"How to write and execute queries in InfluxDB" matching MCP tools:

• openstreetmap_query_rawA

  openstreetmap-mcp-serverA

  Run an arbitrary Overpass QL query for anything the convenience tools cannot express: multi-type or union queries, relation membership, historical queries, regex tag matching. The query must include [out:json], e.g. "[out:json][timeout:15];node[\"natural\"=\"peak\"](47.5,-122.5,47.7,-122.2);out body;"; scope to an OSM boundary with rel(<id>);map_to_area->.a; or way(<id>);map_to_area->.a; then (area.a) on each statement (the 2400000000 way-area offset is gone since Overpass 0.7.57; openstreetmap_query_bbox takes the same scope as within, without QL). The response is one page: page with limit and offset, read totalFound and truncated for the whole match, and an element over max_element_bytes arrives with its members, nodes or geometry array withheld whole and withheldNotice saying how to fetch it back. For plain "near X" or "in this area" questions use openstreetmap_query_nearby or openstreetmap_query_bbox.

  ConnectorNo auth

• google_ads_queryA

  Google Ads MCPA

  Execute a custom GAQL query against a Google Ads account, paginated. Power-user tool for queries the convenience tools don't cover. Prefer google_ads_account_performance, google_ads_campaign_performance, google_ads_daily_trends, google_ads_conversion_performance, or google_ads_search_terms when they fit — they return cleaner shaped data. Use this for ad-group/keyword/asset/criterion-level queries, custom segmentation, or anything not covered by those. Prerequisites: if you don't know the GAQL syntax, call google_ads_gaql_doc first. For field names of a specific view, call google_ads_reporting_view_doc. Sensitive billing/access resources are not queryable here. Returns up to `page_size` rows (default 200, max 1000) plus `next_page_token` for the next page. The user usually wants the full picture for analysis — keep calling with the returned token until next_page_token is null. MCC login_customer_id is auto-detected if omitted. When paginating, pass the SAME query and customer_id back with page_token from the prior response. Args: query: The GAQL query to execute. customer_id: The Google Ads customer ID (digits only). login_customer_id: Optional MCC account ID. Auto-detected if omitted. page_size: Rows per page (1-1000, default 200). page_token: Opaque cursor from a prior response. Omit on first call.

  ConnectorOAuth

• shippo_describe_toolA

  ShippoA

  Get the full input schema, detailed description, and kind classification for a Shippo operation. Call this AFTER shippo_list_tools to learn the exact parameters (required + optional, types, enum values) AND to confirm which execute tool to use (read or write). Pass the short operation name from shippo_list_tools (e.g. 'listWebhooks', 'CreateAddress', 'GetTrack'). Name lookup is case-insensitive.

  ConnectorOAuth

• list_endpointsA

  nordic-dataA

  Discovery meta-tool. Lists ALL available Nordic Data API data endpoints (HTTP method, path, short description) by reading the backend's live OpenAPI spec at runtime — far beyond the curated high-level tools. Use this to discover capabilities the dedicated tools do not cover, then call get_endpoint_schema for parameter details and call_endpoint to execute one. Only read-only operations are listed (GET/HEAD plus the POST screening queries); admin and state-changing endpoints are never returned. Supports an optional `search` keyword filter. The catalog has 230+ endpoints.

  ConnectorNo auth

• create_guidesA

  serpmanticsB

  Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.

  ConnectorNo auth

• get_trading_workflowA

  Crank ProtocolB

  Map of how Crank's tools fit into one end-to-end trading flow (read this first). Read-only, free. Returns an ordered, machine-readable workflow: orient -> intelligence -> yields -> simulate (backtest) -> risk-size -> execute (non-custodial) -> monitor -> journal. Each step names the concrete tool(s) to call, their purpose, key inputs, how to use the output downstream, and the decision points that branch the flow -- so an agent that discovered Crank via tools/list can sequence the full tool surface instead of guessing. Descriptive only (DYOR); only execute-phase tools are value-bearing. wallet_address (optional, PUBLIC key only -- non-custodial): when given, appends ``human_activity`` -- count + most-recent manual override on this shared account in the last 72h (decision_type, asset, rationale summary, timestamp) plus an instruction to reconcile with it before acting. Human and agents act on ONE account: every human action is journaled (see journal_query source="human") so it is never invisible to you. Absent/clean (``{"count": 0}``) when there is no override.

  ConnectorNo auth

• kleo_accountA

  KleoA

  The credits left on this account, whether it can order a FILM (has_paid: films are for accounts that have bought a pack; every account can order the animatic), the link to its page, and the "Kleo key" that carries the same account (and the same credits) to another browser or another computer. Call it before the first kleo_create_video of a conversation and when the user asks how many credits they have, how to get more, or how to use Kleo somewhere else. Give them account_url as a plain link: it opens a read-only page (balance, prices, where to write) and cannot sign anybody in. Show account_key only if they ask for it, because anyone who has it can take the account over and spend its credits.

