World Monitor
Real-time global intelligence on conflict, markets, energy, trade, shipping and cyber. 102 tools.
Description from the maker (original English)
Read-only MCP server for wafergraph.com's semiconductor & AI supply-chain data: 30 tools, no auth.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
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Passed 4/5 checks · What you ask goes through the maker’s server (wafergraph-mcp.jwpalm99.workers.dev)
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Works with Claude · about 2 min
No key, nothing to install · Set it up once on claude.ai or the desktop app, then use it in chat · free accounts can add 1
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+ConnectorsWafergraph MCP
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Works with ChatGPT on business plans only
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Works with Claude Code · about 2 min
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claude mcp add --transport http wafergraph https://wafergraph-mcp.jwpalm99.workers.dev/mcpYou should see Wafergraph MCP listed as connected
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Works with Cursor · about 2 min
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Works with VS Code · about 2 min
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Other apps that use MCP, such as Windsurf, Zed or Gemini CLI, each add servers differently. See how in the app list.
Address for apps that connect over the web
https://wafergraph-mcp.jwpalm99.workers.dev/mcpJSON settings are under “For developers” below
Claude Code
claude mcp add --transport http wafergraph https://wafergraph-mcp.jwpalm99.workers.dev/mcpCursor (~/.cursor/mcp.json)
{
"mcpServers": {
"wafergraph": {
"url": "https://wafergraph-mcp.jwpalm99.workers.dev/mcp"
}
}
}VS Code (mcp.json)
{
"servers": {
"wafergraph": {
"type": "http",
"url": "https://wafergraph-mcp.jwpalm99.workers.dev/mcp"
}
}
}These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.
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ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast
Tool names and descriptions exactly as the server reported them on Nov 10, 2026. The maker wrote them, not us; we show them as plain text.
search_companiesSearch wafergraph's semiconductor & AI supply-chain company dataset (615 companies across 12 segments) by name/one_liner substring and/or segment and/or country. Returns a compact list capped at 25 with a total match count. Use get_segments first if you don't know valid segment i
get_companyFull allowed profile for one company (by id or exact name) plus its supplier/customer supply-chain edges. Includes key_products (short list of named products/lines). Fields are deliberately limited to established/trust-checked data (see README field-discipline note).
get_segmentsThe wafergraph taxonomy: 12 top-level supply-chain segments (materials through ai_datacenter) and their subsegments, each with a live company count, plus the market_position enum. Use this to discover valid `segment` values for search_companies/get_deals. Segment definitions are
get_supply_chainWalk the supplier/customer graph from one focal company, up to 2 tiers up (suppliers), down (customers), or both. Mirrors the chain view on wafergraph.com's Explorer. Returns companies grouped by tier plus the edges between them.
get_dealsSearch wafergraph's semiconductor & AI supply-chain M&A corpus (74 acquisitions/mergers, including notable terminated attempts) by title/summary substring and/or segment. Returns a compact list capped at 30 with a total match count.
compare_companiesSide-by-side comparison of 2-6 companies on the same fields, plus their shared and unique supply-chain counterparties. Cheaper and more aligned than several get_company calls when the question is comparative.
get_country_exposureGeographic concentration of the supply chain: which countries host the companies in a given segment (or across all 12 segments), ranked by company count. Answers 'how concentrated in Taiwan is advanced lithography' style questions. Country is recorded for all 615 companies.
