"Connecting Alpaca to Brokerages, News Sites, and Spark Cluster for Workflow Rebalancing" matching MCP tools:

• query_registry

  Arclan MCP Registry

  Search the Arclan registry for MCP servers. By default returns only connectable servers (active, mcp_partial, auth_gated). Use status=stdio to browse local-only servers available for installation. Use status=all to query the full index. Use production_safe=true to restrict to servers with uptime > 97% and handshake success > 95%. Use read_only=true to restrict to servers with no write or exec tools. Use this before connecting to an MCP server to check its validation status and score. After using a server, call report_server to contribute reliability data.

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• score_facility

  DC Hub — Data Center Intelligence MCP Server

  Use when a user wants an independent 0-100 grade for ONE existing facility across 7 dimensions — power, fiber, water, climate_risk, tax_environment, talent_pool, expansion. Example: "How does the CoreWeave Las Vegas site score, power-weighted?" — score_facility facility_id=<id> weighting=power_priority. Params: facility_id or name (required); weighting one of "balanced" (default) | "power_priority" | "risk_priority" | "expansion_priority". Returns: composite 0-100, tier_classification, peer comparison, and per-dimension detail. Do NOT use for a raw lat/lon parcel (use analyze_site), to compare 2 or more sites (use compare_sites), or to find similar sites (use find_alternatives).

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• courtlistener_get_opinion

  courtlistener-mcp-server

  Fetch the full text and metadata for a single opinion cluster by cluster ID. A cluster groups all opinions filed in a case — majority, concurrence, dissent, and per curiam. Returns all opinion variants with HTML and plain text. Obtain cluster IDs from courtlistener_search_opinions, courtlistener_lookup_citation, or docket results.

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• water_find_sites

  usgs-water-mcp-server

  Find USGS water monitoring sites by bounding box, state, county, or HUC watershed code. Filter by site type (stream gage, groundwater well, lake) and parameter availability. Returns site numbers, names, coordinates, types, and (in expanded mode) drainage area and altitude. Call this first to discover site numbers — water_get_readings, water_get_series, and water_get_conditions all require a site number. To check which parameters or data types a site carries, use the parameterCd or hasDataTypeCd filters. Results are capped at 500 sites; when truncated=true the full upstream count is in upstreamTotal — narrow the query with bbox, countyCd, huc, siteType, parameterCd, or hasDataTypeCd to get all matches.

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• list_sites

  RevenueScope: Japanese EC RPS Benchmarks

  List the sites this caller can analyze, in two groups. my_sites = the sites connected to the signed-in account (each with its display name + domain, so you can match phrases like "the production site" or "revenuescope.jp" without the user pasting a UUID); empty when the caller is not signed in. demo_sites = ready-made sample sites for trying RevenueScope before connecting your own — each is a fictional site with sample data, not a real customer. When signed in (OAuth), prefer my_sites and, if site_id is omitted, default analytics tools to the is_primary=true site. When NOT signed in, my_sites is empty: use a demo_sites site_id and tell the user the numbers come from a sample site, not their own.

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• publish_site

  shiply

  Publish files to the web → live URL at <slug>.shiply.now. UPDATING: never create a new site for changes — re-call with claimToken (anonymous sites) or slug (sites you own with a Bearer key) and the SAME URL gets the new version. Unchanged files are hash-skipped server-side, so re-publishing (including retrying a failed publish) is cheap — always update the same site rather than creating a new one. Works WITHOUT auth (anonymous: 24h lifetime, returns claimToken/claimUrl — SAVE THEM). With a Bearer shp_ key sites are permanent. ≤50 files / 2 MB inline; bigger: REST flow per https://shiply.now/llms.txt. index.html serves at /. spaMode for client-side routing.

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• get_briefed

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Get a personalized market news briefing based on your validated edge library. Profiles your strategies, searches today's news for the instruments and setups you actually trade, and writes a concise digest connecting each headline to your specific book. Each news item includes a ↳ line tying it to your actual positions and edges (e.g. 'your ES momentum setups', 'your GC mean-reversion edge'). Requires at least 5 strong edges in your library. Costs credits.

