules` — the live jurisdictions, the domains, roles, block types (rule kinds), statuses, tax years, and a sample of topics — plus the defaults. Call this before `search_rules` to learn the valid filter values rather than guessing.

Browse the catalog of 260+ audit rules that run during an audit, grouped into categories (crawlability, meta tags, performance, security, accessibility, content, and more). Filter by category code or search by keyword to find what a specific rule checks. Use get_rule for one rule's full detail.

Search tennis players by name. Returns id, country, ranking and tour. Use the returned id with get_player.

Search the OrchestKit documentation. Use this first to find the right page for a question. Input: a query string. Returns a ranked list of pages with titles and URLs.

Fetch one OrchestKit documentation page as Markdown. Use after orchestkit_docs_search to read a page in full. Input: the page path (e.g. '/docs/getting-started/installation'). Returns the page Markdown.

Search PubMed with full query syntax, filters, and date ranges. Returns PMIDs and optional brief summaries. Supports field-specific filters (author, journal, MeSH terms), common filters (language, species, free full text), and pagination via offset for paging through large result sets.

Spell-check a query and get NCBI's suggested correction. Useful for refining search queries.

Search and explore the MeSH (Medical Subject Headings) controlled vocabulary. Returns descriptor records with tree numbers, scope notes, and entry terms, plus pagination via offset for paging past the maxResults cap.

Look up PubMed IDs from partial bibliographic citations. Useful when you have a reference (journal, year, volume, page, author) and need the PMID — deterministic citation matching, more reliable than free-text search for structured references. Each citation must include at least journal or year (ECitMatch primary-keys on journal+volume+page; author-only or volume-only inputs guarantee no match); more fields = better match accuracy.

Convert between article identifiers (DOI, PMID, PMCID). Accepts up to 50 IDs of a single type per request. Only resolves articles indexed in PubMed Central — for articles not in PMC, use pubmed_search_articles instead.

Search Europe PMC, a broad open-access biomedical corpus. Surfaces preprints (`source: PPR`), patents (`source: PAT`), Agricola (`source: AGR`), plus everything in PubMed (`MED`) and PMC. Use when additional coverage is needed — preprints and EPMC-only OA records are the typical recovery. Paginate via `cursorMark`. Defaults to `MED`, `PMC`, and `PPR`; pass `sources` to include `PAT` / `AGR`. Abstracts arrive as a bounded `abstractSnippet` with `abstractTruncated` marking the cut ones — pass a hit’s `source` and `epmcId` to `pubmed_europepmc_fetch` for the complete abstract.

Semantic retrieval (#1089): return the records in a CSV most similar to a free-text query, ranked by cosine similarity. Embeds the chosen column and the query with the zero-config in-house embedder (no cloud/torch by default) and runs ANN search. The read side of the RAG entity-canonicalization epic -- fetch candidate records by query without running a full dedupe.

Find duplicate matches for a record. Provide field values to search against the loaded dataset.

Alias for `find_duplicates`. Find duplicate matches for a record. Provide field values to search against the loaded dataset.

Search Apple's official developer documentation and video content using advanced RAG technology. Returns relevant content from Apple's technical documentation, frameworks, APIs, design guidelines, and educational resources.

Search for clinical trial studies from ClinicalTrials.gov. Supports full-text and field-specific queries, status/phase/geographic filters, pagination, sorting, and field selection. Returns a compact per-study index by default; pass the fields parameter to get specific leaves at full fidelity — full study records are ~70KB each.

Discover valid values for ClinicalTrials.gov fields with study counts per value. Use to explore available filter options before building a search — e.g., valid OverallStatus, Phase, InterventionType, StudyType, or LeadSponsorClass values.

Resolve valid field names from the ClinicalTrials.gov data model — the canonical PascalCase identifiers (OverallStatus, EnrollmentCount, LeadSponsorName) accepted by the `fields`, `advancedFilter`, and `sort` parameters of other tools, and as input to clinicaltrials_get_field_values. Select a mode: `"search"` — keyword search returning ranked matches (pass `query`, e.g. "enrollment", "sponsor", "adverse events"); `"drill"` — drill into a specific section by dot-notation path (pass `path`, e.g. "protocolSection.designModule"); `"overview"` — top-level summary of all sections (no additional args).

Fetch clinical trial results data from ClinicalTrials.gov for completed studies — outcome measures with statistics, adverse events, participant flow, baseline characteristics, and results metadata (limitations & caveats, certain-agreement disclosure restrictions, results point of contact). Only available for studies where hasResults is true. Use clinicaltrials_search_studies first to find studies with results.