find_chokepointsRank supply-chain chokepoints: companies many others depend on, weighted by how concentrated their market position is. A chokepoint here means high downstream dependency plus monopoly/leader position, i.e. few substitutes. Scoring is a transparent heuristic over the public datase
analyze_portfolio_exposureGiven a list of tickers or company ids, report that basket's aggregate exposure across supply-chain segments and countries, and flag where holdings share the same upstream suppliers (correlated single points of failure). Informational supply-chain analysis over public data, not i
filter_companiesStructured multi-criteria screen over all 615 companies: exact segment/subsegment/country/market_position/public filters plus a market-cap range, sortable and paginated. Use this instead of search_companies when the question is a precise filter ('leader-position analog companies
list_subsegmentsEvery subsegment across wafergraph's 12-segment taxonomy, each with its live company count and parent segment id/name, optionally filtered to one segment. Use this (or get_segments) to discover valid `subsegment` values before calling get_subsegment or filter_companies. Segment/s
get_subsegmentAll companies in one segment+subsegment pair, as compact refs sorted by market cap descending, plus a market_position breakdown and a country breakdown computed over the FULL matching set (not just the returned page). Use list_subsegments first if you don't know valid segment/sub
find_similar_companiesNearest structural neighbours to one focal company, ranked by a transparent Jaccard-similarity score — not a market or competitive judgment. Use search_companies or resolve_ticker first if you only have a ticker or an approximate name, then pass the resolved id here.
rank_by_market_capTop N companies by market cap, optionally restricted to a segment/country/market_position, with the priced-coverage ratio for that scope attached — about 28% of companies dataset-wide have no market_cap_usd_b on file, so a plain top-N list without the coverage number would look m
resolve_tickerBatch-resolve up to 25 strings — tickers, company names, or ids, in any mix — to canonical company refs. Call this FIRST whenever you have raw user input (a ticker list, pasted names) and need valid ids before calling other tools; unresolved entries come back with up to 3 suggest
list_countriesEvery country in wafergraph's semiconductor & AI supply-chain dataset (29 countries across 615 companies) with company count, which segments are present there (with counts), public/private split, and priced market-cap totals. Sorted by company count descending. Optional segment f
get_country_profileDeep profile of one country's presence in wafergraph's semiconductor & AI supply-chain dataset: company count, segment breakdown, market-position breakdown, top companies by market cap, notable monopoly/leader companies, and inbound/outbound supplier-relationship edge counts acro
compare_countriesSide-by-side comparison of 2-5 countries: aligned rows for company count, segment mix, market-position mix, and priced market cap, plus which segments each country is uniquely present in or dominant in, and which segments they all share. country is the company's HEADQUARTERS coun
get_segment_leadersWho runs a given layer of the semiconductor & AI supply chain: the companies at monopoly/leader market position in one taxonomy segment (or all 12 if none given), with country and market cap, plus a count of how many companies sit at each position (monopoly/leader/major/challenge
get_upstream_concentrationFor one focal company: break its suppliers down by headquarters country and by segment, report an HHI concentration index (0 = spread evenly, 1 = fully concentrated in one bucket) for each dimension, and name the single most concentrated one. Always reports supplier_edge_coverage
find_paths_betweenEvery documented supply path between two companies, following supplier->customer edges (e.g. 'how does NVIDIA actually depend on Shin-Etsu'). Searches up to max_depth hops in one or both directions and returns each path as an ordered list of companies, shortest first. Capped for
simulate_disruptionRemove one company, every company in one country, or every company in one segment from the documented supply graph and report the blast radius: which companies lose a documented supplier, how many alternative suppliers they retain in the same subsegment, and which are left with z
find_single_source_dependenciesScreen for (customer, subsegment) pairs where the customer has exactly ONE documented supplier in that subsegment — the highest-value documented-concentration risk screen in the dataset. Optionally scoped to customers in one segment or country. Ranked by the sole supplier's downs
rank_by_connectivityRank companies by documented supply-chain degree: customer count (downstream reach), supplier count (upstream dependence), or total. CRITICAL: degree measures how well a relationship is DOCUMENTED in this curated dataset, not how critical the company actually is — a well-covered
find_common_suppliersThe shared-upstream question over a set of companies: given 2-15 company ids/tickers, or a segment id (uses every company in that segment), rank suppliers by how many of the input companies they documentedly serve (e.g. 'serves 9 of 12'), with each supplier's market position and
get_dealFull record for one M&A deal by id: title, type, value, announced date, status, all parties with their resolved company refs where a dataset id exists (and the raw party name where it does not), summary, sources, and the per-deal confidence flag. Use get_deals or find_deals_by_co
find_deals_by_companyEvery M&A deal a company took part in, split by role (as acquirer, as target, or other). Matches by dataset id first, then falls back to case-insensitive name matching — necessary because a deal's target is frequently not itself a company in this dataset and carries a null id (e.
