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io.github.SupplyGraphAI/supplygraph
serveur MCP (Model Context Protocol) · SupplyGraph AI MCP Server 1.0.0
Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur.
Est-ce que io.github.SupplyGraphAI/supplygraph est sûr ?
Au dernier audit MCP Hub, le serveur MCP « io.github.SupplyGraphAI/supplygraph » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.
Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.
Les 9 outils
- corporate_exception_report≈ 1393 tokens
Enterprise Change Report Pricing: {"unit": "credits", "options": [{"chapter_name": "License", "per_run": 1518}, {"chapter_name": "GovRel", "per_run": 1419}, {"chapter_name": "R&D", "per_run": 1749}, {"chapter_name": "Reputation", "per_run": 8481}, {"chapter_name": "Ops", "per_run": 1749}, {"chapter_name": "GeneralRisks", "per_run": 1617}, {"chapter_name": "Profile", "per_run": 1749}, {"chapter_name": "Cost", "per_run": 1386}, {"chapter_name": "Compliance", "per_run": 1419}, {"chapter_name": "Competition", "per_run": 1452}, {"chapter_name": "Brand", "per_run": 3168}, {"chapter_name": "HR", "per_run": 1419}, {"chapter_name": "ALL", "per_run": 26400}]}
Paramètres, retour & exemple (2)
Paramètrespid· string · requis
Internal company ID for the target enterprise, obtained from the search_company_candidates MCP tool (e.g. a77828f060c866441f2403384b271e63 for Tesla, Inc.).chapter_name· string · optionnel · ALL | Profile | License | GovRel | R&D | Reputation | Ops | GeneralRisks
Exception domain chapter to generate. Omit or use ALL for the full corporate exception report.
Exemple d'appel{"name": "corporate_exception_report", "arguments": {"pid": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - due_diligence_report≈ 2197 tokens
Generates comprehensive supplier due diligence reports using global enterprise data, benchmarking ownership, legal, and financial risk for compliance and procurement decisions. Pricing: {"unit": "credits", "options": [{"chapter_name": "Company Registration Information", "per_run": 1518}, {"chapter_name": "Branch Offices", "per_run": 1650}, {"chapter_name": "Corporate Brand Initiatives", "per_run": 2079}, {"chapter_name": "Administrative Sanctions", "per_run": 1485}, {"chapter_name": "Software Copyright Details", "per_run": 1551}, {"chapter_name": "Outbound Investments", "per_run": 1650}, {"chapter_name": "Financing Activities", "per_run": 1452}, {"chapter_name": "Competitor Analysis", "per_run": 1485}, {"chapter_name": "Subsidiary Companies", "per_run": 1848}, {"chapter_name": "Trademark Portfolio", "per_run": 4059}, {"chapter_name": "Patent Holdings", "per_run": 12045}, {"chapter_name": "Website Registrations", "per_run": 1419}, {"chapter_name": "Court Judgments", "per_run": 1584}, {"chapter_name": "Shareholder Structure", "per_run": 1386}, {"chapter_name": "Senior Management Team", "per_run": 1452}, {"chapter_name": "Administrative Permits", "per_run": 1749}, {"chapter_name": "Court Hearing Notices", "per_run": 3696}, {"chapter_name": "Court Notices", "per_run": 3597}, {"chapter_name": "Equity Pledges", "per_run": 1419}, {"chapter_name": "Mobile Applications", "per_run": 1386}, {"chapter_name": "Copyrighted Works", "per_run": 1749}, {"chapter_name": "Equity Freezes", "per_run": 1419}, {"chapter_name": "Chattel Mortgages", "per_run": 1419}, {"chapter_name": "WeChat Official Accounts", "per_run": 1386}, {"chapter_name": "Tendering and Bidding Activities", "per_run": 2145}, {"chapter_name": "Qualification Certificates", "per_run": 2310}, {"chapter_name": "Engineering Irregularities", "per_run": 1419}, {"chapter_name": "Major Regulatory Violations", "per_run": 1452}, {"chapter_name": "Compensation and Benefits", "per_run": 1386}, {"chapter_name": "Enforcement Targets", "per_run": 1452}, {"chapter_name": "Supplier Network", "per_run": 1584}, {"chapter_name": "Credit Ratings", "per_run": 1419}, {"chapter_name": "Tax Offenses", "per_run": 1419}, {"chapter_name": "Regulatory Spot Checks", "per_run": 1485}, {"chapter_name": "Import-Export Credit Records", "per_run": 1386}, {"chapter_name": "Regulatory Actions", "per_run": 1617}, {"chapter_name": "Granted Government Subsidies", "per_run": 1551}, {"chapter_name": "Eligible Government Subsidies", "per_run": 1584}, {"chapter_name": "Consolidated Statements of Operations", "per_run": 1749}, {"chapter_name": "Income Statement", "per_run": 1947}, {"chapter_name": "Statement of Cash Flows", "per_run": 2442}, {"chapter_name": "Consolidated Balance Sheets", "per_run": 2145}, {"chapter_name": "ALL", "per_run": 26400}]}
Paramètres, retour & exemple (2)
Paramètrespid· string · requis
Internal company ID for the target enterprise, obtained from the search_company_candidates MCP tool (e.g. a77828f060c866441f2403384b271e63 for Tesla, Inc.).chapter_name· string · optionnel · ALL | Company Registration Information | Branch Offices | Corporate Brand Initiatives | Administrative Sanctions | Software Copyright Details | Outbound Investments | Financing Activities
Report chapter to generate. Omit or use ALL for the full due diligence report.
