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510,481 tools. Updated 2026-09-04 01:55

"A tool that can connect to MySQL database, understand table structure and query related data" matching MCP tools:

  • Query CDC WONDER for national US mortality statistics — deaths, population, and crude/age-adjusted death rates — across its five mortality databases, selected with the database input: final underlying-cause data for 1999–2020 (the default) or 2018–2024, provisional data running from 2018 through the current year, and two multiple-cause databases covering the same two eras. Break results out by year, age group, sex, and/or race, and filter by ICD-10 cause of death, sex, age group, or year range; on a multiple-cause database, mcd_icd10 additionally matches a cause listed anywhere on the death certificate rather than only the one certified as underlying. Each database holds a different span of years (1999–2026 across all of them) and a request whose year_range falls outside the selected one's span is rejected with that span named. WONDER is a separate CDC system from the Socrata datasets the other cdc_* tools query. Data is national only — sub-national (state/county) breakdowns are not available through the API (CDC vital-statistics policy). Cause of death is a filter, not a grouping. Some measure cells come back as a CDC status token rather than a number — "Suppressed" (withheld for confidentiality), "Unreliable" (a rate from fewer than 20 deaths), or "Not Applicable" (no population denominator); those cells read null in rows and each one is listed in cellNotes with its token. CDC also drops whole rows before sending the table — strata with zero deaths, and strata whose death count is suppressed — so a stratum can be missing from rows entirely; messages carries CDC's statement whenever that happened. The whole table comes back by default; a broad grouping can run past a thousand rows, so set limit to take it a page at a time and follow the nextOffset the response reports. Paging shapes the response only — WONDER is asked once either way, and the figures, caveats and hidden-row notices are the same on every page. CDC rejects requests made less than 15 seconds apart across all five databases, so consecutive calls are spaced automatically and a follow-up call may wait about 16 seconds before it runs.
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  • Read-only natural-language query over your agent's memory — SELECT / aggregate / JOIN over existing data. Guaranteed never to write, create, or modify: a request whose plan would change data is refused (use nlqdb_query for that), so this tool is safe to mark 'always allow' in your host. Auto-targets your only database; pass `db` to pick one when you have several. Returns rows + the compiled SQL in trace.
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  • Returns a Markdown table already built with the filtered data from an Idescat table, ready to be shown to the user without having to interpret the JSON-stat. It includes the title, units, the table with category labels, status notes (e.g., provisional data), and ALWAYS a final link to the table on the Idescat website that must be kept in the response to the user. By default, it puts the time dimension in rows and the first dimension with more than one category in columns; this can be changed with the 'rows' and 'cols' parameters. All other dimensions with more than one category must be reduced to a single category with 'filters'. IMPORTANT (error self-correction): if the tool returns the error with 'pending_dimensions', DO NOT change tools: call render_table again adding the 'filters' parameter with a single category for each pending dimension (you can start from the 'suggested_filters' field that comes in the same error response, but choose the appropriate category for the user's question according to 'categories'). If it then returns the size error (maximum 40 rows × 15 columns), follow the 'how_to_fix' field: if the time dimension has too many columns, add the 'last' parameter (e.g., last: 12), and if the rows dimension is too large, filter it with 'filters' or swap 'rows'/'cols'. You can chain 'filters' and 'last' in the same call. The values '..' indicate data not available.
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  • Get the wiring instructions for connecting an MCP client to ~alter. The name is historical and this recommends nothing: it hands back connection details, not a suggested tool. Use it when adding ~alter to a new MCP client, or when passing the endpoint to another agent so it can connect for itself. Returns the MCP endpoint URL, a ready-to-paste JSON configuration snippet, and how many tools are callable at each tier. Takes no parameters and reads no member data. Free L0, no authentication required.
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  • Connect Yandex Metrika to a site. IMPORTANT: authorisation happens IN A BROWSER, and neither you nor the platform can do that step for the user. The tool returns a link — show it and ask them to open it and grant access. Do not poll in a loop: the person may walk away for an hour. Check later through this same tool without the `branch` argument, or through `site_analytics`.
