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741,228 tools. Updated 2026-11-04 02:41

"A resource for finding prompts" matching MCP tools:

  • Get live public-transport departures with real-time delays for a stop. Sourced from GTFS-RT/HVV/VGN. Unlike the static stop list (``get_city_resource(slug, resource='transit')``), this returns minute-fresh departures including delay for ONE stop. A ``stop_id`` is required: fetch the city's transit stops first to discover valid stop IDs, then pass one here. Read-only.
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  • Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.
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  • Get the current user's available AI tokens Returns the number of AI tokens available to the authenticated user. These tokens fund EVERY AI feature in SERPmantics — meta, outline, intent, internal-links, EEAT, EEAT competitors, score, AND the AISSistant prompts. The endpoint lives under /aissistant for historical reasons but the balance is shared across all AI features. Do NOT confuse with guide-creation credits (see /api/v1/credits). For a combined view (credits + tokens) prefer /api/v1/credits.
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  • Lista os MCP Prompts disponíveis com nome, descrição e argumentos. Tools de descoberta para clientes (como o Claude.ai web) que ainda não expõem UI para prompts. Em Claude Desktop / Cursor / MCP Inspector, prompts aparecem em UI dedicada — esta tool é um caminho alternativo, não substituto. Use depois `compras_obter_prompt(nome, argumentos)` para renderizar um prompt específico. Retorno: { "total": int, "prompts": [ { "nome": str, "descricao": str, "tags": [str, ...], "argumentos": [ {"nome": str, "descricao": str | None, "obrigatorio": bool}, ... ] }, ... ] }
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  • Retrieve one complete prepared resource profile, including evidence-backed facts and primary-source provenance, by its stable resource ID.
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  • Returns the plain-language meaning and step-by-step fix for one Malinois finding. Use it while helping the user fix an issue reported by scan_app, or when they ask what a finding means. Pass the rule_id exactly as scan_app returned it. Read-only, no network, instant.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    B
    maintenance
    面向 Codex、WorkBuddy 等 AI 工具的知你AI助手合并版 Skills,涵盖当前会话分诊、客户查询与画像、历史会话检索、客户分群和 API Key 用量查询,并连接个微、企微、视频号、微信小程序、公众号、服务号、微信客服、微信小店、抖音号、小红书、微博、网站及H5客服等渠道。
    MIT No Attribution
  • A
    license
    Not graded
    quality
    C
    maintenance
    这是面向智能运维场景的 MCP 工具服务,让 Agent 能够按需检索日志内容并获取主机与应用的监控指标数据。它把实时的日志与指标证据接入 Plan-Execute-Replan 诊断流程,从而支撑自动故障排查与根因分析。
    3
    MIT