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• search_clusters

  MRC Data — China's Apparel Supply Chain Infrastructure

  Search Chinese apparel industrial clusters and textile markets. USE WHEN user asks: - "where is China's [denim / suit / women's wear / underwear] manufacturing concentrated" - "what is the largest [silk / cashmere / down jacket] industrial cluster in China" - "industrial cluster comparison Humen vs Shaoxing vs Haining vs Zhili" - "recommend an industrial cluster for sourcing [product]" - "where should I set up a sourcing office for [category]" - "list mega clusters for [category]" - "fabric markets in Zhejiang / Jiangsu" - "accessories / trim / zipper / button markets in China" - "which province dominates [category] exports" - "follow-up: 'tell me more about Humen's cluster scale'" - "服装产业带 / 面料市场 / 产业集群 / 纺织集群 / 辅料市场" - "做 [品类] 应该去哪个产业带 / 集群推荐" Famous clusters this database covers include: Humen (Guangdong, womenswear), Shaoxing Keqiao (Zhejiang, fabric mega-market), Haining (Zhejiang, leather), Zhili (Zhejiang, children's wear), Shengze (Jiangsu, silk), Shantou (Guangdong, underwear), Puning (Guangdong, jeans), Jinjiang (Fujian, sportswear), and more. Returns paginated cluster list with name, location, specialization, scale, supplier count, average rent and labor cost, and key advantages/risks. WORKFLOW: Cluster discovery entry point. search_clusters → compare_clusters (side-by-side up to 10 cluster_ids) OR get_cluster_suppliers (list factories in that cluster) OR analyze_market (broader market view). RETURNS: { has_more: boolean, data: [{ cluster_id, name_cn, name_en, type, province, city, specialization, scale, supplier_count, labor_cost_avg_rmb }] } EXAMPLES: • User: "Where are the biggest denim clusters in China?" → search_clusters({ specialization: "denim", scale: "mega" }) • User: "Show me fabric markets in Zhejiang" → search_clusters({ province: "Zhejiang", type: "fabric_market" }) • User: "童装产业带有哪些" → search_clusters({ specialization: "童装" }) ERRORS & SELF-CORRECTION: • Empty data array → try in order: (1) drop scale filter, (2) broaden specialization (e.g. "服装" instead of "牛仔"), (3) remove type, (4) remove province. • Specialization mismatch → both Chinese and English work. Synonyms: sportswear/运动服, womenswear/女装, underwear/内衣, denim/牛仔. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "No clusters match [criteria]. Try broader specialization or removing filters." AVOID: Do not use this for specific factory search — use search_suppliers. Do not compare clusters by calling search_clusters twice — use compare_clusters with cluster_ids. NOTE: Source: MRC Data (meacheal.ai). 170+ clusters mapped across 31 provinces. 中文：搜索中国服装产业带和面料市场。

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• compare_clusters

  MRC Data — China's Apparel Supply Chain Infrastructure

  Compare multiple Chinese apparel industrial clusters side-by-side on key metrics. PREREQUISITE: You MUST first call search_clusters to obtain valid cluster_ids. Do not guess IDs. USE WHEN user asks: - "compare Humen vs Shishi vs Jinjiang" - "which cluster has lower labor cost — Humen or Dongguan" - "side-by-side: Haining vs Xintang for denim" - "evaluate 3 clusters for my sportswear line" - "对比 [产业带1] 和 [产业带2]" / "哪个集群更适合 [品类]" - "rank these clusters by supplier count" - "which cluster has the highest scale for womenswear" - "follow-up: 'now compare the top 3 clusters you just listed'" Returns full records for each cluster so they can be compared on labor cost, rent, supplier count, scale, specializations, advantages, and risks. WORKFLOW: search_clusters → collect cluster_ids → compare_clusters → optionally get_cluster_suppliers on the winner to list factories in that specific cluster. RETURNS: { count: number, data: [full cluster objects with all fields] } EXAMPLES: • User: "Compare Humen, Shishi, and Jinjiang for sportswear sourcing" → compare_clusters({ cluster_ids: ["humen_women", "shishi_casual", "jinjiang_sportswear"] }) • User: "I want to evaluate Keqiao vs Zhili fabric markets" → compare_clusters({ cluster_ids: ["keqiao_fabric", "zhili_children"] }) • User: "对比虎门、石狮、晋江三个产业带" → compare_clusters({ cluster_ids: ["humen_women", "shishi_casual", "jinjiang_sportswear"] }) ERRORS & SELF-CORRECTION: • "Too many IDs (>10)" → split into batches of 10 and aggregate results in your response. • Fewer results than IDs sent → missing IDs were silently skipped (invalid cluster_id). Re-run search_clusters to verify IDs. • Empty data → all IDs were invalid. Re-run search_clusters and try again with fresh IDs. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call with guessed cluster_ids — always resolve them via search_clusters first. Do not use to list factories in a cluster — use get_cluster_suppliers. Do not compare > 10 clusters in one call. CONSTRAINT: Max 10 cluster IDs per call. NOTE: Source: MRC Data (meacheal.ai). 中文：对比多个产业带的核心指标（最多 10 个）。

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• load_filing_workflow

  Shibui Finance

  Load filing workflow for SEC/EDGAR metadata, 8-K events, 10-K/10-Q reports. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL whenever the user asks about filing dates, filing activity, "who filed", "filed a form", filing frequency, SEC filings, EDGAR, 8-K events, 10-K/10-Q reports, proxy statements, or any query involving the sec_filings table (metadata - when/what type, not transaction detail). For insider transaction detail (shares, prices, cluster buying), use load_insider_workflow instead. Can be combined with other workflow tools.