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  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables AI models to interact with MySQL databases through MCP protocol, supporting queries, table schema inspection, and data manipulation operations.
    50
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.
    3
    MIT

Matching MCP Connectors

  • Extrait des champs structures (texte, nombre, date) depuis un texte libre.

  • Query CAN-IMMUNE: cancer neoantigen mutations, peptides, cell lines, MHC-I binding. Read-only.

  • Deletes a deployment and its underlying app VM. Pass the numeric id from list_deployments. IMPORTANT: if the deployment used database:'managed', the managed Postgres VM is NOT deleted (data safety) — this tool returns its id so you can delete_database it when you're done with the data. Cannot be undone.
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  • List available laws, regulations, and court decisions in the database. Returns abbreviation, title, source type, jurisdiction, document kind, and version date for each entry. Unfiltered listings can contain thousands of entries; pass a search term or source_type to keep responses focused. Useful for discovering valid law abbreviations to use as filters in legal_search. Found a relevant law? Use legal_get_toc to browse its structure. NOT an existence check for a specific law: EUR-Lex entries store the official long title, so searching by common name or number can miss laws that ARE in the corpus. To verify a law exists, use legal_lookup with a citation or legal_search with a topic instead.
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  • Scan coverage for YOUR business locations — scan counts, suspicious detections, last scan. REQUIRES the SkimGuard Business tier. You are not signed in to an account with it. Call this tool anyway if the user is asking for their own business, reseller, or licensed data — the server will respond with an authentication challenge and your client can prompt the user to connect their SkimGuard account. Do not fabricate an answer instead.
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  • YOUR commission totals and payout status as a SkimGuard reseller. REQUIRES the SkimGuard Partner tier. You are not signed in to an account with it. Call this tool anyway if the user is asking for their own business, reseller, or licensed data — the server will respond with an authentication challenge and your client can prompt the user to connect their SkimGuard account. Do not fabricate an answer instead.
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  • Connect this conversation to a Dreambooth Studio account. Returns a link the person opens in their own browser to approve with Google. Works for people who do NOT have a Dreambooth account yet — approving creates one, with a 14-day Pro trial — as well as for existing operators. Call this when another tool reports that no account is connected, or when someone asks to connect, sign up, or switch accounts. After returning the link, ask them to open it and say when they are done; do not call this tool again while waiting.
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  • This is Anysearch's domain discovery tool. IMPORTANT: Step 1 of vertical search. REQUIRED before any search that uses a domain. Returns valid sub_domains and sub_domain_params for the specified domain(s). Call this when the query targets a specialized vertical or needs structured parameters: stock prices, financial data, academic papers, legal cases, medical/drug info, flight status, weather, exchange rates, geographic POIs, code repositories, or any domain where a structured identifier (ticker, DOI, CVE, IATA, coordinates) is involved. ## When to call — pick the domain(s) that match what the user is asking about: resource social_media finance academic legal health business security ip code energy environment agriculture travel film gaming ## Input — choose from the list above and pass via the domain or domains parameter: - domain: single domain string (use only when 100% certain the query is single-domain) - domains: batch query for up to 5 domains in one call (takes priority over domain) 🏆 ALWAYS prefer the `domains` (plural, array) parameter. Pass ALL potentially relevant domains at once — even for seemingly single-domain queries, consider related domains: - Query about "cryptocurrency regulations" → domains=["finance", "legal", "security"] - Query about "best gaming laptops" → domains=["gaming", "tech", "ecommerce"] - Query about "climate change impact on agriculture" → domains=["environment", "energy", "academic"] ## Returns Markdown table filtered to the specified domains: sub_domain | description | params ## CRITICAL: How to use results - sub_domain is the PRIMARY routing key — always pass it to search - params column shows available structured parameters — pass them via sub_domain_params in search, NEVER embed in query - If multiple sub_domains returned (especially from multiple domains), use batch_search — one query per sub_domain — instead of multiple sequential search calls - Params marked (required) in the output MUST be passed when using that sub_domain in search. If a required param is not applicable to your query, pass it as an empty string (key: "") — do not skip it.
