"Understanding RAG (Retrieval-Augmented Generation or related topics)" matching MCP tools:

• ask_intelligence_ragA

  Tresslers Group Intelligence MatrixB

  Ask a natural-language question and receive structured intelligence context retrieved from Tresslers Group dossiers via RAG (Retrieval Augmented Generation). Returns relevant document chunks, source citations, conviction metadata, and graph neighborhood data. The calling LLM should synthesize the returned context into a coherent answer.

  ConnectorNo auth

• ask_intelligence_ragA

  Tresslers Intelligence MCP ServerA

  Ask a natural-language question and receive structured intelligence context retrieved from Tresslers Group dossiers via RAG (Retrieval Augmented Generation). Returns relevant document chunks, source citations, conviction metadata, and graph neighborhood data. The calling LLM should synthesize the returned context into a coherent answer.

  ConnectorNo auth

• build_rag_promptA

  IA-QA — 152 QA & Dev Tools for AI AgentsB

  Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, system instruction injection, and source attribution.

  ConnectorNo auth

• build_rag_promptA

  IA-QA Eval & RAG — 38 toolsA

  Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, system instruction injection, and source attribution.

  ConnectorNo auth

• rag_pipeline_planA

  Moretes Architect MCPB

  Plan a retrieval-augmented generation pipeline from corpus facts: chunking, embedding, indexing, authorization, evaluation and cost drivers, with open questions. No vendor is chosen for you.

  ConnectorNo auth

• rag_queryC

  Mazza Group AI APIsC

  Retrieval-augmented generation pipeline. Price: $1.50/call (x402, USDC on Base).

  Connector

  Destructive

  No auth

• get_contextA

  Vilix AIA

  Load the last few recent messages and semantically related past turns. Call this before you compose your reply. Pass the user's latest prompt only. Vilix runs relevance retrieval internally. For anything more specific you may also call the optional search_semantic / search_keyword / recent_messages tools (e.g. when the user names a source like ChatGPT). Every retrieved item carries an ISO timestamp — when two items disagree about a changing fact (a plan, a status, a decision), the NEWEST timestamp is the latest known value. Related turns may include `context_before`/`context_after` (the adjacent turns) and the payload may include `also_related`: additional nearby matches as compact snippets, newest first — check it before concluding a fact is unknown or unchanged. `chat_id` — pass null (or omit) on a brand-new conversation to also receive `user_rules`, `system_behavior`, `active_projects`, and `active_project_state`. Pass the chat_id returned by a prior `save_turn` to skip those — they are already in the chat's own context from turn 1 and re-injecting wastes tokens. `recent_messages` and `related_conversations` are always returned (they're the cross-tool memory bridge). `attachment_context` (optional, default "") — if the user's CURRENT message has an attachment (file, image, code paste, screenshot OCR), pass a short plain-text summary of it here so retrieval can match on the attachment topic in addition to the bare prompt. Pass the SAME summary to `save_turn` for this turn. Empty = no attachment, ignored.

  ConnectorOAuth

• fotoai_generateA

  Postemi: Social Media SchedulerA

  Generate an image or a video with FotoAI (fotoai.hu), paid with the user's OWN FotoAI credits. Call fotoai_quote first, tell the user the price, and pass its quote_id here only after the user agrees. Returns the generation id. The finished outputs are saved into this Postemi workspace's media library automatically. With wait_seconds (up to 50) the call waits for a short image and returns media paths directly; otherwise call fotoai_get_generation with the id until status is completed. Attach each media PATH to create_post. Pass an idempotencyKey (a UUID you invent per generation). If a call times out, repeat it with the SAME key: you get the same generation back and nothing is charged twice.

  ConnectorOAuth

• cti_get_guidelinesA

  website-searchA

  Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.

  ConnectorNo auth

• brand_guideline_statusA

  Colour MemoryA

  Resolves a brand_job_ref returned by brand_guideline_specify when its race-to-complete window elapsed before generation finished. Read-only, in-process lookup -- never re-runs generation. Returns {"status": "processing"} if still running, {"status": "complete", "brand_ref": ..., "project_id": ..., "recommended_candidate_id": ..., "candidate_count": ...} once done (a compact summary -- use the returned brand_ref with brand_guideline_select/brand_guideline_pdf/brand_guideline_claims for full detail, the same pattern every other Brand Standard tool already uses), or {"status": "failed", "error_code": ..., "message": ...} if generation genuinely failed server-side. An unknown or expired brand_job_ref returns a structured BRAND_JOB_NOT_FOUND error, never a crash or empty success.

