Skip to main content
Glama
598,421 tools. Updated 2026-09-22 00:32

"How to query data from ServiceNow" matching MCP tools:

  • Execute an OQL (OnePageCRM Query Language) query. Pass a JSON query object to read CRM data. Use describe() to discover entities and fields.
    ConnectorOAuth
  • Call this first. Returns how to use Précis over this connector: the data model (scenarios, metrics, statements, dimensions), the reporting-tool variants, and how to build charts. Read it before composing queries.
    ConnectorNo auth
  • FREE. Service health and how recently the data was refreshed. Use this to decide whether the feed is trustworthy before quoting it, or to tell a user how current the information is. Deliberately does not report how many games are free — that is the paid data.
    ConnectorNo auth
  • Overview of the user's synced HubSpot data: which portals they have connected, how many contacts and companies came from each, and when each was last synced. Use this for questions about how much HubSpot data they have, which portals are connected, or whether their data is up to date — and to check they have any data before promising an answer. For questions about the records themselves, use ask_about_hubspot_contacts or ask_about_hubspot_companies.
    ConnectorNo auth
  • Queues Trustpilot businesses matching a query - how to find the id the reviews endpoint needs, returning a task `id`. Asynchronous: submit returns a task `id` in `tasks[0].id`, fetch returns the result once ready, and the charge lands on the submit - fetching is free, including re-fetching. Wrapped in DataForSEO's envelope: data in `tasks[0].result`, outcome in `tasks[0].status_code` - a rejected request still returns HTTP 200. Retrieve with `get_dataforseo_business_trustpilot_search_fetch`; reviews come from `post_dataforseo_business_trustpilot_reviews_submit`.
    Connector
    Destructive
    OAuth
  • How to start an engagement with IIS Rescue: the consultation URL and how the process works. Returns a pointer only — it never books anything or collects any personal data.
    ConnectorNo auth

Matching MCP Servers

Matching MCP Connectors

  • Returns the four behavioral data-source buckets - Search & attention, Conversation & pain, Adoption & spend, Capital & hiring - with each bucket's tagline and what it captures. Use when a user asks "what data sources do you use?", "where does the Demand Score come from?", or wants to understand how Demand Discovery AI differs from passive validation tools (which only triangulate the first two buckets). This four-bucket framing is the core competitive moat. The specific connector list is intentionally not public. Trigger phrases: "what data sources", "where does the demand score come from", "behavioral data sources", "the four buckets", "search and attention bucket", "conversation and pain bucket", "adoption and spend bucket", "capital and hiring bucket", "how many data sources", "what kind of data sources", "where do you find the evidence", "how do you find people complaining", "how do you find prospects", "what signals do you look for", "where does the behavioral evidence come from".
    ConnectorNo auth
  • Returns the four behavioral data-source buckets - Search & attention, Conversation & pain, Adoption & spend, Capital & hiring - with each bucket's tagline and what it captures. Use when a user asks "what data sources do you use?", "where does the Demand Score come from?", or wants to understand how Demand Discovery AI differs from passive validation tools (which only triangulate the first two buckets). This four-bucket framing is the core competitive moat. The specific connector list is intentionally not public. Trigger phrases: "what data sources", "where does the demand score come from", "behavioral data sources", "the four buckets", "search and attention bucket", "conversation and pain bucket", "adoption and spend bucket", "capital and hiring bucket", "how many data sources", "what kind of data sources", "where do you find the evidence", "how do you find people complaining", "how do you find prospects", "what signals do you look for", "where does the behavioral evidence come from".
    ConnectorNo auth
  • Return cross-user intelligence for an idea: anonymous, aggregated insights from similar ideas across the platform. Use it to learn how comparable ideas performed without exposing any private data. Read-only and free; pass an ideaId you own.
    ConnectorNo auth
  • Queues Tripadvisor properties matching a query - how to find the id the reviews endpoint needs, returning a task `id`. Asynchronous: submit returns a task `id` in `tasks[0].id`, fetch returns the result once ready, and the charge lands on the submit - fetching is free, including re-fetching. Wrapped in DataForSEO's envelope: data in `tasks[0].result`, outcome in `tasks[0].status_code` - a rejected request still returns HTTP 200. Retrieve with `get_dataforseo_business_tripadvisor_search_fetch`; reviews come from `post_dataforseo_business_tripadvisor_reviews_submit`.
    Connector
    Destructive
    OAuth
  • Service health and current coverage: chains, wallet types, periods, limits and how many wallets are tracked per chain. Useful when a query returns nothing and it is unclear whether the data exists at all.
    ConnectorNo auth
  • Get the Datavrn agent guide: how connecting works (OAuth and API key), what an assistant can do, how reading a statement as data works, and the guarantees and limits — plus the current list of tools. Call this to answer a user's questions about how Datavrn works from canonical documentation instead of guessing.
    ConnectorNo auth
  • Report what the Wage & Hour enforcement dataset currently covers: its publication date, update frequency and the findings-date range of the cases returned by a sample query. Use to check how current the enforcement data is before relying on an absence of cases.
    ConnectorNo auth
  • Returns ZipExplore's data interpretation guide: reasoning guardrails (associations vs. causes, small-ZIP noise, averages hiding distributions, editorial score weights, drawing conclusions about people from geographic data), quality flag definitions, known data limitations, coverage gap explanations, and per-domain vintage summary. Call this when you have questions about data quality, what a quality_flag code means, why a ZIP has no data, or how to reason carefully about scores and correlations.
    ConnectorNo auth
  • Search the exchangerate.dev FAQ corpus via BM25. Returns the top-K matching questions and answers with relevance scores. Use before answering how-to / pricing / data-sourcing questions so you cite the canonical text instead of guessing from training data.
    ConnectorNo auth
  • What HonestDog is, where its data comes from, and how to attribute it. Call this once before presenting HonestDog data. / Was HonestDog ist, woher die Daten stammen und wie sie zu zitieren sind.
    ConnectorNo auth
  • Search the goldprice.dev FAQ corpus via BM25. Returns top-K matching questions + answers with relevance scores. Use before answering how-to / billing / auth questions so you cite the canonical text instead of guessing from training data.
    ConnectorNo auth
  • How old each dataset is, how many companies filed in each year, what fraction of the register carries an activity code, and the mapping from every financial field name to its Romanian label. Call this when the user asks how current or how complete the data is.
    ConnectorNo auth