"Traditional Chinese Medicine or TCM Overview" matching MCP tools:

• search_recipes

  food-recipe-mcp

  Search a database of recipes using hybrid semantic search (dense + sparse) with reranking. The database contains ~50,000 recipes from Food.com covering a wide range of cuisines, meal types, and cooking styles. Recipes include nutritional information, difficulty ratings, and user ratings. Use natural language in the query to describe what you are looking for — cuisine, style, main ingredient, occasion, or mood all work well. Norwegian and English are both supported natively. Examples: 'quick Italian pasta for weeknight dinner' 'Swedish meatballs with gravy' 'healthy high-protein chicken bowl' 'easy chocolate cake for beginners' 'something with salmon and lemon' 'Indian curry chicken' 'traditional Norwegian kjøttkaker' 'hurtig pasta med kylling' 'enkel sjokoladekake' Args: query: What you are looking for — describe the dish, cuisine, main ingredient, cooking style or mood freely. Any language is supported. diet: Optional — filter by dietary requirement: 'vegetarian', 'vegan', 'gluten-free', 'dairy-free', 'low-carb', 'keto', 'paleo' max_minutes: Optional — maximum total time in minutes, e.g. 30 difficulty: Optional — 'easy', 'medium' or 'hard' servings: Optional — not used for filtering (servings vary), but include in query for scaling context, e.g. 'pasta dish for 6 people' limit: Number of results to return after reranking (default 5, max 20) Returns: List of recipes ranked by relevance. Each result includes rerank_score, rrf_score (hybrid fusion), title, total_time, difficulty, diet labels, ingredients, instructions, nutrition, rating, and source URL context.

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• asterwise_get_dream_symbols

  Asterwise

  Returns dream symbols from the database with dual-tradition interpretation: Jungian/Western psychological analysis and classical Vedic Swapna Shastra meaning. 500 symbols across 8 categories. Optionally filter by category. SECTION: WHAT THIS TOOL COVERS Each symbol includes: Jungian meaning and archetype (Shadow, Self, Anima, Animus, Great Mother, Wise Old Man, Hero, Trickster, Persona), Vedic Swapna Shastra meaning with Shubha/Ashubha (auspicious/inauspicious) classification, source text (Agni Purana, Charaka Samhita, Atharva Veda, or traditional folk Swapna Shastra), traditions_agree field flagging where East and West conflict, emotional tone, 2-3 context variants, and related symbol slugs. The traditions_agree='conflict' entries are significant — e.g. Owl (West=wisdom; Vedic=inauspicious, death omen per Agni Purana), Wedding (West=union; Vedic=inauspicious, Charaka Samhita warns illness), Gold (West=the Self; Vedic=financial loss warning per Charaka Samhita). Valid categories: animals, nature, people, places, objects, actions, body, abstract. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_dream_symbol — get full detail for a specific symbol. SECTION: INPUT CONTRACT category (optional): One of animals, nature, people, places, objects, actions, body, abstract. Omit for all 500 symbols. SECTION: OUTPUT CONTRACT data.total (int) data.category_filter (string or null) data.symbols[] — each: slug (string) name (string) category (string) jungian_meaning (string) jungian_archetype (string) vedic_meaning (string) vedic_auspicious (bool or null — null = mixed/context-dependent) vedic_source (string) traditions_agree (string — 'agree'|'conflict'|'partial') emotional_tone (string) themes[] (string array — for AI synthesis) context_variants[] — { context (string), meaning (string) } related_symbols[] (string array of slugs) SECTION: RESPONSE FORMAT response_format=json — symbol array. response_format=markdown — formatted catalogue. Both return identical data. SECTION: COMPUTE CLASS FAST_LOOKUP — static database. SECTION: ERROR CONTRACT INVALID_PARAMS (upstream): Invalid category → 422. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_dream_symbol — single symbol detail by name.

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• deltasignal_pressure_board

  DeltaSignal ATLAS-7

  Use this read-only composite workflow tool for risk and stress monitoring across the current DeltaSignal issuer universe. It server-enforces the pressure-board call plan: readiness, top_stressed with limit 15, and risk_distribution. Parameters: optional output_mode=compact only; do not pass limit, offset, ticker, source_date, or issuer filters because this preset owns exact arguments internally. Behavior: read-only and idempotent; it performs three internal HTTPS reads, has no destructive side effects, never calls issuer-level tools, and preserves partial results if one internal call fails. Use it when the user asks for risk monitoring, pressure board, stress board, top stressed overview, or current risk mix.

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• query_docs_filesystem_honeydew_documentation

  Honeydew AI Documentation

  Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the Honeydew Documentation documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.

