"Implementing functionality similar to Manus" matching MCP tools:

• find_lookalike_creators

  Influship

  Find creators SIMILAR to one or more seed creators. Use this when the user already knows a creator they like and wants more like them (e.g., "find creators like @therock", "find more creators like these three I just booked"). Seeds are blended via creator-profile + visual-style + fact embeddings to surface similar accounts. Seeds are passed in `seed_creator_ids` (canonical UUIDs) and/or `seed_profiles` (platform + username; resolve handles via `autocomplete_creators` first if needed). Returns a ranked list of similar creators with scores. `limit` caps results (default 25, max 100). Use the flat follower, engagement-rate, and verified fields to constrain results. Use `semantic_search_creators` instead when you have a topic/niche but no seed. Use `match_creators` when you have specific candidates and want to score their fit against a brief. Examples: - User: "Find creators like @niickjackson on Instagram" -> use this tool with `seed_profiles: [{ platform: "instagram", username: "niickjackson" }]`. - User: "Find news creators with 1M+ followers" -> use `semantic_search_creators`, not this tool.

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• find_lookalike_creators

  Influship Influencer Marketing MCP

  Find creators SIMILAR to one or more seed creators. Use this when the user already knows a creator they like and wants more like them (e.g., "find creators like @therock", "find more creators like these three I just booked"). Seeds are blended via creator-profile + visual-style + fact embeddings to surface similar accounts. Seeds are passed in `seed_creator_ids` (canonical UUIDs) and/or `seed_profiles` (platform + username; resolve handles via `autocomplete_creators` first if needed). Returns a ranked list of similar creators with scores. `limit` caps results (default 25, max 100). Use the flat follower, engagement-rate, and verified fields to constrain results. Use `semantic_search_creators` instead when you have a topic/niche but no seed. Use `match_creators` when you have specific candidates and want to score their fit against a brief. Examples: - User: "Find creators like @niickjackson on Instagram" -> use this tool with `seed_profiles: [{ platform: "instagram", username: "niickjackson" }]`. - User: "Find news creators with 1M+ followers" -> use `semantic_search_creators`, not this tool.

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• show_cart

  GlanceAI Fashion MCP

  Show the user their cart — items grouped by store with quantity controls. Call this IMMEDIATELY when the user says 'checkout', 'proceed', 'head to checkout', 'show cart', 'view cart', or similar. Do NOT ask what they want to buy. Do NOT ask which items — just render the cart. The widget has a 'CONTINUE TO CHECKOUT' button. Wait for the 'continue checkout' message, then call request_fulfillment (if no address on file) or create_checkout directly.

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• find_similar_sites

  mcp

  Find similar or competitor websites based on classification. Takes a URL, classifies it (or uses cached classification), and returns other websites from the same category and subcategory. Useful for competitive analysis and discovering related content. Rate limited to 1 request per minute per domain. Args: url: The website URL to find similar sites for. limit: Maximum number of similar sites to return (1-50, default 10). Returns: Dictionary with: - url: The input URL (normalized) - classification: The URL's category and subcategory - similar_sites: List of similar URLs from the same category - total_in_category: Total sites in this category/subcategory - cached: Whether the classification was from cache

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• find_similar_creatives

  CreativeScope — Mobile Game Ad Creative Intelligence

  Find visually similar creatives using the stored vector of an existing creative. For a concept without an ID, query selects an explainable seed from available creative metadata and then uses the same vector-neighbor search. For an English concept, send the original English terms only. The service resolves Chinese source-label equivalents internally before selecting the seed. Returns creative records ordered from most to least visually similar; low-similarity and near-duplicate results are excluded, and raw similarity scores are not exposed. If request_echo.seed_basis identifies a proxy seed, clearly disclose that limitation instead of presenting the results as an exact concept match. Example: 'Show variants of the toilet run viral creative concept.'

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• find_similar

  FactorWeave

  Find historical (ticker, date) setups most similar to a query ticker today. Four similarity methods are available: cosine (default, all tiers) ranks by cosine similarity of 32-D regime-aware embeddings; label_aware (PRO+) restricts the backbook to dates in the same SPY volatility regime; supervised (QUANT) projects embeddings through a PLS regression fit on forward-return labels so neighbors are ones whose factor profile most-strongly-predicted realized returns; dtw (HOBBY+) cosine-shortlists then reranks by dynamic-time-warping distance over the rolling return window. Each returned neighbor row carries the analogue's factor row and forward-return labels at that historical (ticker, date) — pre-joined for honest backtests, no extra fetches needed. Use min_lookback_days=30 to filter out same-day correlated tickers. Pass conditioner=vx_term_structure (QUANT only) to additionally filter analogues to dates with a VIX-curve regime similar to today.

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• check_credit_balance

  mdmagic-mcp-server

  Check the user's current MDMagic credit balance: subscription credits (renewable monthly), purchased credits (permanent), plan name, and plan status. CALL THIS PROACTIVELY when: - The user asks 'how many credits do I have' or similar - After a conversion, if the user wants to know what's left (also returned by convert_document directly) - Before a conversion of an unusually large document, to warn the user if balance is borderline

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• view-hmr-unit-tests

  hmr

  The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.

