"MySQL Query Basics and Examples" matching MCP tools:

• list_entities

  TunnelMind Data API

  Returns a paginated list of corporate entities in the TunnelMind surveillance database. Includes data categories, estimated data value, and industry classification. Useful for enumerating the surveillance ecosystem by sector. Use this tool when: - You want to enumerate all entities in a specific industry (e.g., all ad-tech companies). - You need a dataset of surveillance entities for analysis or reporting. - You are building a comprehensive surveillance landscape map. Do NOT use this tool when: - You need the full profile of a specific entity — use `get_entity` instead. - You are searching by entity name — use `search` instead. - You need domain-level data — use `list_domains` instead. Inputs: - `industry` (query, optional): Filter by industry classification. Examples: `ad_tech`, `analytics`, `data_broker`, `social`, `crm`. - `limit` (query, optional): Results per page. Max 100 (paid), 20 (free). Default 50. - `cursor` (query, optional): Pagination cursor from previous response's `next_cursor`. Returns: - Array of entity list items (slug, name, parent_company, industry, data_categories, data_cost_usd). - `meta.has_more` and `meta.next_cursor` for pagination. Cost: - Free tier: up to 20 results/page, 50 req/day. Pro/enterprise: up to 100 results/page. Latency: - Typical: <150ms, p99: <400ms.

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• pokeapi_find_pokemon

  pokeapi-mcp-server

  Filter Pokémon by generation, type, regional pokédex, or egg group. Returns names and Pokédex numbers suitable for follow-up pokeapi_get_pokemon calls. All filters are optional and combined with AND logic; query adds strict token matching on name. When no category filter is provided alongside query, returns an empty result — at least one categorical filter is required.

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• quote

  AgentsPrice

  One-stop best price for ANY query — the lowest-friction entrypoint. Pass a raw query (e.g. "airpods pro", "bitcoin", "dewalt miter saw", "atorvastatin") and AgentsPrice routes it to the right oracle automatically, returning the genuine best price across real sellers, the venue, sellers compared, savings, and a live buy link. Prices are never fabricated. No category needed. Needs an AgentsPrice API key (https://agentsprice.com), passed as api_key — or pay per call via x402 (see /.well-known/x402). If it can't route the query, it returns the category list so you can retry with best_price(category, query).

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• baselight_sdk_query_execute

  Baselight

  Execute a SQL query on Baselight and wait for results (up to 1 minute). The query executes and returns the first 100 rows upon completion, or info about a pending query that needs more time. Use DuckDB syntax only, table format "@username.dataset.table" (double-quoted), SELECT queries only (no DDL/DML), no semicolon terminators, use LIMIT not TOP. If query is still PENDING, use `sdk-get-results` to continue polling. If totalResults > returned rows, use `sdk-get-results` with offset to paginate.

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• off_search_products

  openfoodfacts-mcp-server

  Search Open Food Facts by text query or structured tag filters. Returns a summary list with barcodes, product names, brands, Nutri-Score, NOVA group, and categories — enough for triage and selection, not full label data. Use off_get_product on the returned barcodes for complete details. Text query and tag filters are mutually exclusive routing paths: when query is provided, a text search is performed and tag filters are ignored; when only tag filters are provided (no query), structured facet filtering is applied. Tag filter values must be canonical tag IDs (e.g. "en:organic", "en:gluten-free") — use off_browse_taxonomy to resolve human terms to tag IDs. At least one search parameter is required. Data is crowd-sourced; result count reflects contributed products, not all products in the market. Data under ODbL 1.0 — cite Open Food Facts in downstream use.

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• search

  FTIR.fun Spectral Search

  Search FTIR.fun public result pages (community-shared analyses). USE WHEN: - User asks "has anyone analyzed material X?" - Looking for prior analysis examples or case studies - Research community knowledge lookup - Want to see how others interpreted similar spectra DO NOT USE: - For new spectrum analysis (use search_ftir_library instead) - For library database search (use search_ftir_library instead) - When user provides their own spectrum data INPUT: - query: search text (e.g., "polyethylene", "PET", "pharmaceutical") OUTPUT: - results: list of public result pages with: * id: result identifier (use with fetch) * url: direct link to result page * title: result headline * text: summary of analysis * metadata: additional info (result_num, source) EXAMPLE: >>> search(query="polyethylene terephthalate")