Match patient demographics and conditions to eligible recruiting clinical trials. Provide age, sex, conditions, and location to find studies with matching eligibility criteria, contact information, and recruiting locations. Results are re-ranked so studies whose own condition matches a requested condition surface above tangential matches from ClinicalTrials.gov's fuzzy condition search.

Search detailed documentation for Strudel live coding or ABC/ABCJS notation. Returns relevant code examples and explanations from the official docs. Use this when the curated guides (get-strudel-guide, get-music-guide) don't cover what you need — for specific functions, advanced techniques, or when you're unsure about syntax. Powered by semantic search over strudel.cc and ABCJS docs.

Search for GitHub repositories by name, description, or topic keywords using the GitHub Search API.

Perform a fast git grep inside the repository, allowing regex matching by default or substring search.

List all available @marmoui/ui components with names, descriptions, categories, and prop counts. Use to discover what exists. Prefer `search_components` when you already know the use case (form, table, dialog, etc.).

Search @marmoui/ui by keyword (e.g. "form", "table", "dialog", "avatar"). Preferred discovery tool — returns the 5-8 most relevant components for a need. Call this before `get_component_info` when unsure which component to use.

The map of emem's tool surface, and the only tool you need to find the rest. Returns the working loop in the order you walk it (name a thing, ground it, cite it, resolve it, verify it, check for drift), then every other tool grouped by the question it answers, each with its one-line trigger. Pass `name` to get one tool's full input schema and a runnable example, so you can use a tool without loading all of the descriptors into context. This endpoint advertises the core loop only; the Earth-observation, search, embedding and log tools are catalogued here and remain callable by name. When to use: Call this FIRST when you do not know which emem tool answers the question, or when you need a capability you cannot see in your tool list. This responder advertises a small core loop by default rather than its full catalog, so a tool being absent from your list does not mean it is absent from the server. Pass `q` to search by topic (`ndvi`, `cloud`, `flood`, `verify`), `name` for one tool's exact schema, or no arguments for the whole map. If you want the full catalog registered as callable tools instead, reconnect to the /mcp/full endpoint; for a one-shot answer without picking a primitive at all, use emem_ask. Example arguments: {"q":"ndvi"}

ChatGPT Deep Research compatibility tool. Returns a ranked list of Swiss court decisions matching a query, each as {id, title, url, snippet}. Pair with `fetch` to retrieve a decision's full text by id. General (non-deep-research) clients should prefer `search_decisions`, which exposes filters (court, canton, date, language) and richer metadata.

ChatGPT Deep Research compatibility tool. Fetches one Swiss decision's full text by id, returning {id, title, text, url, metadata}. The id comes from a `search` result (a decision_id like bger_6B_1234_2025; a docket number or BGE reference also resolves). General clients should prefer `get_decision`.

Use this tool to find COURT DECISIONS (Rechtsprechung): 1,050,000+ Swiss federal and cantonal decisions plus ~2,800 ECtHR decisions concerning Switzerland. Full-text search with keywords, phrases (in quotes), Boolean operators (AND, OR, NOT), prefix matching (word*), docket lookup (6B_1234/2025) and column-scoped search (regeste:keyword). Filter by court, canton, language, date range, chamber. Results are relevance-ranked and enriched with citation counts, statute references and is_leading_case. Use offset to page. Most recent decisions: empty query + sort='date_desc' + court filter. Not for statutes (search_laws), commentary (search_scholarship), or administrative guidance (search_practice).

Use this tool to fetch SEVERAL decisions at once (1-10 ids) instead of calling get_decision repeatedly. Same data per decision — canonical citation strings, Markdown link, Regeste, metadata — in ONE call. Prefer this whenever you are about to read more than one decision from a search result: it is the difference between one tool call and ten, which matters because clients cap tool calls per turn. full_text defaults to FALSE here (unlike get_decision) because ten full judgments exceed connector response limits; set it true only for a short id list, and each text is excerpted with the cut disclosed. Ids not found are reported individually — the rest of the batch still returns.

Build a research-only mock decision outline from user facts. Combines relevant Swiss case law retrieval with statute references. If possible, enriches statutes with Fedlex text excerpts. IMPORTANT: The tool may return clarification questions (high/medium priority). High-priority clarifications must be answered (via the clarifications parameter) before the tool will provide a conclusion. Call again with clarifications to get the full analysis.

Use this tool to retrieve and quote the VERBATIM text of Federal Council Botschaften (legislative messages). Where get_article_purpose answers 'why does Art. X exist?', this tool answers 'show me every Botschaft passage about TOPIC X'. Useful when the caller doesn't know which SR/article to ask for — e.g. starting from a concept like 'Vaterschaftsurlaub' or 'Klimaschutz'. Returns ranked passages (FTS5 BM25) with bbl_citation, page, section path, and an article anchor where the parser could identify one. Quote verbatim; every snippet has a stable Fedlex ELI URI.

