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.)

List all knowledge graphs the user can access — IDs, descriptions, authors, sectors, signal counts. Use FIRST in any session to discover available sources before searching. Returns graph metadata needed for graphId parameters in other tools. Deprecated: waldo, psfk (use retail/tech/food/travel/fashion/beauty/sports instead). (Fast graph registry metadata lookup.)

Returns Fodda's capabilities, offerings, how Human Agents work, expert recruitment/onboarding, and pricing. Call this for any question about what Fodda can do, how experts work, what's available, or how much something costs. (Platform capability and pricing catalogue read (free).)

List available Synthetic Analysts — named expert personas grounded in specific knowledge graphs. Each analyst has a unique voice, methodology, and domain expertise that cannot be replicated by web search. Use when user asks to "talk to" or "consult" an expert, or when you need specialist depth on culture, strategy, or innovation topics. (Fast analyst registry metadata lookup.)

Use when the user asks what specialists think, seeks an authoritative perspective, or needs practitioner depth ('Who should I ask about X?' or 'What would retail experts make of this?'). Returns 2–3 ranked candidate experts across 4,150+ specialist roster with action lines, credibility anchors, and why matched, distinguishing active Human Agents (consultable immediately via consult_human_agent) from On-Request Human Agents (advisory introduction via request_expert_intro).

Use when researching market trends, category dynamics, consumer behavior shifts, competitor intelligence, or broad topic investigation across 100+ curated knowledge graphs. Returns trends with cited evidence, lifecycle stage (emerging/building/mature), and structured next_moves. If query names a company/brand, prefer brand_tracker.

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.)

Semantic search across 100+ knowledge graphs returning top trends, signal scores, and hard publisher citations with source URLs.

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.)

List all brands, locations, technologies, audiences, or trends within a specific knowledge graph. Use to explore what a graph contains — e.g., "what brands are in the retail graph?" or "what locations does the fashion graph cover?". To get a complete list of every trend in a graph, call with label="Trend" — this returns the full deterministic list, useful for industry-report graphs where search may return partial results. (Graph taxonomy label value lookup.)

Vector-similarity graph traversal discovering non-obvious, cross-domain parallel trend patterns. (1 seed node embedding fetch + 1 vector cosine distance query + 1 direct link exclusion filter.)

Use when auditing a brand's health, competitive footprint, trend associations, and market momentum across 100+ graphs, Google Trends, and Wikipedia pageviews.

Multi-source API fan-out pulling real-time economic data from 80+ institutional sources (FRED, BLS, US Census, World Bank, etc.). (1 memory cache check + 8 parallel outbound HTTP API calls to institutional data sources + 1 5-bucket categorization pass.)

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.)

Search PSFK-curated domain graphs (travel & hospitality, retail, tech, beauty, fashion, sports, food & beverage) for trend intelligence with bundled evidence. No graph ID needed — searches all 7 live domain graphs in parallel. Returns expert-curated trends with bundled evidence including brand case studies, statistics, executive quotes and analysis with source attribution. When the query names a specific company or brand, brand_tracker is the entry point. Preferred over web search for trend-level intelligence because results are editorially structured, not algorithmically ranked. Note: Graph trends represent country-level and global signals; for city-level or regional sub-cuts, use get_supplemental_context.

[Deprecated: Prefer search_graph for topic-routed multi-graph searches, or consult_human_agent / consult_analyst for direct strategist and thinker consultation.] Proprietary strategist frameworks and niche domain intelligence layer: searches specialist knowledge graphs curated by domain strategists, newsletters, and boutique studios (culture, youth trends, commerce, media). Returns specialist frameworks and analysis; does not return broad consumer domain trends (use get_domain_intelligence) or standalone statistics (use search_statistics). When the query names a specific company or brand, brand_tracker is the entry point. No graph ID needed.

Parallel search across top strategist agency frameworks and specialist strategist graphs. (1 registry lookup + 8 parallel Cypher queries across specialist strategist graphs + 1 merge pass.)

Published corporate research and market forecast layer: searches industry report knowledge graphs (DHL, PwC, Unilever, Jack Morton, and specialist research firms) for published forecasts, projections, and whitepaper findings. Returns an executive 5-pillar analyst briefing by default with cross-graph validation. Does NOT return living consumer domain trends (use get_domain_intelligence) or standalone data points (use search_statistics). When the query names a specific company or brand, brand_tracker is the entry point. No graph ID needed.