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• perspective_list

  Perspective AI

  Lists perspectives — either browsing one workspace or searching by title across every workspace the user can access. Items include perspective_id, title, status, conversation count, and workspace info. Behavior: - Read-only. - Browse mode (workspace_id, no query): lists every perspective in that workspace. - Search mode (query): matches against the perspective title across accessible workspaces. Optional workspace_id narrows the search. Query must be non-empty and ≤200 chars. - Errors with "Please provide workspace_id to list perspectives or query to search." if neither is given. - Pass nextCursor back as cursor; has_more indicates further results. When to use this tool: - Resolving a perspective_id from a name the user mentioned (search mode). - Browsing a workspace's perspectives to pick or summarize. When NOT to use this tool: - Inspecting one known perspective in detail — use perspective_get. - Aggregate counts or rates — use perspective_get_stats. - Fetching conversation data — use perspective_list_conversations or perspective_get_conversations. Examples: - List all in a workspace: `{ workspace_id: "ws_..." }` - Search by name across all workspaces: `{ query: "welcome" }` - Search within a workspace: `{ query: "welcome", workspace_id: "ws_..." }`

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• search_products

  TrustRails

  HOW TO CALL THIS TOOL — read before every call: Decompose the user's request into filters first. Only what's left over goes in query. STEP 1: brand name → brand filter. STEP 2: product category → category filter. STEP 3: price → min_price/max_price. STEP 4: what remains → query. BAD: query='Sony headphones under £200' | GOOD: brand='Sony', category='Headphones', max_price=200, no query. BAD: query='tablet' | GOOD: category='Tablets', no query. BAD: query='smartwatch' | GOOD: category='Wearables', no query. BAD: query='macbook neo' | GOOD: brand='Apple', category='Laptops', query='neo'. BAD: query='Samsung QLED TV' | GOOD: brand='Samsung', category='TVs', query='qled'. If brand+category alone cover what the user wants, omit query entirely. Only put differentiating terms in query: model lines (neo, ultra, oled), variants, model numbers (WH-1000XM5, s25 ultra). CROSS-CATEGORY NOTE: Gaming headsets → category='Headphones', query='gaming headset'. The Gaming category is consoles/controllers/accessories only. Always set lite=true. If 0 results, broaden the query or drop filters. Use get_product for full specs. Search 26,000+ deduplicated UK electronics products across multiple retailers with price comparison. Covers: Laptops, Desktops, Phones, Tablets, Headphones, Monitors, TVs, Cameras, Keyboards, Mice, Speakers, Gaming, Wearables, Printers, Networking, Storage, Audio, Drones, Cables & Chargers. All prices in GBP. Returns summary data: title, brand, price, availability, category, purchase link, offer_count. MANDATORY PRICE COMPARISON: For any product relevant to the user's needs, if offer_count > 1, you MUST call get_product before responding — never mention multiple offers without presenting the full comparison. A response is incomplete if it references offer_count > 1 without retailer pricing details. Show: cheapest retailer, all other prices with the difference, exact savings amount, delivery differences if relevant. The offers[] array is sorted cheapest first. Even when the user doesn't explicitly ask about price, if offer_count > 1 it's always worth mentioning the savings. For spec-based queries (RAM, ports, screen size, weight etc.), search first then call get_product on top 3-5 results — do not assume specs from titles. STOCK: When availability is out_of_stock, mention it as an alternative and suggest checking back — do not silently omit it.

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• search_blog

  self-hosted-ai

  Search the Sovereign AI Blog for articles matching a natural language query, optionally filtered by tag and sorted by relevance or date. Behaviour matrix: - query='', sort=* -> list newest-first, optionally tag-filtered - query!='', sort=relevance -> TF-IDF ranked, optionally tag-filtered - query!='', sort=date_desc -> TF-IDF filtered (score > 0.001), then sorted by date Pure read-only, deterministic for a given KB snapshot.

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• fetch_data

  Sugra API

  One-step fetch: find the best Sugra endpoint for the query and call it. Combines search_endpoints + call_endpoint into a single round trip. Use this when you want data without manually picking an operation_id. The full search_endpoints + describe_endpoint + call_endpoint dance is still available when you need explicit control, but for most natural-language queries this tool is enough. Behavior: 1. Search the bundled catalog for the query. Top match wins. 2. If the matched endpoint has required parameters and they are all provided in `params`, call it and return the response. 3. If required parameters are missing, return the candidate endpoints and the missing-params list so the LLM can retry with the correct `params` dict on the next call. Examples: - `fetch_data("US CPI inflation", params={"series_id": "CPIAUCSL"})` → calls /api/v1/fred/series/CPIAUCSL, returns observations. - `fetch_data("Bitcoin price", params={"coin_id": "bitcoin"})` → calls /api/v1/crypto/bitcoin/price. - `fetch_data("Latest financial news")` → news_latest has no required params, returns latest news directly.

