"Semantic search, RAG, and memory systems" matching MCP tools:

• search_semantic

  Tripitaka MCP (hosted)

  Semantic search — match by meaning, not exact words. Uses vector similarity (cosine distance) over `text_pali` embedded with a multilingual MiniLM model. 🤔 **In most cases you should use `search_hybrid` instead** — it combines this semantic search with keyword search and ranks better. Use this tool only when you need: - Pure semantic results (no keyword influence) - Fine-grained `threshold` tuning (hybrid uses RRF which is harder to tune) - To debug what semantic alone picks up vs keyword ⚠️ Known limitations: - The index is **Pāli only** (English/Thai queries pass through the multilingual embedding but the model isn't tuned on Pāli) - English queries usually embed better than Thai (model is EN-primary) - For specific Pāli terms (`appamāda`, `dukkha`), exact match is better — use `search_by_keyword` instead - Pāli stock phrases recur in many suttas → similarity scores cluster; read the top 10, don't trust rank 1 alone

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• knowledge_query

  dialogbrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

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• create_collection

  api

  Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (search_collection) or question answering (ask_collection). NOTE: Collections start empty. Add evidence bundles with add_document_to_collection. Indexing is async — once complete, use search_collection or ask_collection. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

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• search_patterns

  saferagenticai-mcp

  Field-weighted keyword search across the framework. Substring match on lowercased terms; field weights: title 10x, summary 4x, SFR text 3x, description 2x, pattern body 1x. `matched_in` reports the highest-weighted field that matched. No semantic / embedding search — known limitation, see /mcp.html. Use verbosity='compact' to drop snippets and confidence flags (~70% smaller payload) when triaging.

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• fetch

  Penfield Memory

  [ChatGPT Connector compat] Fetch memory by ID. Exists to satisfy ChatGPT Deep Research's required `search`/`fetch` tool contract. Native MCP clients should fetch via `recall` + memory_id, or use the API's GET /memories/{id} endpoint directly. Returns a single memory with citation support (id, title, url, text fields). Args: id: Memory UUID to fetch ctx: MCP context Returns: Dict with id, title, url, text, metadata fields

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• openalex_search_entities

  openalex-mcp-server

  Search, filter, sort, or retrieve by ID. Covers all OpenAlex entity types (works, authors, sources, institutions, topics, keywords, publishers, funders). Pass `id` to retrieve a single entity. Otherwise, use `query` and/or `filters` for discovery. Supports keyword search with boolean operators, exact phrase matching, and AI semantic search. Use openalex_resolve_name to resolve names to IDs before filtering. Searches and ID lookups return a curated set of fields by default; pass `select` to override with specific fields, or `["*"]` for the full record.

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• x711_hive_read

  x711

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

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• x711_hive_read

  x711 — Universal Agent Gas Station

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

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• x711_vault_query

  x711

  Semantic vector search across your private vault. Returns ranked memories by cosine similarity × confidence × importance. Recalls the most relevant facts, insights, and skills your agent has accumulated. FREE always. Requires API key (reads your vault only — other agents cannot access it).

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• search

  SocialGPT

  Search across your own connected-account content and return the best matches. Each result has an `id` (pass it to `fetch` for the full item), a `title`, a `url`, and a `text` snippet. This is the deep-research "search" entrypoint the ChatGPT/Claude connectors call by convention; for semantic search over analyzed videos specifically use `search_videos`. Returns {"results": [...]}; when you have no connected accounts it returns reason="no_connected_accounts" plus a connect_url instead of results.

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• semantic_search

  Zoning Signal

  Semantic search across the full corpus — every place dossier, corridor signal, meeting reading, and named-pattern brief. Returns results ranked by cosine similarity in a 1024-dimensional embedding space (Voyage AI 4 + Supabase pgvector). Use when the agent does not know the canonical entity slug or named-pattern title in advance — the search returns the readings whose semantic structure best matches the natural-language query, with type, title, similarity, and resolved URL per hit. Threshold 0.55, top 12.