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• list_saved_sites

  DC Hub — Data Center Intelligence MCP Server

  Use when a user asks to see or review their saved DC Hub shortlist in-chat (FREE with a key). Example: "What sites have I saved?" / "Show my shortlist." — list_saved_sites. Params: none. Returns: an array of saved sites, each with name, market, lat/lon, saved DCPI score, target MW, and notes — the persistent shortlist built by save_site. Do NOT use to add a site (use save_site) or to download the list as a file (use export_dataset); this is the in-chat read-back.

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• build_liquidation_tx

  MEV Intelligence

  Build agent-ready calldata for a liquidation transaction. Returns the target protocol contract address, ABI-encoded calldata, suggested gas limit, value, and chain — everything an agent needs to sign and submit via submit_bundle. Supports Aave V3 (L1 + Base), Spark (L1), and Morpho Blue (L1). Returns calldata only — the agent signs and submits separately. This tool does NOT touch funds and does NOT submit anything; it is a calldata builder.

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• nps_find_parks

  national-parks-mcp-server

  Resolve a place name, US state, or free-text query to National Park Service parks — the required first step before the detail tools. Returns each park's parkCode (the key nps_get_park, nps_get_alerts, nps_find_campgrounds, nps_get_activities, and nps_find_events all use) plus a compact trip-planning summary (designation, states, description, coordinates, headline activities, entrance fee, NPS page). Coverage is US NPS sites only — national parks, monuments, historic sites, seashores — not state parks and not Forest Service or BLM land.

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• google_scholar

  Searchapi

  Search Google Scholar for academic papers, citations, and scholarly articles. Returns results with titles, authors, publication info, citation counts, and links to PDFs. Use cites parameter to find papers citing a specific work, or cluster to find all versions of a paper. For US court opinions and case law, use google_scholar_cases instead.

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• xmp4_guide

  xmp4 — Semantic code knowledge for your stack

  Usage guide for xmp4 tools — read this first to learn the correct workflow

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• principles.list

  AI Design Blueprint Doctrine

  List all 10 Blueprint principles with stable slugs, titles, and clusters. Use this when you need the full inventory or want every principle in one cluster (pass cluster slug to filter). Prefer principles.search when the user describes a topic, failure mode, or keyword in natural language. Prefer principles.get when you already know the exact slug and need full detail.

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• clusters.list

  AI Design Blueprint Doctrine

  List all principle clusters with their stable slugs and linked principle titles. Use this to discover which clusters exist before drilling in with clusters.get or filtering principles.list by cluster. Prefer clusters.get when you already know the cluster slug and need full detail.

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• clusters.get

  AI Design Blueprint

  Get one principle cluster by stable slug. Returns the cluster definition, shared rationale, and the full set of member principles (slug + title) so the caller can pivot into principles.get without a second list call. WHEN TO CALL: the user has already named a specific cluster (e.g. 'delegation', 'visibility', 'trust', 'orchestration') OR you have a slug from a prior clusters.list / principles.list response and need its full definition + member principles. The response embeds member principle slugs + titles already, so DO NOT loop principles.get over each member to get a cluster overview — read the response. WHEN NOT TO CALL: the user is describing a topic, failure mode, or keyword in natural language (call principles.search instead); the user wants to discover which clusters exist (call clusters.list); the user wants the definition of one specific principle (call principles.get directly). Idempotent + cacheable per slug. Returns 404-shaped error_payload on unknown slug — the slug must match exactly the value emitted by clusters.list, with no normalization.

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• load_filing_workflow

  Shibui Finance

  Load filing workflow for SEC/EDGAR metadata, 8-K events, 10-K/10-Q reports. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL whenever the user asks about filing dates, filing activity, "who filed", "filed a form", filing frequency, SEC filings, EDGAR, 8-K events, 10-K/10-Q reports, proxy statements, or any query involving the sec_filings table (metadata - when/what type, not transaction detail). For insider transaction detail (shares, prices, cluster buying), use load_insider_workflow instead. Can be combined with other workflow tools.

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• export_dataset

  DC Hub — Data Center & Power Intelligence

  Use when a user wants to pull their saved DC Hub shortlist OUT of the platform for offline analysis, a spreadsheet, or ingestion into another tool (PRO). Example: "Export my saved sites as GeoJSON for QGIS." — export_dataset format=geojson. Params: format ("csv" default, or "geojson"). Returns: the full file contents as text — CSV rows or a GeoJSON FeatureCollection of your saved sites with DCPI score, target MW, market, coordinates, and notes. Do NOT use to list sites in-chat (use list_saved_sites) or to save a new one (use save_site); this is the bulk-download path.

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