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466,528 tools. Updated 2026-08-19 16:37

"Improving and Refining Prompts for Various Uses" matching MCP tools:

  • Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after document.extract_text or url.extract when you need to validate specific factual assertions. For open-ended questions about a document, use url.qa instead. For multi-document investigation, use collection.ask. Typical workflow: document.extract_text/url.extract → document.check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"
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  • Plan the parallel sub-analyses for a threat-modeling phase. Given the current phase of a threat_model workflow (its id comes from get_current_step) and your workflow context, returns the recommended breakdown: which analysis prompts to run, with what arguments, which can run in parallel, and an inline fallback for MCP clients that cannot invoke prompts. phase_id is one of: phase_0b_scope_check, phase_1_scope_and_dfd, phase_2_stride_enumeration, phase_2b_domain_challenge, phase_3_scoring, phase_3b_threat_enrichment, phase_5_mitigation, gap_assess_controls. Optional — the workflow works without it; use it to speed up large systems by fanning phases out to subagents.
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  • Analyse the long-term trend in a pollutant near a location. Uses Theil-Sen slope estimation with Mann-Kendall significance testing to determine whether air quality is improving, worsening, or stable. Robust to outliers and missing data. Returns a 'summary' with plain-English trend description and statistical details. Present the summary to users first. Args: location: Postcode, place name, or "lat,lon". pollutant: Pollutant to analyse — "NO2", "PM2.5", "PM10", "O3" (default "NO2"). years: Number of years of data to analyse (default 5, range 2–5). Requests outside this range are clamped; the response includes ``metadata.years_clamped`` and a note in ``summary`` when so.
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  • Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."
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  • Compile a minimal JSON schema directly to Swift, bypassing the TypeScript DSL entirely. Supports intents, views, components, widgets, and full apps via the 'type' parameter. Uses ~20 input tokens vs hundreds for TypeScript — ideal for LLM agents optimizing token budgets. Use: use for token-light JSON-to-Swift generation; use compile for full TypeScript DSL control and scaffold for TS starters. Inputs: schema kind selects intent, view, widget, or app output; options add companion metadata. Effects: read-only Swift generation; writes no files and uses no network.
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  • List active governed inference offers visible to the registered requesting agent. Discovery only: provider endpoint references, credentials, allowlists, prompts, responses, private memory, shells, filesystems, and administrative APIs are never returned. Direct Session execution is currently gated. Requires an Authorization: Bearer header.
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Matching MCP Servers

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  • Free copy-and-run ChatGPT prompts for online stores: 924 prompts, 43 categories + 10 tasks.

  • .prompts, the home to all your AI prompts, everywhere you need them.

