"Guidance for writing a conclusion for a long text" matching MCP tools:

• check_textA

  WSC - Writing Style CheckerA

  Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.

  ConnectorNo auth

• cti_get_guidelinesA

  website-searchA

  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.

  ConnectorNo auth

• adcritter_get_platform_overviewA

  AdCritter for BuildersA

  Start here. Returns the AdCritter platform overview - what AdCritter is, the entity hierarchy (organization > advertiser > campaign > ad), the happy path for getting ads running, and how to navigate the other MCP tools. Applications built from this guidance are REST API clients that call /v1/ endpoints, not MCP tool callers. Before writing code, call adcritter_get_api_reference(entity, action) for each entity and action you plan to use - tool descriptions and parameter names describe conceptual behavior only, and do not match actual API routes, field names, query parameters, or response shapes.

  ConnectorNo auth

• migrate_to_propelauth_frontendA

  PropelAuth Integration MCP ServerA

  Returns instructions for migrating to PropelAuth in a frontend framework such as React, JavaScript, TypeScript, or when using Next.js for just the frontend (e.g. client-side rendered). Guidance includes migrating from several auth providers, such as Clerk or Auth0. Each guidance will include documentation from the auth provider and PropelAuth. It is important to follow the instructions carefully to ensure a successful integration. Make sure to use the 'Installation' guidance first. It is important to call every guidance to ensure a successful integration. Do not update a component/hook/etc from the auth provider until you receive guidance about that component/hook/etc. CRITICAL: If the current implementation uses a traditional OAuth/OIDC flow (e.g., via express-openid-connect, passport-auth0, or similar backend-managed session libraries), you MUST select 'OAuth' as the framework, regardless of the frontend library (React/Vue/etc.). Only select 'React' or 'Javascript' if the current implementation uses a frontend-only SDK (like @auth0/auth0-react) or if using fullstack Next.js.

  ConnectorNo auth

• check_textA

  Writing Style CheckerA

  Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.

  ConnectorNo auth

• voice_fingerprintA

  Moltline Humanizer — Voice-Matching EditorA

  Build a measurable voice profile from samples of a person's real writing. FREE. Feed it 2+ samples (emails, posts, essays — 150+ words total) and use the result with humanize_plan / verify_rewrite. Typical input {"samples": ["<email text>", "<blog post>"]} returns {"label": "my-voice", "target_metrics": {"avg_sentence_len": ..., "burstiness": ..., ...}, "favorite_words": [...], "signature_habits": ["..."], "words_analyzed": N}. Use on samples the person actually wrote, to build a target profile. Not for scoring an unknown draft (ai_tell_scan) and not on text the person did not write. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "need 150+ words of real writing across the samples"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• voice_fingerprintA

  Moltline HumanizerA

  Build a measurable voice profile from samples of a person's real writing. FREE. Feed it 2+ samples (emails, posts, essays — 150+ words total) and use the result with humanize_plan / verify_rewrite. Typical input {"samples": ["<email text>", "<blog post>"]} returns {"label": "my-voice", "target_metrics": {"avg_sentence_len": ..., "burstiness": ..., ...}, "favorite_words": [...], "signature_habits": ["..."], "words_analyzed": N}. Use on samples the person actually wrote, to build a target profile. Not for scoring an unknown draft (ai_tell_scan) and not on text the person did not write. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "need 150+ words of real writing across the samples"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• yetty_authoring_guideA

  yettyA

  READ THIS BEFORE WRITING OR SENDING ANY HTML (no account needed, free, instant, no arguments). Returns yetty's exact structure rules — repeated items sharing one class, semantic class names for field labels, headings before sections, no JS-rendered content — that decide whether the site converts into a clean editable CMS with named fields and add/remove collections, or a wall of generic "Text" fields.

  ConnectorNo auth

• update_pcd_skillA

  PCD (Portable Context Deck)B

  Revise an existing skill’s instructions, discovery guidance or ownership; supports targeted patches. Use create_pcd_skill for a new method.