Exemple d'appel{"name": "due_diligence_report", "arguments": {"pid": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - search_company_candidates≈ 594 tokens
Search company candidates by company name, optionally filtered by country or region, and return possible matching records with mapped company IDs for caller-side selection. Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 1}
Paramètres, retour & exemple (1)
Paramètrestext· string · requis
Company name with optional country or region. The input should contain a company name, and may optionally include its country or region (e.g. 'Tesla United States', 'Samsung South Korea', 'Huawei China').
Exemple d'appel{"name": "search_company_candidates", "arguments": {"text": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - search_region_candidates≈ 561 tokens
Resolves natural-language country or region names (including aliases and abbreviations) against SupplyGraph’s internal geography registry and returns a list of standardized region names for downstream agent and MCP tool consumption. Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 1}
Paramètres, retour & exemple (1)
Paramètrestext· string · requis
Country or region name in natural language, including aliases and abbreviations (e.g. 'China', 'USA', 'South Korea', 'Hong Kong', '中国', '美国'). The tool searches the internal geography registry and returns standardized matching region names for caller-side selection.
Exemple d'appel{"name": "search_region_candidates", "arguments": {"text": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - sg_chokepoint≈ 671 tokens
Analyzes multi-tier supply chains to detect single-country concentration and quantify geographic dependency across regions. Pricing: {"unit": "credits", "per_run": 264590}
Paramètres, retour & exemple (2)
Paramètrespid· string · requis
Internal company ID for the target enterprise, obtained from the search_company_candidates MCP tool (e.g. a77828f060c866441f2403384b271e63 for Tesla, Inc.).region_name· string · requis
Standardized country or region name for geographic concentration analysis, obtained from the search_region_candidates MCP tool (e.g. China, United States, Hong Kong).
Exemple d'appel{"name": "sg_chokepoint", "arguments": {"pid": "texte", "region_name": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - sg_visualization≈ 1093 tokens
Generates global multi-tier supply-chain graphs providing full visibility into enterprise and product dependencies. Pricing: {"unit": "credits", "per_run": 264590}
Paramètres, retour & exemple (1)
Paramètrespid· string · requis
Internal company ID for the target enterprise, obtained from the search_company_candidates MCP tool (e.g. a77828f060c866441f2403384b271e63 for Tesla, Inc.).
Exemple d'appel{"name": "sg_visualization", "arguments": {"pid": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - supply_chain_risk_prediction≈ 1305 tokens
Continuously monitors global supply chain risk events and evaluates whether, how, and to what extent those events may affect a target company. Pricing: {"unit": "credits", "per_run": 264590}
Paramètres, retour & exemple (1)
Paramètresevent_info· object · requis
Evaluates how a supply chain risk event may affect a target company through multi-tier supply chain propagation analysis.
Exemple d'appel{"name": "supply_chain_risk_prediction", "arguments": {"event_info": {}}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - tariff_calc≈ 542 tokens
Calculates U.S. customs duties by combining HTS base rates with applicable Chapter 99 measures, providing transparent, rule-based tariff outcomes. Pricing: {"unit": "credits", "per_run": 10}
Paramètres, retour & exemple (2)
Paramètrescountry_or_region· string · requis
Country or region of origin for the imported product, e.g. China, Mexico, European Union.product_description· string · requis
Description of the product to import into the United States, e.g. HS code, product name, material, or specifications.
Exemple d'appel{"name": "tariff_calc", "arguments": {"country_or_region": "texte", "product_description": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]} - tariff_classification≈ 616 tokens
Classifies products into correct HTS codes from text or documents, automating tariff lookup and ensuring customs compliance in real time. Pricing: {"unit": "credits", "per_run": 2}
Paramètres, retour & exemple (1)
Paramètresproduct_description· string · requis
Description of the product used to identify HS/HTS codes.
Exemple d'appel{"name": "tariff_classification", "arguments": {"product_description": "texte"}}Exemple de retour{"content": [{"type": "text", "text": "…"}]}
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