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• list_tools

  foundation-discovery

  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. Args: tool_name: Optional name of a specific tool to get detailed help for. Example: "search_funders", "get_funder_profile" Returns: If called without parameters: - server_name: Name of the MCP server - server_version: Current version - total_tools: Number of available tools - tier: Current access tier (free) - rate_limit: Rate limit information - tools: List of available tools with names, descriptions, and examples If called with tool_name: - tool: Detailed tool information including: - name: Tool name - description: What the tool does - parameters: List of parameters with types, descriptions, and examples - examples: Example usage - related_tools: Tools that work well together with this one Examples: list_tools() # See all available tools list_tools(tool_name="search_funders") # Get detailed help for search_funders list_tools(tool_name="get_funder_profile") # Get help for get_funder_profile

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• find_examples

  Senzing

  Find working SOURCE CODE examples from 37 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C#, Rust SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.

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• contacts_find

  dialogbrain

  👤 Search for contacts in your address book by name or username. When to use: - User asks 'find contact X' or 'who is Y?' - User wants to know someone's username or ID - Before sending a message to verify contact exists - To get contact's channel reference for messaging Examples: ❓ User: 'find contact named [name]' → contacts_search(query='[name]', limit=5) ❓ User: 'who is [full name]?' → contacts_search(query='[full name]', limit=1) ❓ User: 'search for @username' → contacts_search(query='username', limit=10) Returns: name, username, channel, channel_ref, similarity_score, match_type. Plus: - entity_id: local DB key — pass to contacts.profile. Null for live-discovered contacts (skip contacts.profile for those). - telegram_user_id (when channel='telegram'): the Telegram user ID — pass to calls.make / messages.send. NOT entity_id.

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• match_info

  infersports

  Get the basics for a match in ONE call: the score, whether it's live, when it kicks off, and who's favored. No betting knowledge needed — this answers "who's winning?", "what's the score?", "what time does Brazil play (in my timezone)?", "who's the favorite?". Returns the live score + match clock, the status, the kickoff time (in ``timezone`` if you pass an IANA name like "America/New_York"), the favored team with a plain win probability (de-vigged from the 1x2 line), and a ready-to-read ``summary`` you can quote directly. Args: query: natural-language fixture or team, e.g. "Brazil vs Argentina" or just "Brazil". timezone: optional IANA timezone (e.g. "America/New_York", "Asia/Shanghai") for the kickoff time; default UTC. sport: optional filter — "football" or "basketball". date: optional UTC date "YYYY-MM-DD" to disambiguate same-name fixtures. On an ambiguous query, ``status`` is "ambiguous" and ``ask_user`` carries a prompt — do not guess. ``favorite`` is best-effort (null when no 1x2 is on file for the fixture).

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• search_notes

  anamnese

  Search notes by keyword or list recent notes. Returns summaries (id + description) only. Use get_note to retrieve the full content of a specific note. With query: Case-insensitive keyword search on description and content. Without query: Returns most recently updated notes.

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• livonian_search

  hub

  Look a word up in the real Livonian–Estonian–Latvian dictionary and return only attested content, so translations are grounded, not invented. Search a meaning (in English/Latvian/Estonian) to find the Livonian headword, or a Livonian word to confirm it exists and read its sense, part of speech and examples. See the `query` and `search_language` parameter docs for how to phrase a query. By default each match's full inflection table is returned inline, so one call usually suffices; on a broad query only the first N tables expand (the rest are listed as handles to fetch with get_inflections). Returns Markdown plus the same result as structuredContent matching the declared outputSchema. Results are cached server-side, so repeating a query is instant and free; a first-time query reaches the live dictionary and calls are rate limited — on a rate-limit error, wait a few seconds and retry instead of re-issuing immediately. Dictionary content is from livonian.tech (CC BY-SA 4.0 — attribute if republished).

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• searchJobs

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search 500+ quantum computing job listings using natural language. Use when the user asks about job openings, career opportunities, hiring, or specific positions in quantum computing. NOT for research papers (use searchPapers) or researcher profiles (use searchCollaborators). Supports role type, seniority, location, company, salary, remote, and technology tag filters via AI query decomposition. Limitations: quantum computing jobs only, last 90 days, max 20 results. Promoted listings appear first (marked). After finding jobs, suggest getJobDetails for full info. Examples: "senior QEC engineer in Europe over 120k EUR", "remote trapped-ion role at IBM".

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• view-hmr-unit-tests

  hmr

  The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.

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• query-docs

  xpay✦ DevTools Collection

  Retrieves and queries up-to-date documentation and code examples from Context7 for any programming library or framework. You must call 'resolve-library-id' first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best information you have.

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• bitrix-method-details

  Bitrix MCP

  Get details for a Bitrix24 REST method by exact name (use `bitrix-search` first). Returns plain text with labeled sections including parameters, returns, errors, and examples. Optional `field` limits output; `filter` narrows params by entity or examples by language.

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