Fodda Brand Intelligence MCP Server

Check if market data gathering is complete and retrieve the results. Call this after get_supplemental_context — poll every 5-10 seconds until status is COMPLETE or FAILED. (Async supplemental query status poll.)

Federated empirical data layer: queries 110+ live institutional and government feeds (FRED, BLS, US Census, World Bank, OECD, WTO, IMF PortWatch, etc.) for real-time macroeconomic indicators, demographic shifts, global trade flows, container shipping & port throughput, maritime chokepoints (Suez, Panama), and retail spending. When citing US retail sales, cite official Headline Retail & Food Services (NAICS 44X72 / FRED RSAFS, ~\$738B) as the primary retail figure; clearly distinguish from Retail Trade Excl. Food Services (NAICS 44000 / FRED RSXFS, ~\$646B). When citing consumer spending, report live Monthly Personal Consumption Expenditures (BEA Table T20805 / FRED PCE, ~\$22.3T through August) with MoM and YoY growth rates rather than quarterly aggregates. Use when verifying macroeconomic benchmarks, checking consumer spending metrics, grounding supply chain or trend hypotheses with empirical statistics, or when knowledge graph coverage is thin. Sibling routing: Use search_graph for curated consumer trends and brand case studies; use get_supplemental_context when you need authoritative government statistics, port/logistics telemetry, or regional/country economic context. Returns a structured 5-bucket evidence bundle (demand_signals, economic_context, market_data, financial_reporting, demographic_context) with full source provenance.

Check the current user's account status: API call balance, plan, enabled/disabled graphs, and profile info. Use when the user asks "how many API calls do I have?", "what plan am I on?", "what graphs can I access?", or similar account questions. Returns live data — not cached from session start. (User account status & credit balance lookup.)

Use when auditing a single brand's 360-degree intelligence footprint: evaluates brand health, trend footprint, competitor co-occurrences, Google Trends velocity, and Wikipedia pageviews across 100+ knowledge graphs. Mandatory entry point whenever a prompt names a specific commercial brand or company (e.g., 'Audit Nike's market footprint', 'How is Lululemon performing across consumer trends?'). Sibling routing: Use search_graph for topic- or category-level discovery (e.g. 'athleisure trends'); use get_company_earnings for detailed SEC financial statements and earnings Q&A; use brand_tracker when focusing on a specific brand's multi-graph presence, peer competitor matrix, and cultural momentum. Returns a comprehensive brand dossier containing trend_footprint, co_occurring_brands (competitor matrix), search_interest metrics, evidence_velocity, and optional pre-rendered interactive widget_html.

Returns Fodda's capabilities, semantic research workflows, Human Agents (Human Agents), and pricing. Call this for any question about what Fodda can do, how experts work, available research recipes, or pricing. Sibling routing: Use get_capabilities to discover platform features and workflow recipes; use list_graphs to list specific data graphs; use list_analysts to discover expert personas. Returns platform overview, active specialist counts, workflow descriptions, and published offerings.

Evidence citation lookup: retrieves primary evidence articles, publisher case studies, data points, and source URLs that validate a specific trend node. Prerequisite: Requires a valid for_node_id and graphId obtained from a prior search_graph or get_neighbors call. Use when backing a specific trend with original reporting, verifying publisher citations, or extracting concrete brand examples. Sibling routing: Use search_graph first to discover trends and retrieve their node_id; use get_evidence to inspect the underlying source citations for that node; use get_node for high-level metadata; use search_statistics for standalone numbers. Returns an array of evidence items with title, publication, publication_date, snippet, and source_url.

Create a presentation-ready data visualization from research findings. Available chart types: "cultural_shifts" (From→To transitions), "competitive_compass" (brands on 2 axes), "trend_constellation" (network of related trends), "implication_ladder" (Signal→Trend→So What→Do What), "innovation_pathway" (Now→Near-Term→Future), "opportunity_map" (2×2 white space analysis). Returns a branded SVG that renders directly in the chat. (Data payload transformation + SVG chart rendering.)

Use when searching for market trends, category dynamics, and consumer shifts across 100+ curated knowledge graphs. Returns trends with cited evidence, lifecycle stage (emerging/building/mature), and structured next_moves. Sibling routing: Use search_graph as the default starting tool for broad trend discovery; use search_statistics if you specifically need numerical data points and growth rates; use search_insights if you only need qualitative quotes and expert commentary; use brand_tracker if querying a specific company or brand name.

Catalog and discovery layer for Fodda knowledge graphs: lists all accessible knowledge graph IDs, topic vertical tags, author credentials, node counts, and signal metrics. Use first when discovering available domain libraries or when you need a valid graphId parameter for graph-scoped queries (e.g. "What knowledge graphs do you have on retail or sustainability?"). Sibling routing: Use list_graphs to inspect graph metadata and available sources; use search_graph to query trend content across graphs; use list_analysts to discover specialist Human Agents. Returns an array of graph profiles with graphId, name, topics, signal_count, and author.

List unique taxonomy entity values (brands, locations, technologies, audiences) or retrieve the complete table of contents for small, discrete report graphs (<40 trends, e.g. DHL, PwC, or Mintel radars) where search may return partial results. Returns up to 100 unique values ordered alphabetically. Sibling routing: DO NOT use this tool for thematic or topic research (e.g. 'find AI trends' or 'what trends are in tech?') on large domain graphs (tech, retail, cpg), because it caps at 100 items alphabetically without evidence or signals; use search_graph instead for semantic trend retrieval. (Graph taxonomy label value lookup.)

Discover what's connected to a specific trend — related brands, technologies, locations, and cross-domain links. Returns curated editorial connections between trends. Use after search_graph to map the territory around a trend, find which brands are connected, or understand cross-domain relationships. Requires node_id from a prior search_graph result. (1-hop graph relationship traversal from seed node in Neo4j.)

Get the full profile of a specific trend — detailed description, lifecycle stage (emerging/building/mature), signal strength, geographic scope, and all properties. Use when you need deeper detail on a single trend after search_graph returned a summary. Requires node_id from a prior search_graph result. (Direct Neo4j node metadata lookup by ID.)

Extract clean text content from any URL. Use this when a user shares a link (competitor site, news article, client brief, trend report) and wants to cross-reference it against Fodda knowledge graphs. Returns structured text ready for analysis.