Matching MCP Connectors

  • Create a pitch workspace for prospecting: prompts are auto-generated from the prospect's domain and run ONCE (never on a schedule). Consumes 1 of the organization's monthly pitch allowance AND charges every generated prompt to the organization's monthly answer budget (one answer run per prompt per enabled model), so a pitch is far from free — do not create them speculatively. Results land asynchronously — poll get_pitch_status until resultsReady, then use the read tools (get_brand_rankings, list_chats, list_sources) with the new workspaceId. The pitch auto-archives after 30 days; rerun_pitch refreshes it.
    ConnectorOAuth
  • List the curated sandbox topic labels that define the free Discovery corpus — every topic federally-sourced and audience-safety-checked. Use it to preview what content domains exist before creating a stream. Does NOT enumerate messages — the corpus stays internal. For the full live template catalog + gating contract, read the signal://catalog resource.
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  • Get live arrivals for ANY railway station by its EVA number. Mirror of ``station_board_departures`` for arriving trains (all categories, real-time delays, disruption messages). Get the EVA from ``get_city_resource(slug, resource='stations')``. Read-only.
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  • Read a registered server resource URI, not an ordinary web URL. USE WHEN you have a judgment://, legislation://, hansard://, or server:// URI returned by this server. Do not use for https:// URLs; use the source-specific tool instead (for a canonical GOV.UK HMRC internal-manual URL, call hmrc_get_manual_page). Returns the resource content as a string. Binary content is base64-encoded. AFTER calling, use the returned primary-source content and URI in citations; if the URI is rejected, follow the named source-specific tool rather than treating the URL as retrieved evidence. Source: registered UK Legal MCP resource providers.
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  • Read a registered server resource URI, not an ordinary web URL. USE WHEN you have a judgment://, legislation://, hansard://, or server:// URI returned by this server. Do not use for https:// URLs; use the source-specific tool instead (for a canonical GOV.UK HMRC internal-manual URL, call hmrc_get_manual_page). Returns the resource content as a string. Binary content is base64-encoded. AFTER calling, use the returned primary-source content and URI in citations; if the URI is rejected, follow the named source-specific tool rather than treating the URL as retrieved evidence. Source: registered UK Legal MCP resource providers.
    ConnectorNo auth
  • List the curated sandbox topic labels that define the free Discovery corpus — every topic federally-sourced and audience-safety-checked. Use it to preview what content domains exist before creating a stream. Does NOT enumerate messages — the corpus stays internal. For the full live template catalog + gating contract, read the signal://catalog resource.
    ConnectorNo auth
  • List the full eDiscovery Decoder MCP surface — every tool, prompt, and resource, plus the suggested demo flow and safety boundaries — with an example prompt for each. Call this first when you are unsure which tool fits the user's question, or when tool-search shows only a partial list.
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  • The printable PDF itself, returned as a base64 embedded resource — use this when the organiser wants the file handed to them and your client cannot fetch URLs. Default "sheet": the merged tournament document (match sheet with write-in score boxes, player sheet, standings/results when they exist). "results" and "plan" are deprecated pre-merge variants kept for comparison. For a lighter response, get_pdf_link returns download URLs instead.
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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.
    ConnectorOAuth
  • Remove a recheck from schedule_recheck before it fires (nothing was charged). One that fired can't be cancelled: already_ran with its audit_id. Billing: free. When to call: the user no longer wants it (to change prompts or date: cancel, then schedule again). Returns { recheck_id, cancelled: true, already_cancelled? } or { error: not_found | already_ran | not_pending }.
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    Destructive
    OAuth
  • Bring Midjourney exports into a project (FilmLab calls this 'Umzug'). Midjourney has no API, so the person downloads their images or ZIP and this tool carries them on. Give EXACTLY ONE of: `dateien` (names/types/sizes of local files → signed PUT addresses with a ready curl line; PUT the raw bytes, then call again with `quellen` from `weiter`), `quellen` (after the PUT: starts the import job → job_id; poll lab_get_job — result.bildzeilen lists image_id and datei per image (the prompt per image comes with `bilder: true`), result.gesperrt names refused images with the reason, result.ungeprueft images whose check was unreachable), `prompts` (pasted Midjourney prompts, one per line → parameters split and what does not carry over, e.g. --sref codes; free, no job), `anker` ({name, image_ids ≤ 12} → a look anchor for lab_generate's anker_id; images of real people are refused) or `bilder: true` (the project's imported images with image_id and prompt). Every imported image is declared AI material (EU AI Act Art. 50) — no herkunft question. An image_id works directly in lab_animate (source_image_id), as a seed in lab_generate and in lab_referenz_aus_bild. Uploading is free.
    ConnectorOAuth
  • Estimate the water (mL) and energy (Wh) used by chat-style AI prompts (ChatGPT, Claude, Gemini etc.). queries_per_day: prompts per day. usage: 'simple' (text chat, ~0.34 Wh/prompt), 'mixed' (~1 Wh), 'intensive' (image generation / deep reasoning, ~3 Wh). boundary: 'onsite' (data-centre cooling only, as vendors report) or 'comprehensive' (adds water used to generate the electricity – the wider boundary most researchers use). days: number of days to total over (e.g. 250 workdays, 365). Returns per-prompt, per-day and total figures with everyday equivalents, assumptions and a source link. For agentic/coding sessions use estimate_session_footprint instead.
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  • Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, novel research hypotheses, and research resources (code repositories, datasets, models, and demos linked to papers). For novel ML/AI research ideas, use search_type="hypothesis", then get_hypothesis on promising results. search_type accepts all, cluster, paper, author, institution, venue, hypothesis, or resource. Use paper for literature and get_paper to read a result. Use other sources for biographies, news, and non-research web content. Resources: search_type="resource" searches GitHub repositories, Hugging Face datasets/models, and Zenodo records that Lacuna has linked to at least one paper. To find benchmark or evaluation datasets, use search_type="resource" with resource_kind="dataset" (for example query "image classification benchmark" or a dataset name like "ImageNet"). resource_kind accepts codebase, dataset, model, or demo (one value or a list); provider accepts github, huggingface, or zenodo. Both filters require search_type "resource" or "all" (with either filter set, only resources are returned). Each result has an external `url`, a Lacuna `context_url`, and `linked_paper_count`; call get_resource on its id to list the linked papers (with their relationship to the resource), then get_paper on those papers for reported results and numbers. Resource search does not support date_from/date_to, venue, year sorting, or semantic ranking. ranking_profile accepts: - default / lexical (default): production ranking; relevance-sorted paper searches combine lexical and semantic retrieval when fields is unset. - semantic: conceptual paper retrieval; supported for paper and all. - bm25_title_abstract / bm25: lexical paper matching over those fields. sort accepts relevance (default), year_desc, or year_asc. Semantic ranking cannot use year sorting; constrain recency with date_from/date_to instead. date_from and date_to are inclusive YYYY, YYYY-MM, or YYYY-MM-DD bounds. author_id_or_url constrains a paper search to one author. Pass an author ID or Lacuna author page URL; it requires search_type="paper". fields optionally restricts and weights lexical fields, for example "title^4,abstract". Supported names are title, abstract, summary, concepts, name, top_names, and venue, plus description, topics, provider_key, and paper_titles for resources (a resource's title already includes its description, topics, README text, and linked paper titles). Fields must exist on the selected search_type, weights must be within 0 < weight <= 100, and fields cannot be combined with semantic ranking.
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  • Run a raw SoQL query against any Los Angeles open-data resource (data.lacity.org) by its Socrata id (8-char like "2nrs-mtv8"). Full SoQL: where/select/group/order/limit/offset. Use la_datasets to find a resource id, or la_recent for the common ones.
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  • Estimate the water (mL) and energy (Wh) used by chat-style AI prompts (ChatGPT, Claude, Gemini etc.). queries_per_day: prompts per day. usage: 'simple' (text chat, ~0.34 Wh/prompt), 'mixed' (~1 Wh), 'intensive' (image generation / deep reasoning, ~3 Wh). boundary: 'onsite' (data-centre cooling only, as vendors report) or 'comprehensive' (adds water used to generate the electricity – the wider boundary most researchers use). days: number of days to total over (e.g. 250 workdays, 365). Returns per-prompt, per-day and total figures with everyday equivalents, assumptions and a source link. For agentic/coding sessions use estimate_session_footprint instead.
    ConnectorNo auth