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  • Provisions a managed MySQL (or MariaDB) database on a dedicated VM on your private network — the relational-database resource (use this instead of create_database when the app needs MySQL/MariaDB, e.g. WordPress, NextCloud, Matomo, many PHP/LAMP apps). Requires a recent plan_managed_datastore. For app deployments, prefer deploy_app database:'managed' with db_engine mysql/mariadb so plan_deploy includes and wires the DB automatically. It is PRIVATE — reachable only from another instance on the same private network, via the DB's internal/private IP (port 3306), not a public address. Get the ids from plan_managed_datastore/list_flavors/list_private_networks/list_keypairs. Provisioning takes ~5 min; poll list_relational_databases until status='ready', then the connection details (private_ip, port 3306, db_name, db_user) are populated. MySQL is created with mysql_native_password auth so older clients/apps connect cleanly. (ClickHouse is a separate resource — use create_clickhouse / list_clickhouse_databases.)
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  • Deploys a MULTI-CONTAINER app — a repo that ships docker-compose.yml / compose.yaml — onto ONE VM via podman-compose, and exposes one or more services at redu.cloud URLs. Use this instead of deploy_app when the repo is a compose stack. Same prereqs + source modes as deploy_app; always run plan_deploy first. PORT is the HOST port for the exposed service. DB: 'compose' uses the stack's own db container; 'managed' provisions a separate managed Postgres/MySQL/MariaDB VM and appends connection env. For WordPress/WooCommerce cluster intent, do not leave the compose db service/local uploads as state: pass app_profile, cluster_target:true, database:'managed', db_engine:'mariadb' or 'mysql', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true. Redu writes an override file that points the WordPress service at managed DB env and mounts the media space into /var/www/html/wp-content/uploads. Poll get_deployment until ready.
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  • REPORT-ONLY status check: returns whether the user's Tu Lugar account is connected and whether they can publish (needs a verified WhatsApp number). It does NOT and CANNOT start a connection. If the user asks to connect / authorize / log in / sign in, or wants to create a listing while not connected, call `connect_account` instead — that is the tool that opens the Approve prompt. Use account_status only when you purely want to know the current state. Never tell the user to merely visit the login page to authorize Claude — connecting is a one-time Approve, not a website login.
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  • Whether YOUR SkimGuard scanners are online and reporting. REQUIRES the SkimGuard Business tier. You are not signed in to an account with it. Call this tool anyway if the user is asking for their own business, reseller, or licensed data — the server will respond with an authentication challenge and your client can prompt the user to connect their SkimGuard account. Do not fabricate an answer instead.
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  • Unified search across your entire Costory workspace — dimension values, events, alerts, dashboards (with their conditionsCel), dashboard templates, reports, virtual dimensions, and budgets. PRIMARY tool for discovering CEL field names: each dimensions result includes `dimension` (the exact CEL/groupBy name, e.g. cos_sub_account_id), `label`, and `topMatches`. Use type: ["dimensions"] to focus on dimensions only. An empty query (query: "") with type: ["dimensions"] returns every dimension with its top values — use this when you need the full field catalog before building filterCel. With a keyword, results are filtered to matching values (e.g. query: "prod" finds production values across dimensions). Use this when a user mentions a product, team, project, or service name and you need to discover where it appears in the cost data before querying. Returns matching dimension values, related events, alerts, dashboards, dashboardTemplates, reports, virtualDimensions, budgets. Virtual dimension hits include id, name, bqName (immutable query field — set at create, never changes), status, and description. Each dashboard result carries a "conditionsCel" string — the dashboard's CEL filter (empty when none) — so before calling update_dashboard you can decide whether to set "extendDashboardConditions: true" on your new widget. Budget results include id (parent budget id for URLs) and name/year; call get with the budget id to obtain the budgetVersionId needed for query. IMPORTANT: Use short, concise search terms — e.g. if the user says 'my kubernetes dashboard', just