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470,139 tools. Updated 2026-08-21 22:05

"Consulting social networks" matching MCP tools:

  • Find bike-share networks near a lat/lon: "bike share near me", "find bike rental network by location", "citybikes nearby coordinates". Returns the closest networks sorted by distance with their id, name, city, country, and distance_km. If none fall within radius_km, returns count:0 plus a note naming the single nearest network beyond the radius. To then get live stations and free bikes for a returned network, call get_network with its id. Example: Göttingen (latitude 51.53, longitude 9.93) → nextbike-kassel ~39.5km away.
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  • Create a new visitor session and obtain a visitor access token for site "CodeStringers Zoho Consulting Services" (https://www.codestringers.com/_api/mcp). You must use this tool before calling CallWixSiteAPI for this first time. If you already have a visitor token in your context, DO NOT USE THIS TOOL AGAIN.
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  • [Read] Search and analyze X/Twitter discussions for a topic, with tweet-level evidence and cited posts. Aggregate social mood, sentiment score, or positive/negative split -> get_social_sentiment. Open-web pages -> web_search. Multi-platform social search -> search_ugc. Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.
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  • Real-time X/Twitter sentiment narrative. Pass ticker=NVDA for a focused fintwit read on a name, or query=... for a free-form social-media question. Returns the narrative answer with quantified bullish/bearish ratio and any source URLs social-search grounded against. Use when you want the *vibe* on a name right now (retail sentiment, breaking rumours, unusual social activity), not the news article list.
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  • List employers from the State of ATS 2026 dataset, optionally filtered by ATS vendor (case-insensitive substring, e.g. 'workday', 'greenhouse', 'oracle') and/or industry (substring, e.g. 'health', 'consulting'). Returns at most 100 rows per call — narrow the filters or fetch the full dataset from https://withresumeai.com/api/v1/ats.
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  • Browse all bike-sharing networks worldwide. Returns network name, ID, city, country, and coordinates for each network.
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  • Social Signal — post-VOLUME TIME SERIES across the open social networks that

  • social media video downloader: A comprehensive API for extracting video details, metadata, and.

  • Return the Dutch social-domain profile for one municipality. Given a CBS GM-code, returns that municipality's four v1 social-domain indicators — social-assistance receipt, modelled homelessness, Wmo use and youth-care use — each with its raw value, source and whether it was measured or modelled. The composite score and rank are included for context, alongside the v0-equivalent score. Read-only, no personal data. wmo_pressure and youth_care_load are context only — never folded into the score. CBS aggregates describe an area, not its quality. Netherlands-only: the deeper municipal layer exists for Dutch municipalities.
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  • Compare the social-domain profile of several Dutch municipalities. Given two to six CBS GM-codes, returns a side-by-side comparison of their four v1 social-domain indicators plus composite score and rank. Useful for an agent answering "how does municipality A compare to B on the social domain". Read-only, no personal data. wmo_pressure and youth_care_load are context only — shown but never scored. CBS aggregates describe an area, not its quality. Netherlands-only.
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  • Look up networks in PeeringDB by name or ASN. Returns peering policy (Open/Selective/Restrictive), traffic level, info type (Content/NSP/ISP/Enterprise), scope, and IPv4/IPv6 prefix counts. Provide a name query or an ASN. Keyless.
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  • List MockHero's pre-built schema templates for ecommerce, blog, SaaS, and social apps.
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  • Query social attention contagion metrics from the observation stream. Returns windows where attention propagated between viewers (social amplification factor > 1). Social attention data is produced by the AttentionGraphBuilder running on CTV edge devices, which models viewer attention as a directed graph and detects when one viewer looking at the screen triggers nearby viewers to also look (attention contagion / social amplification). WHEN TO USE: - Finding moments where social proof drove collective engagement - Identifying which venues or dayparts exhibit highest attention contagion - Understanding cascading attention patterns (cascade depth) - Correlating social amplification with ad effectiveness (VAS) RETURNS: - data: Array of observation_stream rows with socialAttention payload - payload.socialAttention.socialAmplificationFactor (SAF): ratio of actual-to-expected group attention (>1 = contagion detected) - payload.socialAttention.cascadeDepth: max depth of attention propagation chain - payload.socialAttention.viralAttentionScore: composite metric combining SAF and cascade depth - payload.socialAttention.contagionWindowMs: time window over which cascade occurred - payload.socialAttention.triggerViewerIndex: which viewer initiated the cascade - metadata: { result_count, time_range, min_saf_filter } - suggested_next_queries: Follow-up queries EXAMPLE: User: "Show me moments where attention went viral in bar venues" get_social_attention({ min_saf: 2.0, venue_type: "bar" }) User: "Find the strongest social amplification events this week" get_social_attention({ min_saf: 3.0, time_range: { start: "2026-03-09T00:00:00Z", end: "2026-03-16T00:00:00Z" } })
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  • [Read] Get the top 2-4 social discussion themes for one coin over the latest 4h, including direction, influence, sentiment, platforms, and representative evidence posts. For arbitrary social search use search_ugc; for X/Twitter-only narrative research use search_x. Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.
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  • [Read] Get a coin's 24h multi-platform social mention burst signal, growth, sentiment direction, platform breakdown, and display eligibility. For general sentiment ratios and sample tweets use get_social_sentiment; for individual social discussions use search_ugc. Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.
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  • [Read] Aggregate per-coin social sentiment for a time range: overall sentiment, positive/negative split, mention count, and sample tweets. X/Twitter post search or tweet-level evidence -> search_x. Multi-platform social thread search -> search_ugc. Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.
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  • Analyze a text passage for overused industry jargon using a tech, finance, consulting, startup, or combined dictionary. Returns buzzword count, density score (0-100), severity level, flagged terms, and an optional roast critique when roast=true.
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  • Lobbying and transparency profile of a French company from the official HATVP register of interest representatives: registration status, category, lobbying-expense brackets per year, recent subjects with intervention domains, clients (for consulting firms), affiliations, declaration defaults. Organisation-level only — no personal data. `inscrit: false` is a meaningful answer. Paid via x402 ($0.01 in USDC or EURC).
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  • SOCIAL MEDIA VOLUME as a TIME SERIES: how many posts mention a topic, bucketed by day or hour, so you can see the POST VOLUME TREND and whether SOCIAL CHATTER ABOUT A TOPIC is rising or fading. Answers "how much is X being discussed", "BUZZ OVER TIME for X", "is attention on X growing this week". Returns a per-bucket count broken out by network, running totals, engagement sums where the source exposes them, and an explicit window_actually_covered block. A bucket is a number only where that network was truly measured; where reach ran out it is null, so a measured zero is always distinguishable from an unobserved gap. Coverage is Bluesky, Mastodon, Reddit and Hacker News; X/Twitter is excluded (paid API), so results are a directional proxy over a partial slice of social media rather than total social volume. Examples: topic "bitcoin", days 7, bucket "day" for a week-long attention curve; topic "openai", days 2, bucket "hour", networks ["bluesky","hackernews"] for an intraday spike check.
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  • The population behind a single client fingerprint: how many source IPs carry it, across how many networks (ASNs) and countries, the ports they hit, the top networks and a sample of the IPs, plus a read on whether it is concentrated (a likely coordinated operation, many IPs on few networks) or spread thin (a common client). Use when a user asks: 'is this JA4 one botnet or a common tool?', 'how many networks use this HASSH?', 'how specific / concentrated is this fingerprint?'. fp_type: 'ja4' (TLS), 'ja4h' (HTTP), 'hassh' (SSH). Covers the full retained window (no date range).
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