  • Lists Vocab Voyage's MCP starter prompts (also exposed via the standard MCP prompts/list endpoint). Useful for hosts that don't yet support prompts/list.
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  • Returns a plain-English usage guide for this server — example requests, what it asks the user for, and the available tools. Call this if the user asks how to use Abby SEO, or to orient yourself before starting. (Same content as the 'getting_started' prompt, exposed as a tool for clients that don't surface MCP prompts.) Takes no arguments.
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  • Fetch SnowSure-unique ML/AI trend datasets from the public REST API. Use for powder-day leaders, bluebird-day leaders, bluebird predictions, improving/stable/declining score pulse, per-model accuracy weights, daily SnowSure score component history, ML extended outlook (days 8–14), global forecast trust, and powder/bluebird event logs. Start with dataset=catalog. Its leaderboards read CURRENT-season counters and are global — they take no season and no country/state filter. For a past season, or for any ranking scoped to a state, province, country or region ("most snow days in Maine last season", "rank BC resorts by season snowfall"), use get_season_leaderboard instead. Prefer get_insights for narrative intelligence cards; use this for raw rankings and time series.
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  • Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."
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  • Return a short, human-readable walkthrough for testing this server: the endpoint, the tool/prompt/resource names, and ready-to-paste sample prompts. Use to give someone a guided demo. For the full machine-readable capability catalog, use list_capabilities instead.
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  • Gender Risk & Opportunity Intelligence — maps the structural relationship between GBV prevalence, legal discrimination, female labour force participation, and economic outcomes across 18 countries. Returns two independent scores: gbvRiskScore (0–100 suppression risk — high GBV → female LFPR suppression → GDP drag → fiscal stress → sovereign risk premium) and opportunityScore (0–100 reform upside — improving GBV indicators, closing LFPR gender gaps, and strengthening legal rights precede FDI inflows and consumer credit expansion). Five transmission mechanisms. Live FRED economic stress feedback. AI synthesis. Data: WHO GHO, World Bank WDI, FRED. 12h cache. No input required — GET.
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  • PAID but very cheap (one Gemini call) and SYNCHRONOUS — the result comes back in this response, there is no jobId to poll. Turns a rough game idea into { aiVision, aiPromptGuidance, aiNegativeGuidance }: a written creative direction plus the positive/negative guidance to reuse in later prompts, which is what keeps a whole project visually consistent instead of every asset drifting. Stores NOTHING — feed the wording into create_project (description/style) and into your own prompts. DEFAULTS TO A COST PREVIEW — see the dryRun argument.
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  • List only the authenticated user's private reusable LetMeActForYou actors, newest first. Free. Supports bounded offset pagination and never returns internal identity prompts or visual descriptions.
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  • Fetch records from any India Open Government Data (data.gov.in) resource by its resourceId. Supports pagination, per-field filtering, field projection, and sorting. The resourceId is the UUID shown on a dataset's page on data.gov.in (and in its API URL, e.g. api.data.gov.in/resource/<resourceId>). Example resourceId 9ef84268-d588-465a-a308-a864a43d0070 is "Current Daily Price of Various Commodities from Various Markets (Mandi)" with fields like state, district, market, commodity, variety, grade, arrival_date, min_price, max_price, modal_price. Use resource_meta first if you do not know a resource's field ids.
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  • Calculate numerology compatibility between two people using Pythagorean numerology. Accepts two input modes per person: pre-calculated Life Path, Expression, and Soul Urge numbers, or raw name and birthdate for automatic calculation. You can mix modes across persons (e.g. numbers for person1, raw inputs for person2). Provides comprehensive relationship analysis with overall compatibility score (0-100), individual aspect compatibility (Life Path 50% weight, Expression 30%, Soul Urge 20%), relationship strengths, challenges, and practical advice. Uses detailed compatibility matrix for all number combinations. Perfect for dating apps, relationship counseling platforms, matchmaking services, and compatibility tools. Get actionable insights for improving relationship dynamics.
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  • Ask PodMule for a concise read-only domain answer or an advisory route plan. Use this for quick questions, summaries, diagnostics, drafting, "what should I do?" prompts, and ambiguous action requests. This tool never performs writes. For exact capability contracts use search; for one selected action (read or write) use execute.
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  • Set a project's product vision (prepended to PM/dev/QA iter prompts). `vision_md` is free-form markdown and must be non-empty. Tenant-scoped: a project not in the caller's workspace 404s. Returns {project_id, product_vision_md, updated_at, next_step}.
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  • Gender Risk & Opportunity Intelligence — maps the structural relationship between GBV prevalence, legal discrimination, female labour force participation, and economic outcomes across 18 countries. Returns two independent scores: gbvRiskScore (0–100 suppression risk — high GBV → female LFPR suppression → GDP drag → fiscal stress → sovereign risk premium) and opportunityScore (0–100 reform upside — improving GBV indicators, closing LFPR gender gaps, and strengthening legal rights precede FDI inflows and consumer credit expansion). Five transmission mechanisms. Live FRED economic stress feedback. AI synthesis. Data: WHO GHO, World Bank WDI, FRED. 12h cache. No input required — GET.
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  • Return the most-recently-published Declassified cases over a rolling window. Use for 'what's new in Declassified this week' style prompts. window=today (~24h) | week (default) | month. Public — no auth required.
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