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• knowledge_query

  DialogBrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

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• find_equivalent

  Medical Terminologies MCP

  Search for equivalent terms across multiple medical terminologies. Use this tool to: - Find the same concept in different coding systems - Compare how terminologies represent a concept - Support terminology mapping and data integration Searches across: ICD-11, SNOMED CT, LOINC, RxNorm, and MeSH. Set `target_terminologies` to limit which are searched, or set `source_terminology` to exclude one (e.g. when you already have a code from that terminology and want equivalents elsewhere). The two combine: source is subtracted from targets.

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• search_rentals_natural

  arcasos-rentals

  PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.

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• append_memory

  space0

  Persist one event to this agent's memory stream. For kind=chat, ALWAYS pass `speaker` (the in-world player name behind the line) - flattening "grassguy: i am here" into event_text causes the agent to parrot the speaker as itself on the next tick. Server-side will embed `text` via Workers AI so the memory is reachable by `search_memories` semantic retrieval. Observation/action memories auto-anchor to your current space and last-looked subject by default once you have entered a space; pass space + subjectPosition only to override the anchor precisely. Reflection/chat stay unanchored.

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• search

  gateway

  Unified colony search in ONE call: your own + public/shared MEMORY (hybrid semantic + keyword — C1-private, never another agent's private data) AND the public WALL feed. Pass handle+secret to include your private memory; omit them for public-only. Returns per-source results plus a merged ranked list, each item tagged with `source` and `acl_status`. This is 'search your past and your colony'.

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• iliad_vector_database

  AXIS Toolbox — Agentic Commerce Codebase Intelligence

  AXIS-owned vector store. Two operations: `upsert` (insert or replace vectors) and `query` (cosine top-k nearest neighbors). Namespaces are account-scoped server-side (`acct:<account_id>:<namespace>`), so tenants cannot read each other's vectors. Persistent across restarts via Postgres. Requires Authorization: Bearer <api_key>. Best for RAG retrievers, deduplication, and similarity search. Engineer mode (X-Agent-Mode: engineer — Managed Memory, $0.05): query runs a pgvector/HNSW ANN candidate pool with optional recency-decay reranking (recency_half_life_days — managed forgetting), RRF hybrid fusion (sparse_ids), and metadata filter; upsert applies intra-batch semantic-dedup (dedup_threshold).

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• browse_apis

  Proxygate

  Search the Proxygate marketplace for APIs an agent can buy. Optional free-text query (semantic + keyword search, same as the public catalog), category filter, and result limit. Returns each API with its listing id, name, service slug (for call_api), category, buyer price per request (in micro-USDC), listing type, trust/rating hints, and a capped endpoint preview (endpoint_count = full total). No authentication required.

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• tourradar_vertex-tour-title-search

  MolTravel

  [tourradar] Search for tours by title using AI-powered semantic search. Returns a list of matching tour IDs and titles. Use this when you need to look up a tour by name. When you know tour id, use b2b-tour-details tool to display details about specific tour

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• search_knowledge

  Name Whisper — ENS Intelligence Layer

  Search the ENS knowledge base — governance proposals, protocol documentation, developer insights, blog posts, forum discussions, and Farcaster casts from key ENS figures (Vitalik, Nick Johnson, etc.). Powered by semantic search over curated ENS sources. USE THIS (don't answer from memory) for any "how does X work" / "what is X" / "why does ENS …" PROTOCOL-MECHANICS question — renewal, the grace period, the premium/temporary-premium auction, registration & commit-reveal, resolvers, subnames, the NameWrapper & fuses, reverse resolution, ENSv2 — plus ENS history, DAO/governance proposals, community sentiment, and "what did <person> say about <topic>". Mechanics questions feel answerable from general knowledge, but a sourced, citable answer is the bar here — search first, then cite what you find. Do NOT use this for name valuations, market data, availability, or a specific name's live status — use the other tools for those.

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