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532,965 tools. Updated 2026-09-08 07:46

"Help with Writing Articles" matching MCP tools:

  • 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 official Microsoft Knowledge Base articles on support.microsoft.com by topic or keyword — use for Windows update, patch, and known-issue lookups when you lack a KB number. Returns matching KB article titles and URLs. Use get_kb_article to fetch the full content of a specific article. Returns: Dictionary with 'results' key containing list of matching KB articles with title and url.
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  • List articles from a buyer's licensed catalog via GET /enterprise-license (Phase 10 + 11). Content contract: flat-fee scopes (custom/platform_wide) include full content_body; METERED (filtered-scope) keys get a discovery-only feed — content_body is null and content_access is 'metered_per_call'; fetch article text via get_content (each retrieval is billed). Returns JSON-format response with paginated articles. Use `since` (ISO 8601) for delta-feed polling — only articles published after the timestamp. Use `cursor` for pagination across pages. Requires OPEDD_ACCESS_KEY (ent_* enterprise access key). For larger bulk corpus pulls, use stream_feed_ndjson (up to 1000 articles per call vs 200 here).
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  • Filter articles by gpt-5-6-luna sentiment labels (accent/case-insensitive exact match). One model's reading, not a consensus — 4 other models scored the same articles and often disagree; get_sentiment_distribution with model:"all" shows by how much. `subjectivity` is much the weakest of the three scales, so treat a set selected on it as a lead to read rather than as a finding.
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  • Returns the financial-blogger consensus for a stock plus the underlying blogger articles. Distinct from get_recent_analyst_ratings (Wall Street analysts) and get_investor_sentiment (TipRanks crowd positioning). Args: ticker: Stock ticker (e.g. 'AAPL') limit: Max blogger articles to return (default 20, max 50) Returns JSON: {ticker, company, consensus, articles}. - consensus: {bullish_pct, bearish_pct, neutral_pct, bullish_count, bearish_count, neutral_count, score, avg}. - articles: [{blogger, title, url, site, date}] (newest first).
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  • Extract voice primitives (register / sentence rhythm / lexicon preferences / punctuation habits) from post-shaped text and persist onto the user's VoiceProfile. The voice primitives thread into content generation so generated copy matches the user's actual writing voice. Two input shapes: pass `posts` (list of pre-collected text snippets, ≥80 chars each) or pass `url` (the server scrapes post-shaped snippets from the page: Substack / Medium / blog / X profile). Inline posts win when both are given. Inline post-shaped snippets need to be the user's own writing, not press articles or marketing copy. Returns the extracted primitives + a diff of what changed on the stored VoiceProfile.
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  • List available Disco plans with pricing. No authentication required. Returns all available subscription tiers with credit allowances and pricing. Use this to help users choose a plan.
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  • Get comprehensive bias analysis for a news source. Returns: - source_name, slug_name, page_url - source_match: original query and deterministic match method - articles_analyzed: total articles in the bias database for this source - last_updated: source-profile aggregation timestamp - avg_social_shares: average social shares per article - emotionality_score (0-10): how emotional the writing is - prescriptiveness_score (0-10): how much the source tells readers what to think/do - bias_values: canonical plain-text source-level weighted display scores (-50 to +50 bipolar, 0 to +50 unipolar). Keys match the article tools; these are directional source summaries, not raw article-score averages. - bias_scores: legacy emoji-prefixed display scores - bias_score_methodology: scope and evidence caveats for aggregate scores - bias_description: clean-text, AI-generated overall bias summary narrative - bias_description_metadata: generation time, automated review status, and evidence scope - bias_description_html: optional website HTML when include_html=true - liberal_conservative_description: narrative on political leaning - libertarian_authoritarian_description: narrative on authority stance - signature_phrases: words/phrases uniquely overrepresented vs other sources - signature_negative_phrases: uniquely negative/alarming phrases - most_shared_phrases: phrases in their most viral articles - most_emotional_phrases: phrases used in their most emotional articles - pays_for_traffic_keywords: keywords this source buys ads for - similar_sources: sources with the most similar bias profile - most_different_sources: sources with the most different bias profile - trends_graph_url: URL to a chart of this source's coverage volume over time - bias_plot_urls: dict of 2D bias scatter plot image URLs (political_lib_auth, subjective_objective, informative_opinion, oversimplification_factful) — only present when available - recent_articles: list of most recent articles with full article fields, bias_values, analysis status, and optional self-contained bias_dimensions and bias_analysis. Evidence quotes include verification method and exact character offsets when available. - recent_evidence_coverage: reconciled counts for verified, unverified, partial, legacy-scored, and pending articles, plus evidence-bearing count and verified ratio Throws an error if the source is not found. Args: source: Source name, slug, or domain (e.g. 'Fox', 'reuters', 'bbc.co.uk'). Partial names are accepted only when they identify one source; ambiguous input returns candidates. recent_articles: Number of recent articles to include (1-50, default 10). include_evidence: Include per-article claims, verbatim evidence, counterevidence, confidence, rationale, and limitations. Defaults to false to keep multi-article source payloads compact. include_html: Also return the original website-formatted source narrative. Defaults to false.
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  • 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.