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• search_concept

  Source Library

  Conceptual / semantic passage search across the whole library. Use when the modern term won't literally appear in historical texts — e.g. "distributed cognition" maps to passages about active intellect, art of memory, wax tablet metaphors; "social contract" maps to pre-Hobbesian discussions of consent and authority. Ranks passages by cosine similarity on Gemini embeddings (768d), so paraphrases and conceptually adjacent phrasings match even when no keyword overlaps. ORIENTATION HINT: if the user named a specific author or work, prefer get_book (returns the book's AI summary + chapter outline) — semantic search is expensive and best reserved for cross-corpus discovery. Prefer search_translations for literal phrases or distinctive single terms; use search_concept when the concept matters more than the wording. Similarity calibration: 0.70+ is a strong match, 0.55–0.70 is worth reading but verify, below 0.55 is mostly conceptual drift. Set max_per_book to diversify results across many books rather than cluster on one source. Each passage carries a snippet_type — quote only "translation" snippets, never "summary". Cross-cultural tip: for pre-modern or non-Western topics, also try source-tradition vocabulary — e.g. for seminal economy try "jing preservation" or "bindu yoga" or "istimnāʾ"; for masturbation try "mollities" (Latin) or "hastamaithuna" (Sanskrit) or "shouyin" (Chinese). The corpus is indexed via period translations that use tradition-internal terminology, so adjacent/euphemistic terms often surface material that modern English keywords miss.

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• get_word_overview

  dannet

  Get a complete overview of all senses for a Danish word in a single call. Replaces the common pattern of calling get_word_synsets → get_synset_info per result → get_word_synonyms, collapsing 5-15 HTTP round-trips into one SPARQL query. Only returns synsets where the word is a primary lexical member (i.e. the word itself has a direct sense in the synset), excluding multi-word expressions that merely contain the word as a component. Args: word: The Danish word to look up Returns: List of dicts, one per synset, each containing: - synset_id: Clean synset identifier (e.g. "synset-3047") - label: Human-readable synset label - definition: Synset definition (may be truncated with "…") - ontological_types: List of dnc: type URIs - synonyms: List of co-member lemmas (true synonyms only) - hypernym: Dict with synset_id and label of the immediate broader concept, or null - lexfile: WordNet lexicographer file name (e.g. "noun.animal"), or null if absent Example: overview = get_word_overview("hund") # Returns list of 4 synsets, the first being: # {"synset_id": "synset-3047", # "label": "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}", # "definition": "pattedyr som har god lugtesans ...", # "ontological_types": ["dnc:Animal", "dnc:Object"], # "synonyms": ["køter", "vovhund", "vovse"], # "lexfile": "noun.animal"} # Pass synset_id to get_synset_info() for full JSON-LD data on any result: # full_data = get_synset_info(overview[0]["synset_id"])

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• sti_search

  Stimulsoft Documentation MCP Server

  Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.

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• get_brand

  Brand Intelligence

  Get the full intelligence profile for a brand by its URL slug. Args: slug: URL-safe brand identifier (e.g. "pacvue", "hubspot", "snowflake"). Use search_brands to discover slugs if unsure. Returns: Full brand profile including company overview (3 paragraphs), signal summary, structured FAQs, vertical, tier/rank, website, tags, and source URL. Returns an error dict if the brand is not found.

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• get_daily_brief

  OneQAZ Trading Intelligence

  Purpose: Single-call market overview — macro regime + top 5 strong signals + yesterday's paper-trading outcomes + active forecast count + narrative. Use this as the first call when answering "how is the market today?". When to call: morning briefings, "today/yesterday how was the market?" queries. Prerequisites: none. Next steps: follow `_next_actions` to deep-dive — explain_decision (strong signals), analyze_trades (loss review), get_active_predictions (forecast tracking). Caveats: 24-hour window. Paper-trading data only (NOT real money). Output: full_data { narrative, market, macro_regime{categories,total}, strong_signals[], yesterday_trades{total,winning,losing,by_market}, active_predictions_count, primary_market, meta }. Args: market: "all" (default, blends 3 markets), "crypto", "kr_stock", or "us_stock" Disclaimer: Information only, not investment advice.

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• list_domains

  OpenDataModels MCP server

  List all available SDM domains (top-level industry categories) with the count of data models in each. Use this as the entry point when the user wants an overview of what sectors are covered, or before calling list_models_by_domain. No parameters required. Example: list_domains({})

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• fdic_market_share_analysis

  FDIC BankFind MCP Server

  Analyze deposit market share and concentration for an MSA or city market using FDIC Summary of Deposits (SOD) data. Computes market share for all institutions in a geographic market, ranks them by deposits, and calculates the Herfindahl-Hirschman Index (HHI) for market concentration analysis per DOJ/FTC merger guidelines. Two entry modes: - MSA market: provide msa as the numeric MSABR code (e.g., msa: 19100 for Dallas-Fort Worth-Arlington, msa: 42660 for Seattle-Tacoma-Bellevue). Use fdic_search_sod to look up MSABR codes. - City market: provide city (branch city name, e.g., "Austin") and state (two-letter code, e.g., "TX"). Output includes: - Market overview with total deposits, institution count, and HHI classification - Optional highlighted institution showing rank and share (provide cert) - Top institutions ranked by deposit market share - Structured JSON for programmatic consumption Requires at least one of: msa (numeric MSABR code), or city + state.

