"Understanding the Purpose of Research" matching MCP tools:

• track_order_statusA

  ARADIA | sovereign agentic systemsA

  [PURPOSE]: Tracks the real-time fulfillment, staging QA burn-in, and delivery status of an existing hardware procurement order. [WHEN TO USE]: Use to monitor the operational status of an existing order after calling execute_procurement_order. [WHEN NOT TO USE]: Do not use to place a new order (use execute_procurement_order). [SIDE EFFECTS]: None (pure read-only query against staging records).

  ConnectorNo auth

• search_webA

  Salesbot - B2B outreach LinkedIn MCP Server with CRM and emailA

  Use for general web research (company info, news, job portals). For LinkedIn profiles use search_linkedin_people; to read one company's own site use read_company_website. General-purpose Google search — returns organic results for any query. Unlike search_google_xray (LinkedIn-only), this searches the entire web. Useful for finding job postings on portals (jobs.cz, prace.cz, profesia.sk, indeed.com), company info, news, or any other web content. Results are NOT saved to contacts — use this for research and discovery. Capped at 4 calls per minute to protect the Serper/Google budget.

  ConnectorAPI key

• search_toolsA

  Crank ProtocolB

  Keyword search over the full Crank tool catalog (progressive disclosure). Read-only, free, never gated. Purpose-built for a discovering agent that does not want to load the full tool catalogue into context just to find the one it needs ( R1 -- the Solana Agent Kit V1 failure mode: 60+ tools in context caused tool-selection hallucinations). Pass a keyword or short phrase (e.g. "perp funding rate", "stake sol", "referral"); matches rank by keyword overlap against each tool's name + description. Returns up to ``limit`` (default 10, max 50) results, each ``{name, purpose, toolset}`` -- ``purpose`` is a one-line summary (call the tool itself, or get_trading_workflow, for the full picture), and ``toolset`` names which of the ten named toolsets (market_data, spot, perps, lending_staking, portfolio, strategies, intelligence, rewards_growth, wallet_admin, support) the tool belongs to. No tool is renamed or hidden by this search -- it is purely a discovery aid.

  ConnectorNo auth

• list_pagesA

  California Justice WatchA

  Return the canonical list of pages on cajusticewatch.com — slug, URL, label, and purpose. Use this when the user asks about features/pages/tools of the site, OR when you need to recommend a page, OR before saying "I do not have access to X" — the page may actually exist.

  ConnectorNo auth

• list_storesA

  Market Category BoosterA

  List the marketplaces whose taxonomies are loaded, as `store_id` values. These are the only accepted values for the `store_id` argument of `categorize_product`. Free of charge on purpose: without it an agent would have to pay for a failed call to discover them.

  ConnectorNo auth

• explain_decisionA

  OneQAZ Trading IntelligenceA

  Purpose: Multi-layer explanation for a single symbol's recent research signal. Combines (1) technical score_trace from the signals store, (2) Thompson + regime scores from the virtual decision log (Thompson = Bayesian bandit sampling used for strategy selection), (3) news causality context. Use this when an AI must present a structured "why" rather than a raw verdict. Triggers (casual questions too): "why is BTC bullish?", "왜 이 종목이 매수야?", "explain that signal", "판단 근거 설명해줘", "walk me through the reasoning". When to call: when the user asks "why is this signal bullish/bearish?". Prerequisites: identify the symbol via get_signals or get_latest_decisions first. Next steps: none (this completes the explanation chain). Caveats: `symbol` must match the per-symbol signal store filename (lowercase). Output is research evidence, NOT a buy or sell recommendation.

  ConnectorNo auth

• enrich_providerA

  apis-ioB

  One call, pick your field groups — resolves a slug OR any identifier and returns exactly the groups you ask for, instead of chaining get_provider + get_provider_rating + get_provider_artifacts + get_provider_onboarding. Groups: profile, onboarding, artifacts, rating, insights. Understanding plan — the base groups moved with the rest of the discovery layer on 2026-08-31. Priced B2 (cross-catalog synthesis) — $0.05 per call under pay-as-you-go; included in Understanding and Influence. See apis://prices.

