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524,649 tools. Updated 2026-09-06 16:29

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  • Return how ONE page's Google Search performance changed over time (FD-040) — the time-axis drill-down for a page surfaced by get_breakdown(dimension='page'). Given a `page` (a normalized path like '/news/rps-revenue-per-session-guide' or a full URL — both resolve), returns a `series` of day or week buckets, each with clicks, impressions, and impression-weighted avg_position, plus a `summary` (first/last/best/worst position, position_delta, click & impression totals). avg_position is a RANK: smaller is better, so a NEGATIVE position_delta means the page's ranking IMPROVED over the window (e.g. 12.0 → 9.0 = delta −3.0). Use this to verify whether SEO work on a page paid off (rank rose / clicks grew) or slipped. Buckets where the page never appeared in search are omitted (gaps), so the series can be shorter than the period. `granularity` defaults to 'day' for windows up to ~35 days and 'week' for longer (weekly smooths daily noise); pass it to override. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Google-search only; data lags 1-2 days. This is per-page; for the cross-page snapshot use get_breakdown(dimension='page'), and for per-query (keyword) trends use get_keyword_performance.
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  • Return a content 'playbook' for the site: every content page classified into ONE of five action buckets over a weekly-style window comparison (current window vs the immediately preceding window of equal length), ranked by search-opportunity × session gain so you can tell the user which page to GROW next and what to do: within the 'striking' bucket rows are ordered by expected_sessions_gain DESC (the band-CTR headroom that is the actionable lever there), while the other buckets keep real landing revenue DESC (largest revenue at stake first). This surfaces search intent to add sessions (grow the traffic denominator), NOT CVR — a page already winning on sessions/revenue but with zero clicks still shows up. Buckets: 'decaying' (search clicks actually fell, OR the page had real traffic (previous clicks ≥3) and its rank slid ≥2 positions from within the click zone while clicks did NOT grow → refresh/rewrite; a rank slide alone with growing/negligible clicks is NOT decay — search clicks are the primary signal, position only a leading indicator), 'striking' (has striking-distance queries at positions 4-20 with click upside but clicks still low → push those queries up; top 3 listed in striking_queries), 'rising' (clicks grew significantly → produce more of this, strengthen CTA), 'dormant' (has impressions but ~0 clicks and its main query is far below the click zone → big rewrite or consolidate; zero-pageview pure-rank pages surface here), 'stable' (none of the above → watch). Each page also carries current/previous clicks·impressions·avg_position, is_new, landing sessions/engaged/revenue_jpy, AI-referred sessions/revenue/sources, expected_sessions_gain (the window's expected incremental sessions from striking-band queries — a search click is ~1 session, so it is NOT re-converted via CTR; normalize to a monthly figure with the window length), and expected_revenue_gain (expected_sessions_gain × page RPS, returned ONLY when revenue>0 and sessions>=5 — display-only projection, never a sort key). The deterministic action mapping and all (provisional) thresholds come back in `criteria`; the model does the narrative interpretation (mcp-first). Rows with confidence='low' carry `caveats` — 'geo_winning_suspect' (AI Overview/citations likely substitute the click: a GEO win, don't break the page; cross-check get_ai_traffic), 'zero_click_suspect' (SERP-feature occupation or intent-mismatch/polysemous query: verify the live SERP first), 'ai_cited' (decaying but the AI citation is alive) — verify before acting on low-confidence rows; definitions in criteria.caveat_flags. The response is summary-first (token-aware): `bucket_summary` always holds the FULL pre-limit distribution (per-bucket count/revenue/clicks) plus `total_pages`, while `pages` returns only the top rows in priority order (default limit 15, max 200); pass bucket='striking' etc. to drill into one bucket, and check `truncated` — when present it tells how many rows were cut and how to fetch them. GSC-driven and Google-search only; data lags 1-2 days so the current window's right edge sits a few days back. When narrating a bucket to the user, scope it to Google search — say 'search traffic to this page is declining', NOT 'this page is declining'; the classification is a search-trend diagnosis, not overall page health, so a page labeled 'decaying' can be thriving on Direct/social/AI. Before calling a negative bucket (decaying/dormant) a problem, cross-check the row's landing sessions/revenue_jpy and ai_sessions. site_id is OPTIONAL when OAuth-authenticated. Default window is the last 7 days vs the prior 7; pass period='30d'/'90d' or a raw day count (2-365). Window date bounds are INCLUSIVE on both ends, so period=Nd actually spans N+1 calendar dates (the real range is in `window`); both windows share the same length, so the comparison stays symmetric. This is the cross-page action snapshot; for one page's time series use get_page_trend, and for AI-citation gaps use get_ai_traffic(mode='gaps').
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  • Return the latest competitor SEO snapshot for the site (FD-041): which keywords each tracked competitor DOMAIN ranks for on Google (Japan/ja), at what position, with monthly search_volume, cpc and etv (estimated monthly traffic — a visit estimate, not a monetary value), plus how each rank moved vs the previous snapshot. READ-ONLY — this tool never runs a research (that costs money and is triggered separately from the dashboard, the competitor-research Edge Function); it only reads what was already fetched. The response is summary-first (token-aware): each domain carries a constant-size `summary` (total_keywords, total_etv, volume_bands and rank_bands histograms, and vs_previous new/lost/improved/declined/same counts) that always reflects the FULL keyword set, while `keywords` returns only the top rows ranked by `sort` (etv default | volume | rank; default limit 10 per domain, max 100) with a `truncated` block (shown/matching_total/lost_total). rank is a POSITION: smaller is better, so a NEGATIVE rank_delta means the competitor's ranking IMPROVED (change ∈ new/improved/declined/same/unknown). Keywords the competitor ranked for before but lost are disclosed in `lost_keywords` (top 10 by previous etv), never dropped silently. Pass `domain` to focus one competitor, `min_volume` to drop low-volume keywords. When the site has NO completed research yet the response is { researched:false } with a `guidance` string explaining a research must be triggered from the dashboard first — this tool cannot start one. site_id is OPTIONAL when OAuth-authenticated. This is the external competitor lens (third-party SERP data); for YOUR OWN search performance use get_keyword_performance, and for your content playbook use get_content_actions.
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Matching MCP Servers

