Skip to main content
Glama
743,094 tools. Updated 2026-11-04 18:29

"Deep research on Gemini" matching MCP tools:

  • Run a CanaryUsers UX scan on a DEPLOYED URL (your live or preview app — not source code). A flock of AI personas evaluates the page and reports where real users would get stuck, with concrete fixes. Returns AI-ready findings you can act on immediately. Use depth='deep' for the thorough scan that renders the page, checks it VISUALLY on desktop + mobile (catches mobile breakage and layout issues), and clicks through key flows like signup/checkout (slower, ~60-90s, uses one credit); depth='quick' (default) is a fast static check that does NOT see mobile or visual issues — use 'deep' when the user mentions mobile, layout, or visual problems. IMPORTANT: if this returns status 'running' with a scanId, the findings are not ready yet — wait ~30s, then call get_report_markdown(scanId), repeating until it returns the report. Always fetch and present the findings before stopping, then offer to fix the top issues.
    ConnectorNo auth
  • 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.
    ConnectorNo auth
  • Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; nothing is written, so it is safe to call.
    ConnectorNo auth
  • Confirm a specific, named business in one jurisdiction — the PRIMARY tool whenever the user wants to verify, check, confirm, or look up a company's existence, status, good standing, or details (e.g. "verify Acme LLC in Delaware", "is Acme registered in FL?", "I need to verify a company in Delaware"). If the user has verification intent but has not given the exact company name, ASK them for the name and use THIS tool — do NOT fall back to search_entities. Two tiers: quick (1 credit) returns existence + status + good-standing. Deep (15 credits, or 25 with force_refresh) adds entity type, formation date, registered agent, officers, principal address, and filing history. Deep is available in a subset of jurisdictions; requesting deep where unavailable returns a quick result with a reason. Requires authentication. A completed verification deducts credits whether or not the business is found — a confirmed no-match is a result. Calls that cannot produce an answer (source unavailable or timed out) are refunded.
    ConnectorNo auth
  • Map the full dependency tree of an npm package and identify CRITICAL supply chain risks at every level. Unlike auditing a flat list of packages, this tool traverses the dependency graph — showing not just your direct dependencies but also what your dependencies depend on. Hidden CRITICAL packages (sole publisher + >10M weekly downloads) often lurk 1-2 levels deep. Risk flags: - CRITICAL: single npm publisher + >10M weekly downloads — sole point of failure for a massive attack surface - HIGH: sole publisher + >1M/wk, OR new package (<1yr) with high adoption - WARN: no release in 12+ months (potential abandonware) depth=1 (default): root package + all direct dependencies depth=2: also traverses one more level for any CRITICAL/HIGH direct deps (reveals hidden exposure) Examples: - audit_dependency_tree("express") — see all of Express's deps and their risk scores - audit_dependency_tree("langchain", 2) — reveal transitive CRITICAL deps 2 levels deep - audit_dependency_tree("@anthropic-ai/sdk") — audit Anthropic SDK full tree Use this when someone asks: - "What am I really depending on?" - "Are my dependencies' dependencies safe?" - "Show me the full supply chain risk for package X"
    ConnectorNo auth