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  • Day-by-day SHARE OF GLOBAL NEWS attention for a query — what % of all worldwide articles mentioned this topic each day. Returns datapoints with timestamp and intensity (% of total news volume). Use to detect news-cycle spikes around events ("when did attention to X peak?"), benchmark attention against history, or pair with timeline_tone to chart sentiment vs interest together. Cheaper than search_articles when you only need the volume curve, not the source articles themselves.
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  • List every available Lorg tool with a plain-English description. Call this when the user says /help, /options, "what can you do", or "show me available commands".
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  • Read-only: returns the FULL text of published knowledge-base articles in this tenant, by id. Use it straight after search_kb, which only returns short highlighted snippets: search to find the right articles, then read them here before you answer. Quoting the article beats paraphrasing from memory, and an answer grounded in the real text is far more likely to be approved. Free to call. Drafts and other tenants' articles are never returned. [free]
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  • Create a new, empty series — a named collection that related articles can be added to. Creating the series does not move any article into it; follow up with add_to_series for each one. Call get_series first to avoid making a second series with the same title, since each call creates a NEW series and nothing deduplicates them. Requires an API key. The series and its URL become publicly reachable, though it shows nothing until articles are added. Returns the series with the slug that add_to_series needs.
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  • Search news articles. Returns a list of matching articles. Each article includes: - article_id, classification_id, title, source, date, link, category, rank, total_shares, summary - bias_values: dict of per-dimension bias scores using plain-text keys (e.g. 'liberal conservative bias'), same schema as get_bias_from_url and get_all_source_biases (when available) - bias_analysis_status: 'evidence_ready', 'evidence_unverified', 'evidence_partial', 'scored_legacy', or 'pending' - evidence_ratio: fraction of scored bias dimensions whose supporting quote is verified (0.0-1.0). Raise min_evidence to demand only articles with verified quotes. - bias_dimensions when include_evidence=true: a self-contained object joining each score, scale, evidence status, claim, evidence, counterevidence, confidence, and rationale. Quotes include verification method and exact character offsets when raw-text matching succeeds. Dimension evidence_status is one of: verified, provided_unchecked, quote_mismatch, metadata_incomplete, metadata_only, or missing. - bias_analysis: contract/schema/model/prompt provenance, generation and review status, input scope/hash/size, limitations, quote-verification method, and explicit evidence coverage - context: AI-generated contextual background for the article (when available) - implicit_assumptions: tacit or unstated premises the article's claims or framing rely on (list of concise strings, when available) - extracted_data: structured quantitative/qualitative facts extracted from the article - raw_data: legacy serialized form of extracted_data Args: query: Optional search keywords. Leave empty to return the most recent articles in scope (use with bias to rank them). e.g. 'NVDA earnings'. limit: Max results (1-100, default 20). source: Filter by source name, e.g. 'CNN', 'Reuters'. category: Filter by category. One of: 'trending', 'tech', 'markets', 'politics', 'business', 'science', 'memes'. days_back: Only include articles from the last N days. 0 means no date filter. Default: 720 (2 years). min_shares: Minimum total social shares. sort: Sort order. One of: 'rank' (relevance, default), 'date' (newest), 'shares' (most shared). bias: Optional bias dimension to sort by. Returns the most recent articles ranked highest on that bias, highest score first. Any canonical bias key, e.g. 'liberal conservative bias', 'overall credibility', 'conspiracy bias'. Works with an empty query for a standalone 'most biased recent articles' listing. Use with only_analyzed if you want. include_evidence: Include claim-level evidence, counterevidence, confidence, rationale, and limitations. Defaults to false to keep search payloads compact. only_analyzed: Return only articles with valid canonical bias scores. min_evidence: Minimum fraction of scored dimensions with verified quotes (0.0-1.0, default 0). Raise this to request only articles whose scores are backed by verified evidence, e.g. 0.5. Returns a 400 if sort or bias is not a valid option.
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  • Read the full source markdown of a Capawesome documentation or blog page. Use this after `search_docs` to read a page end to end before writing code against a plugin API, a CLI command or a Capawesome Cloud workflow — snippets from search results are deliberately short and regularly omit required configuration steps. Returns the page markdown including code samples, exactly as it is authored. Do not use this to find a page: it needs the exact URL or path, so run `search_docs` (or `list_blog_posts` for articles) first. Do not use it for pages outside capawesome.io.
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  • List available MCP tools and get detailed help. Use this tool to discover what tools are available and how to use them. Call without parameters to see all tools, or provide a tool name to get detailed help including parameters, examples, and related tools.
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  • Validate formula syntax with the WorkPaper parser before writing it to a cell. This checks syntax only; use set_cell_contents plus readback to evaluate.
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  • Return 787daily articles that mention a specific named entity (person, organization, or place), newest-first. Match is on the entity's canonical name (case-insensitive). Returns { name, count, articles[] }.
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  • Create a new, empty series — a named collection that related articles can be added to. Creating the series does not move any article into it; follow up with add_to_series for each one. Call get_series first to avoid making a second series with the same title, since each call creates a NEW series and nothing deduplicates them. Requires an API key. The series and its URL become publicly reachable, though it shows nothing until articles are added. Returns the series with the slug that add_to_series needs.
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  • Get Lenny Zeltser's expert writing guidelines for incident response reports. Topics: tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance, IR 1.5.0+), frameworks (regulatory + maturity frameworks), handoffs (cross-server routing). When the topic maps to a lens (tone, words, structure), the response includes a rating-sheet checklist appendix as concrete reference points for grounded feedback. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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