  ConnectorOAuth

• generate_blog_outlineA

  Razi ToolsA

  Produce a markdown heading structure for a blog post — title, introduction, numbered sections with subsections, conclusion and an FAQ block. Returns JSON { outline } holding the markdown. It writes the skeleton only, not the article: use generate_text for body prose and humanize_text to rework text that already exists. Paid model call, capped at roughly 1,000 tokens, so a large section count yields thinner sections. 10 calls per minute per IP; identical requests may return a cached outline.

  ConnectorNo auth

• generate_blog_outlineA

  Razi Text GenerationA

  Produce a markdown heading structure for a blog post — title, introduction, numbered sections with subsections, conclusion and an FAQ block. Returns JSON { outline } holding the markdown. It writes the skeleton only, not the article: use generate_text for body prose and humanize_text to rework text that already exists. Paid model call, capped at roughly 1,000 tokens, so a large section count yields thinner sections. 10 calls per minute per IP; identical requests may return a cached outline.

  ConnectorNo auth

• get_earnings_call_summaryA

  TipRanksA

  Returns the most recent earnings call summary for a ticker — management guidance text, overall call sentiment (positive / neutral / negative with a one-line rationale), and AI-extracted highlights and lowlights from the call as {title, content} bullets. This is a structured summary derived from the call, not the raw transcript text. Useful for "what did management say about X on the last call", "was the most recent call bullish or bearish", or "summarise the highlights from MSFT's latest earnings". Only the most recent quarter is stored per ticker; for historical EPS actual-vs-estimate series use get_earnings_history. Args: ticker: Stock ticker (e.g. 'AAPL', 'NVDA'). Returns: { ticker, fiscal_year, fiscal_quarter, guidance, sentiment: { label, summary }, highlights: [ { title, content }, ... ], lowlights: [ { title, content }, ... ] }

  ConnectorNo auth

• cortex_rememberA

  mitosisA

  Persist a fact, decision or conclusion into the memory, attributed to you. It becomes retrievable via cortex_ask immediately, in this session and every future one, from any agent the user has connected. Provenance: pass source_universal_ids from a previous cortex_ask so the fact links to its evidence. Keep each memory to ONE self-contained fact. Use this whenever the conversation produces a durable conclusion the user would want remembered — a decision, a preference, an outcome, a commitment. If the result carries `choice.choice_required: true`, the memories source is waiting for the user’s enrichment choice: ask `choice.question`, offer exactly "Standard" or "Describe your goal", and record the answer with cortex_choose_enrichment.

  ConnectorNo auth

• ai_tell_scanA

  Moltline Humanizer — Voice-Matching EditorA

  Scan a draft for the measurable tells of AI-generated prose. FREE. Flags stock phrases (with exact quotes), structural reflexes, uniform sentence rhythm, em-dash overuse, and hedging boilerplate — every flag cites the actual text. Typical input {"text": "<draft>"} returns {"reads_human_score": 0-100, "metrics": {"burstiness": ..., "avg_sentence_len": ..., ...}, "evidence": [{"type": "stock_phrase", "quote": "..."}], "note": "..."}. Use for a first read on whether a draft carries machine-writing signals. Reports measurable patterns, not a verdict on who wrote the text, and must not be used to accuse a person of anything. Not for rhythm detail (burstiness_report) or for a rewrite brief (humanize_plan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• ai_tell_scanA

  Moltline HumanizerA

  Scan a draft for the measurable tells of AI-generated prose. FREE. Flags stock phrases (with exact quotes), structural reflexes, uniform sentence rhythm, em-dash overuse, and hedging boilerplate — every flag cites the actual text. Typical input {"text": "<draft>"} returns {"reads_human_score": 0-100, "metrics": {"burstiness": ..., "avg_sentence_len": ..., ...}, "evidence": [{"type": "stock_phrase", "quote": "..."}], "note": "..."}. Use for a first read on whether a draft carries machine-writing signals. Reports measurable patterns, not a verdict on who wrote the text, and must not be used to accuse a person of anything. Not for rhythm detail (burstiness_report) or for a rewrite brief (humanize_plan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• analyze_schemaA

  Entity EnricherA

  Analyze a saved schema's property ambiguity and relationship identity scoping, writing annotations to the schema. Requires editor; billed. Incremental by default; force=true rechecks all sites. Returns findings, suggested descriptions, identity-scoping annotations and pending unification proposals; it does not apply suggested structural changes. Fails with ambiguity_check_disabled if the feature is off. Use update_schema_property for approved descriptions or renames; review structural changes and publish them when linked. Interpretation and modeling guidance: enricher://docs/schema-reference.