Matching MCP Servers

Matching MCP Connectors

  • Discovery stage — estimate a property's monthly RENT (POST /gemini-estimate). No account or prop_id needed. Pairs with `beycome_estimate`, but this tool is SPECIFICALLY for rental estimates: use it ONLY for rental inquiries or for properties that have a unit number. For sale-price estimates, use `beycome_estimate`. Returns ``rentEstimate`` (e.g. "$2,600"), ``rentEstimateRange`` (e.g. "$2,400 - $2,800"), and the ``sources`` the figure was grounded on (source URLs are stripped to keep the response small). Caveat: an unrecognized address still returns ``success: true`` with a generic market-average rent — check that the source titles actually reference the property's address/city before trusting the number; if they don't, treat the result as unavailable and ask the user to verify the address.
    ConnectorNo auth
  • Launch a full overnight-grade research report on a local business: an autonomous research crew maps the competitive landscape, reads the business's and rivals' web presence, and delivers a long-form graded report with structured findings and an action list. ASYNC: this tool returns a slug immediately; the report takes roughly 10-20 minutes. Poll check_report_status, then fetch with get_report_result. Price: $9.99, charged when the report launches; if the report fails, the charge is refunded automatically. Each call starts a NEW report — do not retry a call that already returned a slug.
    ConnectorNo auth
  • Use this when a deep research run needs to look up digital tools and products on uneed.best. Same catalog and same relevance ordering as search_products, returned as `{id, title, url}` documents; pass a result's id to `fetch` for the full profile. Prefer search_products when you want structured product fields directly.
    ConnectorNo auth
  • List the provider API keys your owner has stored in their Vault (e.g. Gemini, ElevenLabs, OpenAI) so you can use them in a task. Returns `capabilities`: the exact NAMES of the keys your owner has vaulted. Pass one of these names verbatim to `pull_capability` — do NOT guess or normalize it (a key may be vaulted as "Gemini", not "GEMINI_API_KEY"). Names only, never secrets, so this is safe to call freely. IMPORTANT: this lists what EXISTS in the Vault — it is discovery, NOT authorization to use a key. Only pull and use a key when your OWNER directs you to in this thread. Takes no arguments.
    ConnectorNo auth
  • Fetch a single post by id: views, likes, comments, engagement rate, outlier scores for seven time windows, thumbnail and the owning profile. When a transcript or visual analysis already exists it is included at no extra cost. The visual analysis is a structured scene-by-scene breakdown (per-scene timing, on-screen text, visual elements and a recreation note) plus an overall-style summary. Request new enrichment via request_transcript (speech / on-screen text) or request_visual_analysis (scene breakdown). Use after search_outliers to deep-dive a result. Cost: 1 credit per call.
    ConnectorNo auth
  • Embed a text string into a 768-dim Gemini Flash vector. Use for HyDE-style retrieval: (1) write a hypothetical short paper that would perfectly answer the user's query, (2) embed it with task_type='RETRIEVAL_DOCUMENT' (default — matches the corpus embedding side), (3) pass the resulting embedding back through search-style tools to find real papers nearest to the hypothetical. task_type='RETRIEVAL_QUERY' matches the query side and is useful for direct user-query embedding without HyDE. Pro-only — requires an SF_API_KEY on a Pro account; anonymous and free callers get a 403 pro_required. Cost: ~$0.0001/call; rate-limited at 30/minute per API key.
    ConnectorNo auth
  • Checks the status of a deep report or export record (by its record id, e.g. from smi_create_deep_report draftIds). Returns state = ready | in_progress | draft | failed | unknown and a terminal flag. Poll with bounded attempts and backoff while in_progress; stop as soon as terminal is true (ready or failed) — a failed record will never become ready. Also stop when state is draft: the record was never submitted, so it will not progress until smi_submit_deep_reports or smi_submit_exports is called on it.
    ConnectorOAuth
  • Open-ended discovery across SMI deep reports, research bundles, and exports when you do not already have a record id — finds candidates by subject name, url, or keyword and returns typed references to pass to fetch. If you already have a record id from the conversation rather than from a search result, call the specific tool instead: smi_get_deep_report or smi_get_export to read one record, smi_check_order_complete for order readiness, smi_list_deep_reports or smi_list_exports for filtered listings, smi_get_report_findings for findings within a report. Deep reports and research bundles are filtered server-side by subject first/last name, broadening to an unfiltered fetch ranked client-side (across id, name, url, summary) when the filtered fetch finds nothing; exports match on url.
    ConnectorOAuth
  • Search this Cubite site's courses by keyword and get each match's public page URL, for linking to or citing it. This is the search half of the search/fetch pair that ChatGPT connectors and deep research require. The whole query is matched as one case-insensitive phrase, so use one or two distinctive words. Courses match on name or description and are listed in name order; a course without a URL slug has no public page and is never returned, while courses hidden from the catalog are. Returns up to 10 results as {id, title, url}; pass a result's id to `fetch` for its citable text. It is not an inventory: to list, count or manage courses use course_list, which has no keyword filter but returns every course, with or without a slug, with its id, settings and enrollment count.
    ConnectorAPI key
  • Multi-step deep research: search, scrape, enrich entities, and synthesize into a cited report. Returns a report (title, summary, sections, key findings, confidence) with numbered sources and the people and companies it found, enriched. Use for a question that needs many sources and a synthesized, cited answer. It takes far longer and costs more than search, which suits a quick fact. Cost: standard 10 credits, deep 20 credits, comprehensive 35 credits, charged only when a call succeeds. Options change it: include_analysis true +5, academic in source_types +2, enrich_entities false -3, synthesis false -5.
    ConnectorNo auth
  • 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
  • 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.
    ConnectorNo auth
  • Consume the 6-digit code the founder received and start the research pipeline. **When:** after `create_application`, and ideally after the deck has been uploaded over plain HTTP (see the `curl` printed by `create_application`). Founder has read the code from their inbox. **Before you call:** - CONFIRM you have the right code from the founder (not guessing, not stale). Once this succeeds, research starts and is irreversible. - If deck parsing is still pending, that's fine — research auto-defers and dispatches when the deck resolves. Don't wait, don't re-verify. **Returns:** `status: researching` + whether research was dispatched now or deferred on the deck. **On error:** - `wrong code` — response includes attempts-remaining. Ask the founder to check their inbox again. If the error says the code is invalidated, call `resend_verification` for a new one. - `no active code` — they never received one, or it expired. Call `resend_verification`. - Already verified — idempotent success; returns current status. Don't retry in a loop.
    ConnectorNo auth
  • 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
  • Generate a BRAND-NEW image via Gemini; stores it and returns image_url (+ markdown preview). Billed — REQUIRES confirmSpend=true after a REAL human yes in a SEPARATE message. STANDALONE: omit sitePageId → account-root AI folder; after success SHOW the image. PAGE/SITE: forPage=true AND sitePageId=<page id> → Sites/{site}/{page}/AI; propose prompt+quality+charge first, then place the URL on the page. quality: standard/high/premium. On aiSpendExhausted: warn + Billing — do not retry.
    ConnectorOAuth