  ConnectorOAuth

• whoamiA

  SteledgerA

  Report the current session's identity. Read-only, no sign-in required: an anonymous session gets `{authenticated: false}` with a hint (not an error), a signed-in one gets `{authenticated: true}` plus the GitHub-rooted id, login and tariff. Call it to confirm who you are before `register_identity` / `store_memory`; an anonymous caller must sign in (GitHub OAuth) first. `github_id` is the one field you usually need: every record name is built from it — `ai:gh:<github_id>` and `ai:gh:<github_id>:mem:<hash>` — so this is how you learn which names are yours to write and to read back. `tariff` is `free` for every account today; it governs the write limits, currently 10 writes per minute and 100 per trailing 24 hours per account, and writing needs a GitHub account at least 30 days old. `quota` says how many writes are left right now (and, for a young account, the date writes open); every write returns the same figures, so plan batches with them. Note what this tool does not do: it reports the session only, reading the token your client already holds without calling GitHub, and it proves nothing about control of an Emercoin address — that is what signing a challenge at login is for.

  ConnectorNo auth

• whoamiA

  Emer Ai ToolsA

  Report the current session's identity. Read-only, no sign-in required: an anonymous session gets `{authenticated: false}` with a hint (not an error), a signed-in one gets `{authenticated: true}` plus the GitHub-rooted id, login and tariff. Call it to confirm who you are before `register_identity` / `store_memory`; an anonymous caller must sign in (GitHub OAuth) first. `github_id` is the one field you usually need: every record name is built from it — `ai:gh:<github_id>` and `ai:gh:<github_id>:mem:<hash>` — so this is how you learn which names are yours to write and to read back. `tariff` is `free` for every account today; it governs the write limits, currently 10 writes per minute and 100 per trailing 24 hours per account, and writing needs a GitHub account at least 30 days old. `quota` says how many writes are left right now (and, for a young account, the date writes open); every write returns the same figures, so plan batches with them. Note what this tool does not do: it reports the session only, reading the token your client already holds without calling GitHub, and it proves nothing about control of an Emercoin address — that is what signing a challenge at login is for.

  ConnectorNo auth

• sixta_analyze_workloadA

  sixta-connectA

  Find your worst queries by TOTAL time — no connection needed. Paste a MySQL slow query log or a PostgreSQL pg_stat_statements export and get a ranked top-N: each query shape with calls, total/mean time, and (slow log) the rows-examined-to-sent ratio, fingerprinted so thousands of log lines collapse into a few classes. Flags the dominant query, N+1 patterns, and full-scan ratios, reports how concentrated the load is (what share of total time the top shapes own), and hands the worst offenders to sixta_analyze_query. Call this whenever the user shares a slow query log or pg_stat_statements export — even a long one — or asks which queries are slowest: summing time across thousands of log lines is arithmetic a model cannot do reliably by eye. Input is analyzed in memory and never stored.

  ConnectorOAuth

• execute_sqlA

  Google Big QueryA

  Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (`SELECT`, `INSERT`, `UPDATE`, `DELETE`, `CREATE`, etc.) * AI/ML functions like `AI.FORECAST`, `ML.EVALUATE`, `ML.PREDICT` * Any other query that bigquery supports. Example Queries: ```sql -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; ``` Queries executed using the `execute_sql` tool will always have the default job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the initial result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.

  Connector

  Destructive

  No auth

• get_integration_guideA

  TestMyVibesA

  Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.

  ConnectorNo auth

• generate_queryA

  ALM X++ MCP ServerA

  WHEN: developer needs correct X++ select or T-SQL for D365 tables with proper joins. Triggers: 'X++ select', 'generate a query', 'SQL for', 'join with', 'how to query', 'générer une requête', 'write a select statement', 'select from', 'X++ query for', 'requête X++', 'écrire une select'. Generate both X++ select statements and equivalent T-SQL queries for D365 F&O tables. Uses real field names, relations, and indexes from the knowledge base to produce correct joins. Supports: field selection, multi-table joins (auto-detects relations), WHERE filters, ORDER BY, TOP/firstonly, cross-company. Also accepts natural language descriptions like 'find all open sales orders for customer 1001 with CustTable join'. [!] For multi-table joins, call find_related_objects (or get_relation_graph if the relation index is loaded) FIRST to get the correct FK relations -- this tool will then produce accurate join conditions. [!] The generated X++ is a template -- adapt it to your custom code context before using in production. Returns side-by-side X++ and SQL with explanations.

  ConnectorNo auth

• get_quickstartA

  Vector Panda DocumentationA

  The Vector Panda quickstart: install the veep Python SDK, upload a file, and run similarity queries end to end.

  ConnectorNo auth

• google_ads_execute_add_negativesA

  Google Ads MCPA

  Execute a previously previewed negative keyword addition (Google Ads write tool). You MUST call google_ads_preview_add_negatives first. Args: customer_id: Google Ads customer ID (must match the preview).