List all available legal agreement templates as a paginated compact catalog. Returns lightweight metadata for discovery — call get_template for full per-field detail. Templates are returned in stable lexicographic order by template_id. For finding templates by topic, jurisdiction, or source, use search_templates instead.

Search for legal agreement templates by keyword. Uses BM25 ranking to find the most relevant templates matching your query. Searches across template names, descriptions, categories, sources, and field definitions. Use this instead of list_templates when you know what kind of agreement you need.

Searches CometChat documentation including SDK guides (JavaScript, React, iOS, Android, Flutter, React Native), UI Kit references, REST API documentation, integration tutorials, and OpenAPI specs. Returns ranked snippets with titles and direct links to source pages. Supports an optional `version` filter to scope results to a specific documentation version.

Vaaya's consultant. Describe ANY external capability you or the user might want — generate an image/video, search or scrape the web, run code in a sandbox, send/receive email, enrich a contact — and it helps figure out the best way, teaching the user what Vaaya can do. It is CONVERSATIONAL and remembers prior turns. It returns: mode='converse' (a reply to RELAY to the user verbatim — questions, options, ideas; get the user's response and call consult again with it, so the conversation continues), mode='call' (an ordered list of calls to run via `use`, with a message explaining the preferred choice + alternatives + why; multi-step results may contain placeholders like '<from step 1: sandbox_id>' — run earlier steps first and substitute), or mode='unsupported'. Every reply includes `suggestions` (2-3 things to do next) — surface these to the user. AFTER you run a `call` result's calls via `use`, call consult ONE more time with a short note on the outcome (what was produced / any failures) — it returns result-aware, Vaaya-grounded next steps to offer the user (the `call` result's `after_running` field reminds you). Call consult whenever you hit a capability gap or the user wants to know what's possible. It does NOT execute or bill — you run returned calls via `use`. ALWAYS show the user consult's `message` and `suggestions` and let them steer.

Execute a single call that `consult` handed you, and bill on success. Used for any external capability (image/video/audio generation, web search, scraping, email, document parsing, code sandbox, browser automation, embeddings, etc.). The server validates params against a registered schema and proxies to the upstream — you never pass URLs or API keys. Always get the exact (service, action, params, max_cost_cents) from `consult` first — don't guess them.

Search for items within a single crate by name or documentation.

Retrieve detailed CVE data by ID: description, CVSS v3.1 + vector, CVSS v2 (always emitted), EPSS score + percentile, CISA KEV status (expanded: due_date, required_action, ransomware flag, vendor_project, product, vulnerability_name, short_description, notes, cwes, date_removed when in_kev=true), NVD vulnerability_status (Analyzed/Modified/Awaiting Analysis/Deferred/Rejected/Withdrawn), cve_tags ('disputed' triggers [DISPUTED] summary prefix), affected products (CPE), references, patch availability, related CVEs. By default affected_products is truncated to the first 20 entries (total_products reports the honest count) and references to the first 10 (total_references reports the honest count). Pass include_affected_products=true and/or include_full_references=true for the complete lists. Pass include_reference_tags=true to receive structured references_full=[{url, tags, source}] (NVD upstream tags + source provenance) — also activates tag-first patch detection. Pass include_severity_breakdown=true to receive severity_sources/consensus/disagreement (multi-source view of NVD/MITRE/GHSA/OSV severity assessments). Use for single-CVE details; use cve_search for queries by product/severity. Response carries next_calls — chain with kev_detail when kev.in_kev=true, with cwe_lookup on each CWE in cwes (up to 3 pivots), and with exploit_lookup for public PoC availability. Free: 30/hr, Pro: 500/hr. Returns {cve_id, summary, description, severity, cvss_v3, cvss_v2, cvss_v2_vector, cvss_breakdown, cwe_id, cwes, vulnerability_status, cve_tags, published, modified, sources, first_seen_source, first_seen_at, epss, kev (in_kev, date_added, due_date, required_action, known_ransomware_use, vendor_project, product, vulnerability_name, short_description, notes, cwes, date_removed), affected_products (first 20 by default), total_products, references (first 10 by default), total_references, total_references_unique, references_full (only when include_reference_tags=true), severity_sources/severity_consensus/severity_disagreement (only when include_severity_breakdown=true), patch_available, related_cves, verdict, next_calls}.