get_ma_activity_summaryAggregate view of the full 74-deal M&A corpus: counts by year (from announced date), by deal type, and by status; total and median disclosed value; and the largest deals by value. Value figures are computed only over the subset of deals with a disclosed value_usd and are never ex
find_consolidation_hotspotsRanks taxonomy segments by M&A activity by mapping each deal's parties onto their companies' segments (matching by id, then falling back to case-insensitive name), then aggregating deal count and disclosed value per segment. Deals whose parties cannot be resolved to any dataset c
get_dataset_statsThe honesty tool: what this dataset actually contains and where it is thin. Live-computed per-field coverage for companies and deals, last_verified staleness distribution, supply-chain edge coverage, data source mode, and a plain-words list of known limitations. Call this before
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 12, 2026
The registry name was verified against the domain wafergraph.com (the registry checks domain ownership)
Code last changed 2 months ago
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It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.
The GitHub repo last changed 2 months ago, and the latest version (1.2.0) reached the registry 4 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT Business and above.
In the same category: World Monitor, Claude Flow, Tldraw
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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Real-time global intelligence on conflict, markets, energy, trade, shipping and cyber. 102 tools.
AI orchestration with hive-mind swarms, neural networks, and 87 MCP tools for enterprise dev.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
A remote MCP server exposing wafergraph.com's semiconductor & AI supply-chain dataset — 565 companies across 12 segments, the supplier/customer graph, and a 74-deal M&A corpus — as 30 read-only tools any MCP-speaking AI agent can call directly.
No auth, no cost, read-only. Streamable HTTP transport at /mcp. Human landing page at /.
Live: https://mcp.wafergraph.com
| Tool | Purpose |
|---|---|
search_companies({query?, segment?, country?}) | Name/description search with segment & country filters. Compact list, capped at 25, with a total match count. |
get_company({id}) | Full allowed profile for one company (by id or name) plus its supplier/customer edges. |
get_segments() | The 12-segment taxonomy (+ subsegments, market_position enum) with live company counts. |
get_supply_chain({id, direction, depth}) | Walk the supplier/customer graph from a focal company up to 2 tiers up ("up"), down ("down"), or "both". |
get_deals({query?, segment?}) | Search the M&A deal corpus. Compact list, capped at 30, with a total match count. |
compare_companies({ids}) | Side-by-side comparison of 2-6 companies on aligned fields, plus their shared and unique supply-chain counterparties. |
get_country_exposure({segment?}) | Geographic concentration: which countries host a segment's companies, ranked by count, with disclosed market-cap coverage. |
find_chokepoints({segment?, limit?}) | Rank chokepoints by downstream dependency weighted by market position. Transparent heuristic, not a proprietary risk model. |
analyze_portfolio_exposure({holdings}) | Map tickers or ids to segment/country exposure and flag suppliers that several holdings share. Not investment advice. |
| Tool | Purpose |
|---|---|
filter_companies({...}) | Structured multi-criteria screen (segment, subsegment, country, position, public, market-cap band, has_ticker) with sorting and pagination. |
list_subsegments({segment?}) | Every subsegment with its live company count and parent segment. |
get_subsegment({segment, subsegment}) | All companies in one subsegment with position and country breakdowns. |
find_similar_companies({id}) | Nearest structural neighbours by Jaccard similarity over segment tags and shared supply-chain counterparties. |
rank_by_market_cap({segment?, country?, limit?}) | Largest companies by disclosed market cap, always with its coverage ratio. |
resolve_ticker({inputs}) | Batch-resolve up to 25 tickers, names, or ids to canonical companies, with suggestions on a miss. |
| Tool | Purpose |
|---|---|
list_countries({segment?}) | Every country with company counts, segment mix, and public/private split. |