  Connector

  Destructive

  OAuth

• modem_skillsA

  ModemA

  Skills that teach you how to use THIS Modem MCP server's tools. Call before your first `search_modem`, `modem_agent_invoke`, or write-tool call, or when unsure which Modem tool to use. Pass `name` for the full skill, one of `search-modem`, `agent-runs`, `write-tools`. Omit `name` to list them with descriptions. This is not the Skills page in dashboard settings.

  ConnectorOAuth

• pathrule_setupA

  PathruleA

  Fetch the active Pathrule bootstrap brief and execute it. Call this ONCE when the user asks to set up / bootstrap / initialize Pathrule for a project (e.g. 'Set up Pathrule for this project', 'Bootstrap Pathrule'). The response `body` is a prompt you must follow immediately — it tells you how to scan the project, propose memories/rules/skills, and write the approved items via pathrule_write_memory / _rule / _skill. Do NOT call this mid-task, for already-populated workspaces, or when the user just wants context — use pathrule_get_context for routine context lookups. If no workspace exists yet, call pathrule_list_organizations + pathrule_create_workspace first.

  ConnectorNo auth

• batch_searchA

  AnySearchA

  This is Anysearch's parallel search tool. Parallel search — run multiple Anysearch queries in a single call. Prefer this over multiple sequential calls when you have 2–5 queries. Saves context space and returns all results at once. Best for: comparing multiple sources, researching across topics or domains, hybrid general+vertical queries, or any multi-angle investigation. ## When to use Use batch_search instead of multiple sequential search calls when you have 2–5 independent queries. 🏆 PRIMARY use case: After get_sub_domains(domains=[...]) returns sub_domains across multiple domains, use batch_search to send one query per sub_domain in parallel. This is more efficient than sequential per-domain search calls. Also useful for ambiguous / fuzzy queries within a single domain: after get_sub_domains, use batch_search to explore multiple sub_domains in parallel. ## Constraints - Maximum 5 queries per call - Each query item follows the search tool parameter structure (query is required; domain, sub_domain, sub_domain_params are optional. For general queries, omit all domain fields. For vertical queries, domain + sub_domain + sub_domain_params MUST come from get_sub_domains(domain=<domain>) output — same rules as the search tool) - Queries run in parallel; a single query failure does not block others - REQUIRED PARAMS: Same rule as search — when a required param from get_sub_domains is not applicable, pass it as an empty string (key: ""). Never skip required params. ## Examples ### Single-domain batch (multiple sub_domains) Instead of: search(query="latest TSLA earnings", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA stock forecast", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA analyst rating", domain="finance", sub_domain="finance.us_stock") Use: batch_search(queries=[{query:"latest TSLA earnings", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA stock forecast", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA analyst rating", domain:"finance", sub_domain:"finance.us_stock"}]) ### Multi-domain batch (after get_sub_domains with multiple domains) After: get_sub_domains(domains=["finance", "health", "legal"]) Use: batch_search(queries=[ {query:"AI regulation impact on healthcare stocks 2025", domain:"finance", sub_domain:"finance.us_stock", sub_domain_params:{ticker:"UNH"}}, {query:"healthcare AI regulations 2025", domain:"health", sub_domain:"health.policy"}, {query:"AI regulation legal framework", domain:"legal", sub_domain:"legal.legislation"}]) ### Hybrid: general + vertical in parallel (universal pattern for any borderline query) Use this whenever you are unsure if the query is pure encyclopedia or domain-specific — fire BOTH channels in batch_search: batch_search(queries=[ {query:"..."}, // general — no domain {query:"...", domain:"...", sub_domain:"..."}]) // vertical channel(s) This applies universally: classical texts, financial concepts, legal theories, historical events, scientific discoveries, medical topics — any query where domain knowledge could enrich the encyclopedia answer.

  Connector

  Destructive

  No auth

• explain_three_primitivesA

  DiscreteRateA

  Return a focused write-up of the three DRS modeling primitives: Constraint (rate-limiter), Buffer (accumulated state), Interrupt (stoppage). Use this when the user asks specifically about modeling primitives or how to spell a system in DRS. Deterministic text.

  ConnectorNo auth

• get_buying_guideA

  cyclesite-mcp-serverA

  Search Cyclesite's expert buying guides (24+ articles by cycling-journalism authors). Returns up to 3 matching guides with title, excerpt, difficulty, reading time, and URL. Use for educational queries that don't need live inventory. Example: 'how do I choose a bike size?', 'tips for buying a used e-bike'.

  ConnectorNo auth

• recipe_runA

  local-mcpA

  Executes a recipe end to end: binds params, runs each step's tool in order via the registry, persists the run (see recipe_runs), and returns each step's result plus any markers_path. Recipes with state-changing steps (write/send/delete) PREVIEW first — call again with confirm:true to execute; read-only recipes run immediately. A step that errors stops the run and is reported.

  ConnectorOAuth