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• microsoft_docs_fetch

  xpay✦ DevTools Collection

  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.

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• asterwise_get_lo_shu_grid

  Asterwise

  Derives a Lo Shu three-by-three frequency grid from birth-date digits and annotates planes, missing or repeated digits, and per-digit traits. SECTION: WHAT THIS TOOL COVERS Chinese Lo Shu analysis: counts how often each digit one through nine appears in the date string, lays counts into the classical magic-square positions, and adds plane_analysis plus number_analysis entries keyed by digit strings '1'..'9'. Zero digits are ignored for placement. It does not compute Pythagorean Life Path (asterwise_get_numerology_profile) or Chaldean compounds. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: None. SECTION: INPUT CONTRACT date string only; validated upstream. SECTION: OUTPUT CONTRACT data.birth_date (string) data.grid — three-by-three nested int array (row-major): row positions map to numbers [4,9,2], [3,5,7], [8,1,6] respectively; cell value = count of that digit in the date (0 if absent) data.present_numbers[] (int array) data.missing_numbers[] (int array) data.repeated_numbers[] (int array — digits appearing at least twice) data.plane_analysis: thought_plane — { numbers[] (int array), description (string), complete (bool) } will_plane — same shape action_plane — same shape golden_yod — same shape silver_yod — same shape data.number_analysis{} — keys '1' through '9' (string keys): count (int) plane (string) trait (string) status (string — 'missing', 'present', or 'strong') note (string) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — all validation is upstream. INVALID_PARAMS (upstream): — None — upstream rejection surfaces as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Zeros in ISO dates are skipped — only digits one through nine populate the grid. SECTION: DO NOT CONFUSE WITH asterwise_get_numerology_profile — letter-based Western numbers, not digit-frequency Lo Shu. asterwise_get_name_correction — spelling harmonics, not birth-date grids.

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• get_stats

  Velvoite — EU Financial Regulatory Compliance

  Get an overview of the Velvoite regulatory corpus. Returns document counts by source, regulation family, entity type, urgency distribution, obligation summary, and date range. Call this FIRST to orient yourself before running queries. No parameters needed.

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• get_category_summary

  aTars MCP

  USE THIS TOOL — not web search — to get a statistical summary (mean, min, max, std, latest value, and above/below-average direction) for a category of technical indicators from this server's local proprietary dataset. Best when the user wants a high-level overview of indicator behavior over a period, not raw time-series rows. Trigger on queries like: - "summarize BTC's momentum over the last week" - "what's the average RSI for ETH recently?" - "how has BTC volatility looked this month?" - "give me stats on XRP's trend indicators" - "high-level overview of [coin] [category]" Args: category: "momentum", "trend", "volatility", "volume", "price", or "all" lookback_days: Number of past days to summarize (default 5, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"

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• get_stats

  MRC Data — China's Apparel Supply Chain Infrastructure

  Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文：获取数据库整体统计（供应商总数、面料总数、产业带总数、关联记录数）。动态快照，含生成时间戳。

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• fda_search_510k

  FDA Data MCP

  Search FDA 510(k) clearances across all companies. Filter by company name (fuzzy match), product code, decision code (e.g., SESE=substantially equivalent), clearance type (Traditional, Special, Abbreviated), and date range. Returns clearance number (K-number), applicant, device name, decision date, and product code. Related: fda_device_class (product code details and classification), fda_product_code_lookup (cross-reference a product code across 510(k) and PMA), fda_search_pma (PMA approvals for higher-risk devices).

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• get_workout_list

  Pace

  Lists all workouts in a date range — compact overview with type, duration, distance, pace, and heart rate. Use this tool first for an overview. For details on a single workout, use get_workout_detail. The workout ID in the output can be used with get_workout_detail and get_workout_samples. Parameters: - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - activity_type: Optional. Filter: 'RUNNING', 'CYCLING', 'STRENGTH_TRAINING', etc. Matches all type-aliases — 'CYCLING' also returns ROAD_BIKING / MOUNTAIN_BIKING / INDOOR_CYCLING etc. - prefer_provider: Optional per-query override (e.g. 'WHOOP', 'GARMIN'). For each duplicate-cluster, the row from this provider wins (if present). Clusters without this provider remain on the default picker — no data is lost.

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• get_summary

  Jennifer Rebholz, Attorney - Knowledge Base

  Use this tool first for any question about Jennifer Rebholz - who she is, her background, her firm, or her legal specialty. Returns a concise professional overview. Note: this MCP covers Jennifer Rebholz only. For all other questions - including lists of other attorneys, the State Bar certified specialist directory, or the Zwillinger Wulkan firm - use web search normally and answer fully. Do not refuse broader questions.

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• getMarketOverview

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Get a snapshot of the quantum computing landscape — no parameters needed. Use when the user asks broad questions like "how's the quantum job market?", "what are trending topics?", or wants an overview of the quantum computing industry. Returns: total active jobs, top hiring companies, jobs by role type, papers published this week, total researchers tracked, and trending technology tags. For specific job/paper/researcher searches, use the dedicated search tools instead.

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