get_country_profile({country}) | One country in depth, including how many supply relationships cross its border in each direction. |
compare_countries({countries}) | 2-5 countries side by side, with uniquely-present and dominant segments. |
get_segment_leaders({segment?}) | Who occupies the monopoly and leader positions in a segment. |
get_upstream_concentration({id}) | One company's supplier mix by country and segment with an HHI index and edge-coverage context. |
| Tool | Purpose | ||
|---|---|---|---|
find_paths_between({from, to, max_depth?}) | Documented supply paths between two companies, shortest first. | ||
| `simulate_disruption({company_id? | country? | segment?})` | Blast radius if a company, country, or segment goes offline, ranking those left with no documented alternative. |
find_single_source_dependencies({segment?, country?}) | Customer/subsegment pairs served by exactly one documented supplier. | ||
rank_by_connectivity({metric?, limit?}) | Rank by documented degree. Measures documentation density, not real-world criticality. | ||
| `find_common_suppliers({ids? | segment?})` | Suppliers shared across an arbitrary set of companies, with the share of the set each serves. |
| Tool | Purpose |
|---|---|
get_deal({id}) | One M&A deal in full, with parties resolved to companies where possible. |
find_deals_by_company({id}) | Every deal a company took part in, split by role, with the match method exposed. |
get_ma_activity_summary() | Deal counts and disclosed values by year, type, and status. |
find_consolidation_hotspots() | Which segments are consolidating, with unresolvable deals counted rather than dropped. |
get_dataset_stats() | What this dataset contains, how fresh it is, and where it is thin. Call it to learn what the data cannot answer. |
Every response includes:
data — the payload.attribution — compiled-by/sources block (SEC, Wikidata, Wikipedia, GLEIF) plus a company_url back to the full sourced profile on wafergraph.com.links — { report, newsletter } pointing back to wafergraph.com and its hand-scored Vendor Exposure Review.Company records include key_products as of 2026-07-19. That field in wafergraph's own dataset was originally bulk-drafted with a known fabrication/mis-scope history and was withheld here until a verified deep-fill landed upstream; it now has (confirmed live) 564/565 companies filled, with only sk_enpulse empty (defunct, absorbed into SKC 2025-12-23). Every field shipped here — name, ticker, market_cap_usd_b, segments, one_liner, market_position, key_products, key_customers/key_suppliers graph edges, deals — is established, trust-checked data. Fields are still whitelisted (not blacklisted) in src/types.ts (toAllowedCompany), so anything added upstream later stays excluded by default until deliberately added.
Counters only, never identities. No IPs, no query contents or tool arguments, no user identifiers, no cookies, no PII. Specifically: how many times each tool was called per day, how many distinct sessions opened per day, and which client *software* connected (the clientInfo name and version your MCP client already sends in the initialize handshake, e.g. claude-code@2.1.0).
Sessions are not users — one person reconnecting counts several times. Nothing recorded here can identify who you are.
claude mcp add --transport http wafergraph https://mcp.wafergraph.com/mcp
Settings → Connectors → Add custom connector → paste:
https://mcp.wafergraph.com/mcp
{
"mcpServers": { "wafergraph": { "url": "https://mcp.wafergraph.com/mcp" } } }
Cloudflare Worker, TypeScript, built on the agents package's McpAgent (Durable-Object-backed Streamable HTTP MCP server — free on the Workers Free plan; SQLite storage backend). See CLAUDE.md for the file map and the data-source-mode decision (hybrid live-fetch + one vendored snapshot, discovered by E2E-testing the upstream endpoints rather than assumed).
npm install
npm run dev # wrangler dev, local npm run typecheck # tsc --noEmit npm run deploy # wrangler deploy
See ATTRIBUTION.md for the full source/license breakdown (SEC EDGAR public domain, Wikidata CC0, Wikipedia CC BY-SA 4.0 — attribution required, GLEIF). Short version: free to use, credit wafergraph.com, not financial advice.
This is an independent project built against wafergraph.com's public dataset; it is not an official wafergraph product.