  • A
    license
    A
    quality
    D
    maintenance
    Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp
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    MIT

Matching MCP Connectors

  • Hepburn romanize Japanese names (katakana/hiragana) in passport style. Supports family-first and given-first order. 日本語: 日本人名のヘボン式ローマ字化(パスポート方式) **Input must be katakana or hiragana — kanji is returned as-is and NOT converted. To get readings of kanji names, call kanji.toKana first.**
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  • List this account's company memory, one line per entry (name + description), newest first. Traverse index-first: scan this, then memory_recall(name) for full bodies. Memory accrues automatically from your competitor scans (exhaust) and from your own memory_note writes.
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  • [free] Describe this connector: flagship-first tools layer (search/answer as the front door), how to install (Claude Code / Cursor / npm), free vs paid tiers, and discovery URLs. Call this first.
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  • Bots that bought in the first block or first seconds of this token's launch, and whether they are connected to the deployer. The window is the first 120 seconds after the first transaction; at most 30 rows are returned and totalEarlyBuyers carries the full count. Migration-crank MEV bots are excluded and named rather than silently dropped. Check reachedGenesis: when false the walk did not reach the true first transaction, the result is partial, and it is cached for only 10 minutes instead of 2 hours. Use it to tell an organic launch from a pre-arranged one. Not for coordinated multi-wallet launch buying (get_launch_bundles) or the deployer's funding of other wallets (get_wallet_links). Arguments: address and chain; one API unit on any chain.
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  • Hepburn romanize Japanese names (katakana/hiragana) in passport style. Supports family-first and given-first order. 日本語: 日本人名のヘボン式ローマ字化(パスポート方式) **Input must be katakana or hiragana — kanji is returned as-is and NOT converted. To get readings of kanji names, call kanji.toKana first.** [Torify namespace — official]
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  • Pay Fieldproof $42. Returns every live rail: Stripe card_uri, EIP-681 usdc_uri, BIP-21 btc_uri, x402 POST /v1/sponsor, Zelle, and GET /v1/invoice. One settlement meets the first-$42 bar.
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  • Get first-half / first-five / first-set lines and live progress for an NFL, NCAAF, NBA, NCAAB, MLB, soccer, or tennis event. Joins persisted first_half_spreads / first_half_totals mains to this-event period scores (1H = Q1+Q2, NCAAB or soccer native 1H; MLB F5 = innings 1–5; tennis S1 = first-set games) and grades the period, not the full game. GET /odds / get_odds stay on moneyline/spread/total. Returns available:false with no charge if no period mains have been ingested. Other sports return HTTP 400.
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  • ⚠️ CRITICAL: request this tool first before creating any code! Returns essential slice creation rules. READ THIS FIRST before creating any slices! Contains the MOST IMPORTANT naming rule: ALWAYS use SINGULAR names (user/ not users/).
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  • List the product categories that have a published tier list, newest research first. Call this first when you do not already know the exact category slug: slugs cannot be guessed reliably and every other tool takes one.
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  • Use this FIRST whenever the user asks how a product, competitor or domain grew, got traffic, or found its first users — a report may already exist and reading it is free. Returns matching report slugs for read_report. Searches by domain or name fragment.
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  • List the organizations the signed-in user belongs to (personal org first).
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