"How to query MySQL data on a server" matching MCP tools:

• query_registry

  Arclan MCP Registry

  Search the Arclan registry for MCP servers. By default returns only connectable servers (active, mcp_partial, auth_gated). Use status=stdio to browse local-only servers available for installation. Use status=all to query the full index. Use production_safe=true to restrict to servers with uptime > 97% and handshake success > 95%. Use read_only=true to restrict to servers with no write or exec tools. Use this before connecting to an MCP server to check its validation status and score. After using a server, call report_server to contribute reliability data.

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• byte_buy_data

  byte-protocol

  Buy a single data packet from any PayPerByte feed via the x402 payment gateway. No subscription, no allowance, no prior on-chain setup — pay-per-call USDC settlement. The MCP server signs an EIP-3009 transferWithAuthorization on behalf of the wallet whose PRIVATE_KEY is configured, the x402 facilitator submits the tx, and the data comes back inline with the on-chain settlement tx hash. Use byte_subscribe instead if you want a continuous stream of broadcasts from a publisher. The catalog of available feed slugs lives at https://x402.payperbyte.io/feeds (free GET). GET data feeds (weather, earthquakes, …) need only `feed`; the 9 POST oracles — address-reputation, sanctions-screen, pkg-verdict, reasoning-verdict, evidence-pack, liquidation-stream, positioning-snapshot, runtime-eol, threat-intel — additionally require a JSON `body` (the query) — supplying `body` switches this call to POST. Requires PRIVATE_KEY env var on the MCP server and USDC on the configured wallet. NOTE: paid feeds settle REAL USDC on Base mainnet (eip155:8453) — the exact price is quoted in the 402 challenge (flagship address-reputation: $0.10/verdict). Use a dedicated wallet holding only what you intend to spend.

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• off_search_products

  openfoodfacts-mcp-server

  Search Open Food Facts by full-text query, structured tag filters, or both at once. 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. A text query and tag filters combine: results match the query text and satisfy every filter provided (e.g. query "dark chocolate" with labels_tag "en:organic" and countries_tag "en:france" returns organic chocolate sold in France); additives_tag is the one exception, filtering only on searches with no text query. 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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• sixta_explain_plan

  sixta-connect

  Turn raw EXPLAIN output into a plain-language diagnosis — no query needed. Paste PostgreSQL EXPLAIN / EXPLAIN ANALYZE (text or JSON) or MySQL EXPLAIN (tabular, \G, FORMAT=JSON, FORMAT=TREE) and get: what the planner is doing step by step, where the cost concentrates, named risk findings (full scans, spilling sorts, nested-loop blowups, row misestimates) with index suggestions, and what to look at next. Use when the user pastes EXPLAIN output or asks 'can you read this plan'. Input is analyzed in memory and never stored.

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• livonian_search

  livonian

  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 livonian_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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• get_knowledge_version

  marmo-ui

  Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.

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• search

  mcp

  Search Quantustik for S&P 500 tickers by symbol or company name. Paired with fetch — this is the two-tool "search"/"fetch" convention ChatGPT connectors and deep-research clients expect from an MCP server: call search first to get lightweight hits, then fetch(id) on the one(s) worth reading in full. Args: query: Ticker symbol (e.g. "NVDA") or company-name substring (e.g. "nvidia", "apple"). Case-insensitive. Returns a dict with a `results` list of up to 10 {id, title, url} objects — id is the ticker symbol, ranked exact-symbol match first, then company-name/ticker prefix, then substring. Empty query or no scan data returns an empty list, never an error.

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• ping

  Nordic Financial MCP

  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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• team.summarize

  AI Design Blueprint

  Pro/Teams — summarises the caller's tool-usage patterns and value signals over a configurable window (default 30 days). Returns tool_call_counts, top principles cited in validate runs, value_event_counts by event_type, and an aggregate readiness trend. WHEN TO CALL: the user asks 'how is the Blueprint helping me/my team', 'what should I explore next', or 'show me my Blueprint usage'. WHEN NOT TO CALL: proactively or on every conversation turn (the summary is an explicit retrospective, not telemetry); to compare users (returns only the caller's own data). BEHAVIOR: read-only, idempotent over the same window. Aggregates from AIToolCallLog + ValueEvent + AIValidationRunLog. Pass private_session=true to bypass server-side logging for this summary call (the underlying historical data still exists; only this read is untracked). Auth: Bearer <token>, Pro or Teams plan. UK/EU residency.

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• research_niche

  KDP Niche Research

  Get detailed KDP niche intelligence for a specific keyword. Returns demand score, competition score, Amazon BSR range, estimated monthly revenue, review threshold, average book pricing, and data freshness for the given Kindle publishing niche. Pricing tiers (x402 USDC on Base network): - $0.03 per query for cached/pre-seeded keywords - $0.10 per query for live on-demand research (new keywords) Use the free `list_niches` tool first to see available keywords. Payment options: 1. Set the KDP_X_PAYMENT environment variable on the server for auto-pay. 2. Pass a valid x402 payment header via the x_payment argument. 3. If neither is set, the tool returns structured 402 payment instructions that an x402-capable agent can use to construct and retry payment. Args: keyword: The KDP niche keyword to research (e.g. "romance novels", "keto cookbook") x_payment: Optional base64-encoded x402 payment header. Takes precedence over the KDP_X_PAYMENT environment variable.