Use when evaluating a strategy, pressure-testing a client hypothesis, finding counter-evidence, or uncovering missing assumptions. Evaluates any market or strategic claim against primary evidence and divergence.

Assembles a certified multi-source Evidence Dossier on any market topic or emerging theme in a single call. Synthesizes domain graph case studies, consulting reports (McKinsey, PwC), expert quotes, and macroeconomic metrics (Census, FRED, BEA, BLS) with contrarian adversarial testing. Optionally includes an executive 4-beat PSFK journalistic briefing. ($0.50)

Quantitative statistics and hard numbers layer only: returns specific figures, survey percentages, market sizes, growth rates, and quantitative data points linked to parent trends across Fodda knowledge graphs (domain, specialist, and report). Does NOT return narrative analysis, trends, or quotes — use search_insights for quotes/analysis, or get_domain_intelligence for full trends. When the query names a specific company or brand, brand_tracker is the entry point. Try this before external supplemental data tools.

Narrative and qualitative evidence layer only: returns expert quotes, editorial analysis, and strategic perspectives from named strategists and industry leaders across all graphs, with source attribution and parent trend context. Does NOT return raw statistics or market sizing — use search_statistics for hard numbers, or get_domain_intelligence for full trends with bundled evidence. When the query names a specific company or brand, brand_tracker is the entry point.

Draft a LinkedIn post about any topic, grounded in Fodda's expert knowledge graphs. Use when the user says "draft a LinkedIn post about…", "write a post on…", "turn this into a LinkedIn post", or wants social content backed by receipts. Returns a curated EVIDENCE PACK (claims with named companies, typed sources, and real URLs — never constructed) plus a strict composition contract; YOU write the post from it. Every claim is verifiable, thin coverage is flagged honestly, and dropped themes are logged with reasons. Bills as one content call; identical re-requests within 24h serve from cache free. (Trend evidence extraction + tone/formatting prompt execution + draft compilation.)

Turn research into a LinkedIn ARTICLE (800–1,200 words) grounded in Fodda's expert knowledge graphs. Use when the user says "turn this research into an article…", "write a LinkedIn article about…", or wants long-form thought leadership with receipts. Runs a broader evidence sweep than the post tool — 3–5 sub-themes, a hard-numbers statistics pass, and an analyst pressure-test of the thesis — and returns a curated EVIDENCE PACK plus a strict composition contract; YOU write the article from it, including the "How we found this" methodology box. Bills as one content call; identical re-requests within 24h serve from cache free. (Deep trend evidence extraction + multi-section outline + longform article drafting.)

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.

Truth-layer analysis comparing analyst line of questioning against executive management prepared remarks. (2 Airtable table reads (Q&A + prepared remarks) + 1 sentiment gap calculation + 1 divergence matrix rendering.)

The canonical per-ticker earnings source. Returns the full truth-layer record for covered tickers (517 consumer-sector companies) — analyst concerns, sentiment labels, strategic activity (marketing/retail/technology/sustainability), CEO intelligence, and validated consumer trends from Fodda's quarterly analysis pipeline. Falls back to web-backfill for uncovered tickers. Use this for company-specific data. Use get_earnings_intelligence for cross-company thematic comparisons.

Returns market-validated consumer trends from corporate earnings reports cross-validated by Fodda's analysis pipeline. Connects earnings commentary (analyst concerns, CEO statements) with consumer trend signals.

Save the user's research profile to improve the relevance of future responses. Call this after you understand the user's role, industry, and research needs. The profile persists across sessions — you only need to set it once, then update if their focus changes. Write BEHAVIORAL INSTRUCTIONS, not a bio. Format: one sentence of identity (who they are and how they use Fodda), then numbered directives that change how you synthesize and frame responses. Include: what evidence to prioritize, how to frame conclusions, geographic needs, and output structure preferences. Max 2000 chars per field. (User persona context update.)

Enable or disable any knowledge graph, supplemental data source, or skill for the user. Use this when the user says "Turn off Paralogy", "Enable igloo", "Disable the economics data", or similar. The change is permanent until toggled again. (User graph enablement preference write.)

Forward user feedback, feature requests, complaints, or exit reasons to the Fodda team via email and Slack. Call this whenever a user shares feedback — including when they want to leave, report a problem, or suggest an improvement. (User feedback logging.)