  ConnectorNo auth

• search_research_postsA

  GlobalSource Partners MCP ServerA

  Search published research posts associated with one or more research entities. This tool requires research entity UUIDs. If only entity names are known, first call search_research_entities to obtain candidate research entities and their UUIDs. Use only entity_uuid values copied exactly from search_research_entities results. Never infer, guess, construct, or transform UUIDs from entity names. Use this tool only after selecting the most appropriate research entity from the search_research_entities results. Examples: - To find research about "United States", first call search_research_entities with "United States", review the returned matches, select the most appropriate entity, and then call this tool with the selected entity_uuid. - To find recent research about multiple entities, pass all selected entity_uuid values in research_entity_uuids. - To retrieve the next page of results, pass the previous response's next_cursor as cursor. Results can be filtered by publication date and retrieved using cursor-based pagination.

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• insights_industriesA

  apis-ioB

  UNDERSTANDING — the demand-side industry rollup: the sectors the profiled companies fall into. Each row carries TWO counts and they answer different questions: `company_count` is the research roster, `profiled_count` is how many of those you can actually read back via find_company_insights(industry:) — size a cohort on profiled_count. The counterpart to find_industries, which counts SUPPLY (providers publishing APIs into a vertical) — this counts DEMAND (companies buying into it). Comparing the two is how you find a sector with buyers and no sellers. Priced B2 (cross-catalog synthesis) — $0.05 per call under pay-as-you-go; included in Understanding and Influence. See apis://prices.

  ConnectorNo auth

• get_strategy_leaderboardA

  OneQAZ Trading IntelligenceA

  Purpose: Top RL-learned research strategies — GLOBAL pool + per-symbol partition. Layer E evidence (Layer E = strategy-performance tier of the 5-layer trust pyramid). The GLOBAL pool may include synthesized win_rate values, so per_symbol_leaderboard is the primary measured-edge surface for trust auditing. Triggers (casual questions too): "what are the best strategies?", "제일 잘 버는 전략 뭐야?", "top strategies?", "전략 순위 보여줘", "which strategy has the best win rate?". When to call: final trust-validation step. Prerequisites: none. Next steps: market://{market_id}/signals/summary for live signals. Caveats: `min_trades` filter enforces statistical validity. Strategies are paper-tested, not real-money executed.

  ConnectorNo auth

• get_trident_specA

  tridentA

  Returns the complete Trident 2D specification including grammar, syntax rules, coordinate system, containers, nodes, connections, shapes, and icon reference. Use this when you need deep understanding of the Trident DSL.

  ConnectorNo auth

• list_categories_api_agents_categories_getA

  Agentry — The Trust Layer for the Agent EconomyB

  List Categories List all agent categories with counts. Returns every category in the directory along with the number of agents in each. Useful for building category filters or understanding the directory's coverage areas. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json [ { "category": "Category", "count": 1 } ] ```

  ConnectorNo auth

• get_mission_statsA

  mcp-serverA

  Get aggregate statistics about missions on the HomeVisto platform. Returns total counts, status breakdown, and average bounty information. Useful for understanding platform activity.

  ConnectorNo auth

• get_knowledge_statsA

  mcp-klever-vmA

  Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.

  ConnectorNo auth

• get_research_noteA

  SearchShop AIA

  Get the summary and URL of a specific SearchShopAI Research Note by its slug (returned by search_research_notes).

  ConnectorNo auth

• vector_quantizeA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.

  ConnectorNo auth

• get_research_noteA

  La Luer — AI Skincare CommerceA

  Get the summary and URL of a specific SearchShopAI Research Note by its slug (returned by search_research_notes).

  ConnectorNo auth

• billing.statusA

  Admin SubstituteA

  Check subscription status, plan details, billing cycle, and feature access. Useful for understanding what the business can and cannot do on their current plan.

  ConnectorNo auth

• gbif_get_species_classificationA

  gbif-biodiversity-mcp-serverA

  Return the parent chain for a taxon — from kingdom (or domain) down to the immediate parent of the queried taxon — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → immediate parent of the queried taxon); the queried taxon itself is not included — call gbif_get_species for its own record. Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.

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• get_research_noteA

  Apostle — AI Men's Skincare CommerceA

  Get the summary and URL of a specific SearchShopAI Research Note by its slug (returned by search_research_notes).

  ConnectorNo auth

• recommend_settingsA

  Proving LabA

  The capture settings that fit a given purpose, each with the measurement behind it or an explicit note that none exists. Pass purpose as citation, figure, archive or ocr. Use before capturing: the file that comes out is between 8.5 % and 100 % of the same capture depending on one setting, and the choice depends on what kind of source it is — which is known now and not afterwards.

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