Search CVE database with filters: product/vendor, severity, published date range, EPSS score, CWE, CVSS range, CISA KEV status. Default response is SLIM per-result (cve_id, summary, severity, cvss_v3, cwe_id, epss, kev, total_products, published, modified, sources) — pass include='full' for description, cvss_breakdown, affected_products, references, first_seen_*. Verdict (sources_queried, falsifiable_fields, completeness, data_age) is at the response root — applies to the whole batch, not per-row. Product/vendor filters are EXACT NVD-canonical-token matches (not the common name — e.g. nginx is 'nginx_open_source'/'nginx_plus', vendor 'f5'); a low/zero count for a well-known product means the token differs, so for dependency/package lists use check_dependencies and for a domain's whole stack tech_stack_cve_audit (both auto-normalize tokens). Use for vulnerability discovery by criteria; pass cwe_id (e.g. CWE-79) to enumerate every CVE in our database mapped to a weakness — pair with cwe_lookup for the category description and mitigations. Use cve_lookup for single CVE by ID, kev_detail when kev=true filtering and the agent needs federal patch deadlines per result. Response carries a global hint pointing at cve_lookup — drill into any returned cve_id for full detail and chained pivots (exploit_lookup, kev_detail, cwe_lookup). Free: 30/hr, Pro: 500/hr. Returns {count, total, truncated, offset, summary, results, query_echo, next_offset, verdict, hint}.

List CVEs indexed from MITRE/GHSA BEFORE NVD publication (early-warning, freshest data). By default each result is slim (no description, no cvss_breakdown, no affected_products list, no references) — pass include='full' for the same payload shape as cve_lookup; for drill-down on a single CVE prefer cve_lookup. Use for threat intelligence on emerging CVEs; use cve_search for published NVD data. Verdict (sources_queried, falsifiable_fields, completeness, data_age) is at the response root — applies to the whole batch, not per-row. Response carries a global hint pointing at cve_lookup — drill into any returned cve_id for full detail and chained pivots (exploit_lookup, kev_detail, cwe_lookup). Free: 30/hr, Pro: 500/hr. Returns {count, total, truncated, offset, summary, results, next_offset, verdict, hint}.

Search public exploits/PoC for a specific CVE across three sources: (1) GitHub Advisory Database (sources.github.advisories[]), (2) Shodan CVEDB references (sources.shodan_refs.results[] — packetstorm/seclists/vendor URLs cited by Shodan; results capped at SHODAN_REFS_LIMIT default 200, truncated=true when capped, count is the honest upstream total), (3) ExploitDB CSV mirror (exploits[] array, with edb_id + author + verified flag — these are the actual ExploitDB entries). Use to assess if a vulnerability has weaponized exploits in the wild; run after cve_lookup to evaluate real-world risk. When the CVE is also in CISA KEV (kev.in_kev=true on cve_lookup), pair with kev_detail for federal patch deadline; pair with cwe_lookup on cwe_id for the underlying weakness category and mitigations. Response carries next_calls — single cve_lookup pivot for full context (KEV status, CWE chain, CVSS, EPSS); cve_lookup's own next_calls then surface kev_detail and cwe_lookup automatically (this endpoint has no in_kev/cwe_id schema, so blind emission of those pivots is intentionally avoided). Free: 30/hr, Pro: 500/hr. Returns {cve_id, exploits_found, has_public_exploit, sources: {github, shodan_refs: {found, count, truncated, results}}, exploits: [{edb_id, cve_id, date_published, author, type, platform, url, verified, description}], summary, verdict, next_calls}.

Look up CISA KEV (Known Exploited Vulnerabilities) full record for a CVE. Returns federal patch deadline (due_date), CISA-specified required_action remediation, known ransomware association, vendor/product, the CISA-given common name (e.g. 'Log4Shell'), CISA-reported CWE list, plus lifecycle metadata: date_updated (when CISA last revised the entry), date_removed (set when CISA removed the CVE from the catalog — null while still active), and updated_at (our DB sync freshness). Returns 404 when the CVE is not in the KEV catalog — use cve_lookup for non-KEV CVEs. Best follow-up after cve_lookup or cve_search(kev=true) when an in_kev=true CVE is identified; chain with cwe_lookup on each returned CWE to investigate the weakness category. Free: 30/hr, Pro: 500/hr. Returns {cve_id, vendor_project, product, vulnerability_name, date_added, due_date, required_action, known_ransomware_use, notes, cwes, date_updated, date_removed, updated_at, verdict, next_calls}.

