Fodda Earnings Intelligence MCP Server

Surfaces strategic deflection and narrative tension by comparing Wall Street analyst questions against executive responses across quarterly earnings calls. Use when investigating corporate transparency, executive dodge/deflection patterns, or uncovering unaddressed operational risks (e.g., 'Where are retail CEOs deflecting analyst questions on margin pressure?', 'Show analyst-management divergence for hospitality brands'). Sibling routing: Use get_company_earnings for standard single-ticker quarterly records and get_earnings_intelligence for multi-company strategic initiatives; use get_earnings_divergence specifically to analyze tension between analyst scrutiny and executive deflection. Returns clustered divergence themes, verbatim question/response pairs, company deflection counts, and deflection severity metrics. OUTPUT REQUIREMENT: For multi-company thematic findings, present results in a structured evidence table citing: | Company (Ticker) | Reporting Quarter | Speaker (Analyst & Firm / Exec) | Verbatim Quote / Finding |. Clearly distinguish Fodda's synthesized theme labels (e.g. "Pilot Trap") from what analysts or executives actually said on the call.

Cross-company thematic earnings intelligence from the knowledge graph and web sources. Use for multi-company comparisons ("what are hotel companies saying about labor costs?"), industry-level queries, sector filters, or strategic corporate activity categories (activity: "sustainability" | "marketing" | "retail" | "technology"). For corporate ESG, circular economy, and climate initiatives (e.g. "What sustainability commitments did apparel companies make this quarter?"), pass activity="sustainability" or rely on server-side intent routing to avoid conflation with analyst margin sustainability questions. Multi-company and sector queries return active initiatives along with confirmed_absent brands (checked tickers with confirmed silence / no initiatives discussed). For single-brand earnings, brand_tracker includes earnings automatically. For per-ticker structured analysis (analyst concerns, activity breakdown, validated consumer trends), use get_company_earnings instead — it reads the canonical truth layer. Results may include "knowledge_graph" or "web_supplemental" provenance. OUTPUT REQUIREMENT: For multi-company thematic findings, present results in a structured evidence table citing: | Company (Ticker) | Reporting Quarter | Speaker (Analyst & Firm / Exec) | Verbatim Quote / Finding |. Clearly distinguish Fodda's synthesized theme labels (e.g. "Pilot Trap") from what analysts or executives actually said on the call.

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.)

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.

The canonical per-ticker earnings source for 517 covered companies. - view='snapshot' (default): Full quarterly record including headline, financial metrics, analyst sentiment, strategic activity, CEO quotes, and top Q&A entries. - view='qa': Itemized analyst Q&A session with verbatim analyst questions, analyst names, analyst firms, executive responses, and response directness. USE THIS VIEW whenever the user asks who asked a question, for analyst names/firms, or for verbatim call quotes. - view='history': Narrative quarterly timeline. - view='compare': Side-by-side comparison of 2-5 tickers.

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.)

Discovers consumer trends that have been empirically corroborated by corporate earnings reports in Fodda's cross-validation pipeline. Connects high-level cultural trend signals with corroborating executive statements and analyst Q&A from quarterly 10-Q/10-K earnings calls. Use when you need proof that an emerging consumer trend is impacting corporate P&L and executive strategy (e.g., 'Show consumer trends validated in recent beauty earnings calls'). Sibling routing: Use search_graph for pure cultural/consumer trend discovery; use get_company_earnings for full single-ticker filings; use get_validated_trends when you specifically need the bridge between consumer trends and corporate earnings validation. Returns validated trend records with corroborating company tickers, validation types, analyst themes, and executive quote excerpts.