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• brand_guideline_statusA

  Colour MemoryA

  Resolves a brand_job_ref returned by brand_guideline_specify when its race-to-complete window elapsed before generation finished. Read-only, in-process lookup -- never re-runs generation. Returns {"status": "processing"} if still running, {"status": "complete", "brand_ref": ..., "project_id": ..., "recommended_candidate_id": ..., "candidate_count": ...} once done (a compact summary -- use the returned brand_ref with brand_guideline_select/brand_guideline_pdf/brand_guideline_claims for full detail, the same pattern every other Brand Standard tool already uses), or {"status": "failed", "error_code": ..., "message": ...} if generation genuinely failed server-side. An unknown or expired brand_job_ref returns a structured BRAND_JOB_NOT_FOUND error, never a crash or empty success.

  ConnectorNo auth

• brand_guideline_statusA

  Colour MemoryA

  Resolves a brand_job_ref returned by brand_guideline_specify when its race-to-complete window elapsed before generation finished. Read-only, in-process lookup -- never re-runs generation. Returns {"status": "processing"} if still running, {"status": "complete", "brand_ref": ..., "project_id": ..., "recommended_candidate_id": ..., "candidate_count": ...} once done (a compact summary -- use the returned brand_ref with brand_guideline_select/brand_guideline_pdf/brand_guideline_claims for full detail, the same pattern every other Brand Standard tool already uses), or {"status": "failed", "error_code": ..., "message": ...} if generation genuinely failed server-side. An unknown or expired brand_job_ref returns a structured BRAND_JOB_NOT_FOUND error, never a crash or empty success.

  ConnectorNo auth

• getApiJobA

  Ludo AI Game AssetsA

  Poll the status of a generation job started by any tool. Every generation tool returns `{id, status}`; call getApiJob with that id until status is `succeeded` (then read `result`, shaped exactly like the tool's documented output) or `failed` (read `error`). Poll every few seconds, or pass `wait: 30` to long-poll - the call is held up to 30 seconds (max 60) and returns as soon as the job finishes.

  ConnectorNo auth

• get_utility_service_statusA

  Ardaro Agent Runtime UtilitiesA

  Check the selected service's current runtime readiness, input limits, pricing and retention before use. Returns service and details; ready=false means the underlying service is unavailable. Free cached runtime read; no external probe, payment, analysis or historical document retrieval. Use get_utility_example for a fixed integration sample instead.

  ConnectorNo auth

• get_graphA

  Enhanciar: AI company brainA

  Return the workspace's knowledge graph: topics, links and clusters. Use it to draw or reason about the overall shape of the brain (which topics are hubs, how areas connect), not to read content: for page text use ``get_page``, for code dependencies use ``impact``. It takes no arguments and can be large. Returns ``{nodes, links, communities, stats}``, the graph the app's Map shows, with pages you cannot open left out, or ``{error}`` when no graph has been built for this workspace yet. It costs no AI tokens.

  ConnectorAPI key

• siteborne_retrieve_verified_web_contextA

  SITEBORNE Utility NetworkA

  Retrieve and verify evidence from one public web URL using direct HTTP retrieval. Use when: the request is specifically about the content and provenance of a URL. Do not use when: evidence must be synthesized for a company (use siteborne_build_company_evidence_graph), extracted from an authorized document (use siteborne_extract_document_evidence_json), checked against an existing output contract (use siteborne_verify_agent_output), or the page requires JavaScript rendering (rendered retrieval is not available). Parameters: retrieval_mode must be direct; rendered is defined but unavailable and is rejected before any payment challenge, never substituted with direct retrieval. output_mode structured needs buyer_schema and field_selectors to be meaningful; redirect_policy and max_redirects bound redirect following; max_content_size bounds returned bytes; maximum_authorized_price only constrains payment and never changes retrieval scope. Behavior: open-world: a paid request performs bounded outbound HTTP retrieval of the target URL through a safe-egress boundary (private and loopback destinations are refused) and persists governed payment, audit, job and Workflow state; an unpaid request returns payment_required; a repeated identical request with the same payment is replay-protected and is not charged twice. Retrieval admission follows the governed runtime rate policy; no fixed public per-origin rate is promised. This service is currently production-disabled and rejects execution. Economics: exact price of $0.008 USD per request in direct mode; the payment challenge requires that amount and the charge settles at exactly that amount. rendered mode has a governed price of $0.029 USD but is not available: it is rejected before any payment challenge and never substituted. Failure: schema-invalid input and unavailable modes are rejected before any payment challenge; target-site errors, timeouts and unavailable retrieval return a structured MCP error and are never answered with a substitute result. Returns: normalized, source-attributed web context with verification and PCC evidence.