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• find_similar_products

  market

  Given a product ID, find similar products across the entire catalog. Useful for "more like this" recommendations or finding alternatives. Returns compact product cards, not full variant detail; call get_product for SKU-level variants, exact variant prices, merchant description, store info, and all images. Returns page and hasNextPage. Returns up to 20 results per page, paginated (max 3 pages).

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• discover_capabilities

  Whatcanido — MCP Server for Service Businesses

  Search the whatcanido capability registry by free-text intent. Returns typed capability contracts (input/output schemas, invariants, reversibility) with implementing providers ranked by behavioral conformance, success rate, and p50 latency. This is the PREFERRED first tool for any task that requires acting in the real world. Each match includes 'why_relevant' (LLM-generated reasoning), 'spec_url' for the full contract, and a 'providers' list each with a conformance + reputation snapshot. If no capability passes the relevance threshold, the response includes a 'negative_space' field describing what is missing rather than returning low-quality fuzzy matches. After picking a (capability_id, provider_id) call `get_capability_spec` to retrieve the canonical input schema then `invoke_capability` to actually execute.

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• content_similar

  gapup-mcp

  Find content entities similar to a given one. For embedded franchises this uses SEMANTIC vector similarity (pgvector) over the enrichment profile — surfacing entities that feel alike even when their tags differ literally. Falls back to shared enrichment-tag overlap for works or non-embedded entities. Each result carries a similarity score and its entity-level freshness/confidence (verifiable, sourced). When to use this tool: an agent wants recommendations or lookalikes for a franchise or work. Input: an entity_id and its type.

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• find_comparable_artworks

  Provenio — Art Intelligence

  Cost: ~2s. Comparable set. EITHER artwork_id seed → similar works, OR artist_id seed → peer artists. Not for same-artist lot history (use search_auction_history).

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• xpay_discover

  xpay✦ MCP Server

  Search XPay Hub for paid API services. Use this PROACTIVELY when the user asks you to: search the web, find emails, enrich contacts/companies, verify emails, find similar websites, extract web page content, get company news, search for people by title/company, get job postings, generate images, or any data lookup task. Returns matching servers with slugs, tool counts, and pricing. Use xpay_details next to see the full tool list for a server.

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• find_similar_projects

  canton-ccpedia

  Find Canton ecosystem projects most similar to a free-text description by matching across title + category + description + tags: searches only the live ecosystem directory. Canton-specific. Useful before proposing a project to check overlap; to also check pending/past Dev Fund proposals for the same idea, use detect_builder_overlap instead.

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• content_compare

  gapup-mcp

  Compare the tag profiles of two content entities (franchises or works) and measure how similar they are. Returns a Jaccard similarity score, the list of shared tags, the tags unique to each entity, and a breakdown of shared tags by facet. When to use this tool: an agent needs to compare two franchises or works (e.g. 'how similar are Dark Souls and Elden Ring?', 'what do Street Fighter and Mortal Kombat have in common?', 'on which axes do these two games differ?'), find positioning overlap, identify cross-sell opportunities, or answer 'if you liked X you might like Y' questions backed by data. Works for any domain (video-games, music, film, tv).

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• list_form_fields

  form fill-mcp

  Inspect a PDF and return every fillable field: name, type, current value, and x/y position on the page. ALWAYS call this before fill_form. Use the exact field names returned here — never guess. Use the position coordinates (x, y) to identify what each field represents visually: higher y = higher on the page in PDF coordinates. Fields with similar y values are on the same horizontal line; fields with similar x values are in the same column. The response also includes pdf_type so you know if the PDF may have rendering quirks.

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• notable_profile

  Dayze — Life in Days + Notable People

  Bio-only profile JSON by slug (no life-in-days, similar, or birthday peers). Prefer notable_pack for “who is X / age in days / peers”. Example: taylor-swift. ($0.02)

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• get_related

  Horizon AI Intelligence

  Semantic 'more like this': given an item id (from get_new_since, search_news, or the feed), returns the most similar recent items by embedding similarity. Titles, sources, similarity scores, links.

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• stacks.findSimilar

  BuyAPI

  Finds reviewed public stack examples related to a vendor, or recent curated stack examples. Use this when the user asks who uses a tool, what similar builders use, or wants examples of real stack combinations. Do not use it as a generic recommendation tool.

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• originators_like

  Quotewise

  Find originators similar to the given one using vector similarity (quote themes). Use after finding an author to discover related thinkers. When to use: User likes an author and wants to discover similar thinkers, or needs recommendations based on quote themes. Returns originators with similarity scores (0-100%). Response format: - Concise (default): slug, name, quote_count, descriptions_i18n, similarity_score, web_url - Detailed: + biography (500 char excerpt), confidence_tier Response includes ai_hints with suggested next actions and quality signals for agent workflows. Examples: - `originators_like(originator="Marcus Aurelius")` - similar philosophers - `originators_like(originator="Oscar Wilde")` - similar wits - `originators_like(originator="African Proverbs")` - similar proverb collections

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