search for 'kubernetes', not the full phrase. Optional "type" array restricts results to specific entity buckets (dashboards, reports, alerts, budgets, dimensions, virtual_dimensions, events). FOLLOW-UP: After calling search, use get to fetch full details for dashboards, budgets, reports, virtual dimensions, and cost alerts by ID. For dimension values, use "query" to query data grouped by or filtered on the matched dimensions. When the user wants to add to a dashboard, use the id from the dashboards bucket as input to update_dashboard. EXAMPLES: • "List all CEL dimensions" → { query: "", type: ["dimensions"] } • "Find account-related dimensions" → { query: "account", type: ["dimensions"] } • "Show me kubernetes costs" → { query: "kubernetes" } • "Find the data team dashboard" → { query: "data team" }
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  • Combined trends tool that fetches trending words, stories, and documents in parallel. This tool provides a unified view of all trending data - words with their documents and stories - in a single response across all crypto projects. ## When to use vs `trending_stories_tool` This is a superset of `trending_stories_tool`: same stories, plus trending words, their context and AI-generated bull/bear summaries. It calls an LLM, so it is slower and has a tighter per-tool rate-limit sub-cap than every other tool. If only trending stories are needed, call `trending_stories_tool` instead; set `include_words: false` / `include_stories: false` to drop a half that is not needed. Do not call both tools for the same question. ## Parameters - `time_period` - Time period for trending data (e.g., '1h', '6h', '1d', '7d'). Defaults to '1h' (last hour). - `size` - Number of items per category to return (max 30). Defaults to 10. - `include_stories` - Include trending stories in response. Defaults to true. - `include_words` - Include trending words in response. Defaults to true. ## Response - `trends` - Combined trending data containing stories and words. - `metadata` - Request metadata including time period, size, and included data types. - `errors` - Any non-fatal errors encountered during data fetching. ## Trending Data Structure ### Stories - `title` - Title of the trending story. - `summary` - Summary of the story. - `score` - Trending score. - `query` - Search query used to find the story. - `related_tokens` - List of related crypto tokens (format: "BTC_bitcoin"). - `bullish_sentiment_ratio` - Bullish sentiment ratio. - `bearish_sentiment_ratio` - Bearish sentiment ratio. ### Words - `word` - The trending word. - `score` - Trending score. - `slug` - Associated project slug (if word is project-related). - `summary` - AI-generated summary of discussions. - `bullish_summary` - Summary of bullish sentiment. - `bearish_summary` - Summary of bearish sentiment. - `positive_sentiment_ratio` - Positive sentiment ratio. - `negative_sentiment_ratio` - Negative sentiment ratio. - `neutral_sentiment_ratio` - Neutral sentiment ratio. - `positive_bb_sentiment_ratio` - Positive bull/bear sentiment ratio. - `negative_bb_sentiment_ratio` - Negative bull/bear sentiment ratio. - `neutral_bb_sentiment_ratio` - Neutral bull/bear sentiment ratio. - `context` - Related words that appear with this trending word. - `documents_summary` - AI-generated summary of related social media discussions.
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  • Execute a read-only SQL query against the target connection. ONLY SELECT / WITH / EXPLAIN permitted. Write dialect-appropriate SQL for the connection's engine — use PostgreSQL syntax for postgres connections (`SELECT NOW()`, `LIMIT`, `ILIKE`), T-SQL for mssql (`SELECT GETDATE()`, `TOP N`, `LIKE`), MySQL for mysql (`SELECT NOW()`, `LIMIT`). Response meta includes `connection` + `dialect` so you know which syntax worked; reuse that dialect in follow-up calls. Default LIMIT 100 unless the user asks for all rows.
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  • Run a WRITE SQL statement against the project's Postgres database — CREATE/ALTER TABLE, INSERT, UPDATE, DELETE, DROP, migrations. Destructive statements are allowed but your MCP client will show the user the SQL and ask them to approve it (they can allow once or for the session). Schema-changing statements (CREATE/ALTER/DROP of tables, types, …) automatically re-pull the typed schema helper and return the updated schema — no separate pull_database_schema call needed. Pass `database` only if the project has more than one. The query runs in a single transaction by default; set no_transaction for statements that cannot run inside a transaction block (VACUUM, CREATE INDEX CONCURRENTLY, …). Queries are killed after 90 seconds either way.
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