  ConnectorOAuth

• get_ir_documentsA

  MetricDuck — Financial AnalysisA

  Retrieve IR earnings-PRESENTATION-DECK text — forward guidance, operational KPIs, and segment outlook that are ONLY in the company's investor-relations slide deck and NOT in the SEC 8-K/10-Q release text or XBRL. Reach for this when get_metric_history / get_xbrl_facts have no series for a KPI or guidance figure, or the 8-K earnings release from get_filing_section lacks it (decks are a separate source): production or revenue guidance ranges, segment/division outlook, KPIs shown as slide charts (e.g., berth capacity %, Mboed production guidance, adjusted-EBITDA guidance). Use Cases: - "OXY Q3 2024 production guidance" -> get_ir_documents("OXY", fiscal_year=2024, fiscal_period="Q3", query="production guidance") - "NCLH berth capacity outlook" -> get_ir_documents("NCLH", fiscal_year=2021, fiscal_period="Q3", query="berth") - "KNTK adjusted EBITDA guidance range" -> get_ir_documents("KNTK", fiscal_year=2023, fiscal_period="Q3", query="EBITDA") Each deck returns its title, original IR url, a stable MetricDuck-hosted gcs_uri, and the matching slide text cited by page. Pass a `query` to land on the exact page; omit it for a bounded prefix of the latest deck. Resolve by ticker or cik; narrow with fiscal_year/fiscal_period.

  ConnectorAPI key

• read_observatory_signalsA

  Kaneme — Your writing identity, measured and portableA

  Reads a text with the Observatoire method (frozen copy, `methodVersion` returned): passages to reread, each with its criterion (G01 generic phrasing, S01 announced conclusion, V01 claim without visible evidence, R01 near-repeated vocabulary, S02 repeated opening, D01 long sentence), UTF-16 positions, the quoted passage and editorial advice. No account, no credits, nothing stored. It is NOT an AI detector, NOT a proof of authorship and has NO score: never turn the number of passages into a verdict, and never merge it with a Kaneme measurement. Labels and advice are in French: relay them in the user’s language. Language is detected when omitted; English rules are experimental. To rewrite the text in the user’s own voice, use improve_in_voice (account required).

  ConnectorNo auth

• url_scanner_scanA

  PreClick — An MCP-native URL preflight scanning service for autonomous agents (formerly URLCheck).A

  Analyze a URL for security threats (synchronous, blocks until complete or timeout). Returns risk score, confidence, agent access guidance, and intent_alignment (always not_provided for this tool; use url_scanner_scan_with_intent for intent context). For long-running scans, prefer url_scanner_async_scan which returns immediately with a task_id for polling via url_scanner_async_task_result.

  ConnectorNo auth

• get_security_writing_guidelinesA

  website-searchA

  Get Lenny Zeltser's expert writing guidelines for security reports and assessments. Provides guidance on tone, structure, clarity, executive summaries, and avoiding common writing mistakes. Includes rating-sheet items (the four lens sheets: structure, look, words, tone) as concrete reference points for grounded feedback. Works for any security document. This server never requests your documents and instructs your AI to keep them local—guidelines flow to your AI for local analysis. Note: For incident response reports specifically, use the ir_* tools which provide deeper section-by-section review criteria.

  ConnectorNo auth

• assessment_get_guidelinesA

  website-searchA

  Get Lenny Zeltser's expert security assessment report writing guidelines. Topics: severity (the risk-adjusted severity model — the spine), findings, remediation, methodology, scope, strengths, brief (one-page brief section guidance), executive_summary, analysis, anti_patterns, frameworks, handoffs, and summary. The general 'tone' topic defers to `get_security_writing_guidelines` for the canonical Five Elements rules. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.

  ConnectorNo auth