Look up MITRE CWE (Common Weakness Enumeration) catalog record from research view 1000. Default response is SLIM (first 3 mitigations, first 3 examples; extended_description is null) — pass include='full' for the verbose record (full mitigations + examples lists, populated extended_description). Returns description, abstract type (Pillar/Class/Base/Variant/Compound), status (Stable/Draft/Incomplete/Deprecated), exploit likelihood, recommended mitigations, observed example CVEs, parent_cwe (walk up the hierarchy), child_cwes (drill down to more specific weaknesses), and cve_count (LOWER BOUND — counts only CVEs whose primary CWE matches; CVEs with multiple CWEs may not be counted). Use after cve_lookup or kev_detail to understand the underlying weakness category; chain with cve_search(cwe_id=...) to enumerate all matching CVEs. Returns 404 when the CWE is not in research view 1000. Free: 30/hr, Pro: 500/hr. Returns {cwe_id, name, description, extended_description (null on slim, populated on include='full'), abstract_type, status, likelihood, mitigations (first 3 by default), total_mitigations, examples (first 3 by default), total_examples, parent_cwe, child_cwes, cve_count, updated_at, verdict, next_calls}.

Full-text search over the Skill of Skills directory (AI coding skills, plugins, MCP servers across Claude Code, Cursor, Codex, Windsurf, Cline). Results are ranked by the quality-first composite score, not popularity. By default only tools with quality ≥ 40 and risk below high are returned. Scores are evidence, not endorsements.

Full quality profile for one tool by slug (slugs come from search_skills / get_collection): score breakdown, structural signals, risk level and reasons, platforms, repo link. Direct lookups return any active tool regardless of risk — the risk assessment is part of the answer, not a filter here.

Public, k-anonymous demand signal from the directory: trending searches, most-saved and most-installed tools. Aggregates only — sourced from ≥5 distinct anonymous sessions per entry.

Get synsets (word meanings) for a Danish word, returning a sorted list of lexical concepts. DanNet follows the OntoLex-Lemon model where: - Words (ontolex:LexicalEntry) evoke concepts through senses - Synsets (ontolex:LexicalConcept) represent units of meaning - Multiple words can share the same synset (synonyms) - One word can have multiple synsets (polysemy) This function returns all synsets associated with a word, effectively giving you all the different meanings/senses that word can have. Each synset represents a distinct semantic concept with its own definition and semantic relationships. Common patterns in Danish: - Nouns often have multiple senses (e.g., "kage" = cake/lump) - Verbs distinguish motion vs. state (e.g., "løbe" = run/flow) - Check synset's dns:ontologicalType for semantic classification DDO CONNECTION AND SYNSET LABELS: Synset labels are compositions of DDO-derived sense labels, showing all words that express the same meaning. For example: - "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}" = all words meaning "domestic dog" - "{forlygte_§2; babs_§1; bryst_§2; patte_1§1a}" = all words meaning "female breast" Each individual sense label follows DDO structure: - "hund_1§1" = word "hund", entry 1, definition 1 in DDO (ordnet.dk) - "patte_1§1a" = word "patte", entry 1, definition 1, subdefinition a - The § notation connects directly to DDO's definition numbering system This composition reveals the semantic relationships between Danish words and their shared meanings, all traceable back to authoritative DDO lexicographic data. RETURN BEHAVIOR: This function has two possible return modes depending on search results: 1. MULTIPLE RESULTS: Returns List[SearchResult] with basic information for each synset 2. SINGLE RESULT (redirect): Returns full synset data Dict when DanNet automatically redirects to a single synset. This provides immediate access to all semantic relationships, ontological types, sentiment data, and other rich information without requiring a separate get_synset_info() call. The single-result case is equivalent to calling get_synset_info() on the synset, providing the same comprehensive RDF data structure with all semantic relations. Args: query: The Danish word or phrase to search for language: Language for labels and definitions in results (default: "da" for Danish, "en" for English when available) Note: Only Danish words can be searched regardless of this parameter Returns: MULTIPLE RESULTS: List of SearchResult objects with: - word: The lexical form - synset_id: Unique synset identifier (format: synset-NNNNN) - label: Human-readable synset label (e.g., "{kage_1§1}") - definition: Brief semantic definition (may be truncated with "...") SINGLE RESULT: Dict with complete synset data including: - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → semantic types with @set array - dns:sentiment → parsed sentiment (if present) - synset_id → clean identifier for convenience - All semantic relationships and linguistic properties Examples: # Multiple results case results = get_word_synsets("hund") # Returns list of search result dictionaries for all meanings of "hund" # => [{"word": "hund", "synset_id": "synset-3047", ...}, ...] # Single result case (redirect) result = get_word_synsets("svinkeærinde") # Returns complete synset data for unique word # => {'wn:hypernym': 'dn:synset-11677', 'dns:sentiment': {...}, ...}