  ConnectorNo auth

• asset_generate_3dA

  SceneplaneA

  External generation, not a catalog search: start only when the user has chosen or already authorized this generation path. Do not spend provider credits to discover candidate assets. Start a text/image → 3D generation of ONE freestanding object per task — build ground planes and assemblies from scene operations, and reuse a generated model by duplicating it with scene ops. provider=hyper3d (Rodin) takes prompt only; provider=hunyuan3d and provider=tripo3d take prompt OR imageUrl. Each provider needs its integrations_set token. Poll asset_generate_status, then import with asset_library_import source=<provider> slug=<taskUuid>.

  ConnectorAPI key

• hourly_intensityA

  Emission FactorsA

  Get hourly grid carbon intensity (kg CO2e per kWh) for any US ZIP code, derived from EIA-930 hourly fuel-mix data. Data lags up to about a day (not real-time). Returns time series with per-hour fuel mix, total generation, and carbon intensity. For timing and analysis (demand response, load shifting, grid patterns), not inventory reporting: it uses fixed per-fuel combustion factors on in-BA generation (no imports), so its values don't match eGRID's annual factors. For live or forecast intensity, WattTime or Electricity Maps are better options.

  ConnectorNo auth

• awareness_search_knowledgeA

  Awareness — Memory, Knowledge & Agent MarketsA

  Search the PUBLIC Awareness knowledge graph — 1,000+ sourced scientific papers. Use this when the user (especially a researcher or a research agent) asks for peer-reviewed findings: "latest evidence on X", "highly-cited papers about Y", "what does the literature say about Z". This is NOT a memory tool — it does not read the user's private memory, and it needs no per-user API key, so it works even when Awareness memory is not configured (the MCP connection itself still needs its own token). Coverage: condensed-matter, quantum-information, materials, machine-learning, renewable-energy, chemistry, biology, medicine, neuroscience, computer-science (2022-2026, cited >= 50), plus a business supply/demand domain. What comes back (anonymous preview mode): title, snippet (preview), confidence, source_ref (DOI), citation_count, domain/topic, and a detail page URL. Full content is NOT in the preview — tell the user it is paid retrieval via POST /api/v1/public/retrieve with an API key (see https://awareness.market/en/knowledge for the KaaS product). Args: query: Natural-language research question or keywords (2-1000 chars). domain: "science" (default) or "business". topic: Optional sub-topic slug, e.g. "medicine", "condensed-matter", "supply-chain". Empty = search all topics in the domain. top_k: Max results, 1-20 (default 5; anonymous callers are capped at 3). include_graph: When true, also return same-topic related items. Returns: {"results": [{id, title, snippet, confidence, source_ref, citation_count, domain, topic, url}], "related": [...], "total": int, "mode": str} `mode` is "preview" (anonymous) or "full" (authenticated).

  ConnectorNo auth

• search_lacunaA

  Lacuna Research MCPA

  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

• query_capacity_factor_v1A

  exascale.buildA

  Query verified U.S. capacity factor — how hard a fleet actually runs — by joining EIA-860M capacity and EIA-923 generation. Requires `data_month`: one ISO month start, e.g. "2026-01-01". If the user names no month, ask which one (or state the month you chose); if a month is not covered, the error lists the months that are — do not retry blindly. capacity_factor = net generation (MWh) / (operating nameplate capacity (MW) × hours in the month), computed over plant×fuel present in BOTH sources, so scope is auto-aligned. Optional `group_by` of `state` and/or `fuel_group`, and `state`/`fuel_group` filters. Returns the capacity factor per group with its generation and capacity, a `coverage` declaration (what share of in-scope capacity/generation matched), and a citation to BOTH the capacity and the generation source row. Basis is nameplate; storage is excluded; the capacity snapshot is matched to the month. Does not determine per-generator capacity factor, a net-summer/winter basis, or months absent from either source.

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