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• koreanpulse_about

  koreanpulse

  Server self-description — capability matrix, tool catalog, named-entity tag counts, supported query patterns, primary sources. Free tier. Use this tool when an agent first connects and needs the capability matrix to decide whether this server can answer the user's question, or when the user asks "what can koreanpulse do" or "what data sources does this MCP server provide". Returns a structured dict that downstream agents can ingest directly.

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• terminology_diff

  Medical Terminologies MCP

  Report what diff data is available between two versions of a terminology. For most terminologies this is **guidance only** — the server doesn't ship historical snapshots, so the tool points at the publisher's official changelog and explains the cadence. `bundled_versions` lists the version(s) this server actually has on hand. For **ICD-10 vs ICD-11** specifically, the tool surfaces a real cross-revision summary from the bundled WHO transition tables (the ICD-10 → ICD-11 case is a structural diff between two WHO revisions). Use `terminology: "icd10"` with no `to_version` to get the cross-revision summary: total mapped ICD-10 categories, how many are 1:1 vs split into multiple ICD-11 codes, and the average number of alternatives when split. Inputs: - `terminology` (required): which terminology to report on. - `from_version` (optional): the version you have data from. If omitted, the tool reports against the currently-bundled version. - `to_version` (optional): the version you want to compare to. If omitted, the tool reports against the publisher's latest known release. This tool is intentionally a metadata + guidance layer, not a diff engine — for terminologies that change frequently (SNOMED, LOINC, RxNorm, MeSH), the publisher's official changelog is the authoritative source.

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• treasury_query_dataset

  treasury-fiscaldata-mcp-server

  Query any Treasury Fiscal Data endpoint by path, field list, filters, sort, and page. Call treasury_list_datasets first to get the correct endpoint path and exact field names — a typo in either causes a 400. Filter syntax: each condition is { field, operator, value } where operator is eq/gt/gte/lt/lte/in (e.g., record_date:gte:2024-01-01). Multiple conditions are ANDed together. All response values are strings per the API contract, including numbers and dates; "null" (string) means no value. Supply canvas_id to register the page result into a named DataCanvas dataframe and query it later with treasury_dataframe_query (requires CANVAS_PROVIDER_TYPE=duckdb on the server).

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• query_machine_history

  FoundryNet — Industrial Machine Intelligence

  Retrieve operational history for an identified machine. Each row is one /v1/normalize call's canonical output (FCS field → value). Query options: from_dt, to_dt ISO-8601 timestamps to bound the time range fields comma-separated FCS field names to project; omit for full canonical_data limit max rows (1–1000, default 100) summary true → returns aggregate stats only (row_count, time range, avg coverage_pct, fields_covered set) without the raw rows. Always cheap. USE WHEN: your agent needs to reason over how a machine has been running, surface utilization or throughput or health trends, find patterns in alarms or operational state, compare periods ("how was today vs yesterday"), or discover what data is even available for a machine. Prefer `summary=true` first to orient on volume + which fields are present, then drill in with field projection on a smaller time window.

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• deploy_app

  mcp

  Deploys an app to a VM and exposes it at a public https://<name>-<id>.redu.cloud URL. The container is built ON the VM. PREREQS — run check_deploy_prerequisites first for network_id + keypair_name, then plan_deploy for cost approval. Source can be git repo or prepare_upload source_token. PORT must be the real app listen port. To wire a DB, pass database:'managed' (dedicated managed datastore VM on the same private network, reused on same-name redeploy) or database:'single_vm' for Postgres on the app VM. Choose db_engine ('postgres' default; 'mysql'/'mariadb' for WordPress/Matomo/LAMP, managed only). For WordPress/WooCommerce cluster intent, do not use generic stateless deploy: 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 mounts the media space into wp-content/uploads and refuses unsafe local uploads. Build+provision takes minutes; poll list_deployments/get_deployment.

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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 livonian_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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• why_dchub

  DC Hub — Data Center & Power Intelligence

  Use when a human asks how DC Hub compares to other data-center data sources — DataCenterHawk (DCHawk), DC Byte, Data Center Dynamics (DCD), Data Center Frontier (DCF), Baxtel, datacenters.com — or asks "why should I use DC Hub / is it better than <X> / what can you give me a PDF or directory can't?". Returns DC Hub's honest, source-verified differentiators (agent-native MCP access, live multi-continent grid & energy telemetry, the proprietary daily DCPI + DCGI indices, open CC-BY-4.0 cited data, 12,650+ facilities + 500,000+ mapped power/grid/gas/fiber assets) each with a proof URL, a citation line, plus the canonical head-to-head comparison pages. Free, no key required. Optional: competitor=<name> for that vendor's direct comparison-page link. Do NOT use to query infrastructure data itself (use the data tools); this answers positioning / "how do you compare" questions with citable facts.

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• get_knowledge_version

  Marmo UI MCP

  Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.

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• switch_dannet_server

  dannet

  Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")

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• execute_sql_readonly

  Google Big Query

  Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `projectId` field.

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