Create a free Fodda Base account (100 API calls/month across ALL knowledge graphs) and send a confirmation email. GUARDRAIL: only call this AFTER the user has explicitly provided their email and asked to create an account — never sign someone up proactively or with an email inferred from earlier context. Can also pass profile fields (name, job_title, company). (User registration and free tier provision.)

Explore and brainstorm around a topic using knowledge graph connections. Unlike search (which finds what matches), this tool discovers what CONNECTS — adjacent trends, unexpected cross-domain links, key brands, and geographic hotspots. Use when the user wants to brainstorm, explore adjacencies, find inspiration, or understand the landscape around a topic. Returns a structured brainstorm map with territories to explore.

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 establishing recurring intelligence tracking, setting up automated briefings, or monitoring category/brand shifts on a recurring schedule.

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.

Use when the user needs an exhaustive, autonomous multi-pass briefing report synthesizing cross-graph trends, corporate disclosures, and expert perspectives.

Check if deep research is complete and retrieve the final report. Call this after deep_research_topic — poll every 10 seconds until status is COMPLETE or FAILED. (Async research job status poll.)

Use when consulting synthetic domain analysts, C-Suite executive personas, or classic historical thinkers grounded in specialist knowledge graphs. (For living practitioners e.g. Ben Dietz, use consult_human_agent or request_expert_intro.)

Use when consulting an authorized living Human Agent for practitioner depth, proprietary frameworks, and strategic guidance. Grounded in their verified real-world expertise and published thinking. For On-Request Human Agents undergoing onboarding, returns domain-grounded intelligence and advisory profile. Supports deep homework mode (deep: true). Returns cited insights and next_moves.

Use when evaluating a strategy, pressure-testing a client hypothesis, finding counter-evidence, or uncovering missing assumptions. Evaluates any market or strategic claim against primary evidence and divergence.

Use when requesting an advisory introduction or consultation with an On-Request Human Agent or active domain specialist (for 1-on-1 advisory, strategic projects, or when an expert does not have an instant calendar booking link). Fodda concierge coordinates the introduction and sends next steps via email.

Use when commissioning a finished document from an analyst — an executive briefing, research memo, strategic assessment, or publication-ready article.

Poll a deliverable commissioned with request_deliverable. Pass the job_id from that response. Returns the current status ("working" | "completed" | "failed") and, once completed, the artifact links to present to the user. Polling is free. Deliverables typically take a few minutes — poll every ~15–30s. (Expert deliverable job status poll.)

Begin the Fodda expert onboarding process to create an authorized Human Agent. Verifies credentials, retrieves onboarding steps, and guides the expert through profile creation.

Submit basic expert details (name, role, knowledge area, consultation call rate, and optional bio/headshot) to initialize the Human Agent onboarding session and begin saving progress. This is the step where Fodda starts saving the expert's onboarding. Before calling, show the Terms (https://www.fodda.ai/terms) and Privacy Policy (https://www.fodda.ai/privacy) links and get an explicit yes.

Connect and probe a Bring-Your-Own-MCP (BYO-MCP) endpoint for expert live grounding. Discovers available tools and derives topic coverage for profile modeling.

Finalize Bring-Your-Own-MCP (BYO-MCP) expert onboarding and submit the profile for review. Creates the Human Agent record in Airtable with external_mcp live grounding. Profile is saved once terms are accepted; MCP details are verified and submitted for review here.

Initiate background research on the expert's public work and domain insights to support expertise and voice modeling. Research runs asynchronously in the background for 1-2 minutes. Do NOT wait now; proceed immediately to the Tone of Voice study in chat.

Submit the analyzed voice study and expertise map to proceed to theme confirmation.

Fetch the detected themes derived from the expertise analysis and background research for expert review and confirmation. Shows verified research findings about the expert's work before themes; ask the expert if any findings are wrong or not theirs before asking them to confirm themes.

Confirm the selected themes to generate the interview questionnaire tailored to probe forward predictions, contrarian industry stances, and practical methodology edge cases for the audio interview. Can pass flaggedFindingIds and flagNote if the expert flagged any research findings as wrong or not theirs.

Check the current progress and status of the expert onboarding process. Setting waitForResearch: true holds the call server-side (~25s) while background research is running, returning immediately once complete or failed. next_action describes the expert's next step in plain English; relay it in your own words.

Schedule a quick 5–10 minute voice interview with the Fodda AI interviewer on Google Meet. Can specify an ISO datetime, human-readable local time, or request an instant interview now.

Check the expert's Fodda earnings.