generated: '2026-09-19'
method: searched
status: published
source: https://www.fodda.ai/.well-known/mcp.json + https://registry.modelcontextprotocol.io/v0.1/servers?search=ai.fodda/mcp-server
  + https://github.com/piers-fawkes/fodda-mcp (tools-manifest.json, README.md) + https://www.fodda.ai/llms-full.txt
  + live probes of mcp.fodda.ai 2026-09-19
summary: 'Fodda ships a hosted, first-party MCP server at mcp.fodda.ai (streamable-http /mcp, SSE /sse, a curated
  17-tool Copilot Studio endpoint /copilot, and offering-scoped endpoints such as /grok-brand-context) AND a stdio
  package on npm (fodda-mcp). Anonymous tools/list and initialize both return HTTP 401 with a JSON-RPC -32000 error
  and a WWW-Authenticate: Bearer resource_metadata="https://mcp.fodda.ai/.well-known/oauth-protected-resource/mcp"
  challenge, so the live inputSchema set is auth-gated; the 53-tool list below is the provider''s own build-time
  tools-manifest.json (names, categories, billing keys, descriptions — no input schemas), corroborated by the tool
  list in llms-full.txt.'
deployment:
  mode: both
  endpoint: https://mcp.fodda.ai/mcp
  install: npx -y fodda-mcp
  package: https://www.npmjs.com/package/fodda-mcp
  auth: oauth
  auth_methods:
  - oauth
  - api-key
  verified: probed
  notes: endpoint verified by POST tools/list on 2026-09-19 (401 + RFC 9728 challenge — the server is live and gated,
    not absent). install string verbatim from https://www.fodda.ai/api and /agents ("npx -y fodda-mcp", env FODDA_API_KEY).
    The npm package (1.46.31, 2026-08-23) lags the hosted server version (1.46.79 per the MCP host landing page
    and the agent card) by 48 patch releases.
server:
  name: Fodda
  registry_name: ai.fodda/mcp-server
  transport:
  - streamable-http
  - sse
  - stdio
  url: https://mcp.fodda.ai/mcp
  sse_url: https://mcp.fodda.ai/sse
  copilot_url: https://mcp.fodda.ai/copilot
  personal_connection_url_pattern: https://mcp.fodda.ai/c/<token>
  protocol_version: '2025-11-25'
  server_version: 1.46.79
  discovery:
    url: https://www.fodda.ai/.well-known/mcp.json
    file: well-known/fodda-ai-mcp-discovery.json
    note: Non-standard but provider-served MCP discovery document naming the transport, endpoint, auth and signup
      URL.
  offering_scoped_endpoints:
  - name: ai.fodda/grok-brand-context
    card: well-known/fodda-ai-mcp-offering-grok-brand-context.json
    endpoint: https://mcp.fodda.ai/grok-brand-context
    tools: 8
  - name: expert-consult
    card: well-known/fodda-ai-mcp-offering-expert-consult.json
  - name: brand-intelligence
    card: well-known/fodda-ai-mcp-offering-brand-intelligence.json
  - name: deep-research
    card: well-known/fodda-ai-mcp-offering-deep-research.json
  listings:
  - Official MCP Registry (registry.modelcontextprotocol.io) as ai.fodda/mcp-server, latest 1.46.31 published 2026-08-23
  - Claude custom connector (claude.ai / Claude Desktop) via personal connection URL
  - ChatGPT Developer Mode custom connector
  - Microsoft Copilot Studio (curated /copilot endpoint)
  - Google Gemini Connected Apps / Vertex AI ADK (/sse)
  - xAI Grok Bot templates (five analyst bots)
  - VS Code extension fodda.fodda and Open VSX fodda/fodda
auth:
  methods:
  - oauth2
  - api-key
  - personal-connection-url
  oauth2:
    protected_resource_metadata:
    - https://mcp.fodda.ai/.well-known/oauth-protected-resource
    - https://mcp.fodda.ai/.well-known/oauth-protected-resource/mcp
    - https://mcp.fodda.ai/.well-known/oauth-protected-resource/copilot
    authorization_server: https://clerk.fodda.ai
    authorization_server_metadata: well-known/fodda-ai-clerk-oauth-authorization-server.json
    registration_endpoint: https://clerk.fodda.ai/oauth/register
    pkce: S256
    grant_types:
    - authorization_code
    - refresh_token
    - urn:ietf:params:oauth:grant-type:device_code
    scopes_supported:
    - openid
    - profile
    - email
    - public_metadata
    - private_metadata
    - offline_access
    - user:org:read
    resource_scopes_supported:
    - read
    note: 'Scopes on the resource side come from www.fodda.ai/.well-known/oauth-protected-resource (scopes_supported:
      [read], authorization_servers: []); the MCP-host documents name clerk.fodda.ai as the authorization server
      and list no scopes.'
  apikey:
    header: X-API-Key
    alt_header: 'Authorization: Bearer <api key>'
    env: FODDA_API_KEY
    deprecated: ?api_key= / ?user_id= query-string connection URLs — return HTTP 401 with an explicit message (per
      the fodda-mcp README)
  challenge_observed:
    status: 401
    body: '{"jsonrpc":"2.0","error":{"code":-32000,"message":"Authentication required. Connect via OAuth, or use
      your personal connection URL or API key from https://app.fodda.ai."},"id":1}'
    www_authenticate: Bearer resource_metadata="https://mcp.fodda.ai/.well-known/oauth-protected-resource/mcp"
billing:
  model: metered per API call, $0.50/call; per-tool call counts published in tools-manifest.json / llms-full.txt;
    SPT (Stripe Shared Payment Token) via HTTP 402 is REST-only per llms.txt ("Direct REST API only")
  free_tier: 100 API calls per month with an account key
tool_count: 53
tool_count_source: tools-manifest.json (count 53, generated by the provider at build time; llms-full.txt lists the
  36 tools visible in the default profile and the Copilot endpoint exposes 17)
tools_manifest_file: mcp/fodda-ai-tools-manifest.json
input_schemas: not captured — anonymous tools/list is gated (401); schemas require authenticated introspection
tools:
- name: begin_expert_onboarding
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: Begin the Fodda expert onboarding process to create an authorized Human Agent. Verifies credentials,
    retrieves onboarding steps, and guides the expert through profile creation.
- name: brainstorm_topic
  category: Ideation
  bills_as: brainstorm
  profiles:
  - mcp
  description: 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.
- name: brand_tracker
  category: Brand
  bills_as: brand_intelligence
  profiles:
  - mcp
  - brand-intelligence
  - copilot
  - chatgpt
  - grok-brand-context
  description: Comprehensive brand footprint combining 100+ Neo4j graphs, Google Trends, Wikipedia, Amazon commerce
    data, and earnings transcripts into a single deliverable.
- name: check_deliverable_status
  category: Status
  bills_as: free
  profiles:
  - mcp
  - expert-consult
  description: 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.)
- name: check_research_status
  category: Status
  bills_as: free
  profiles:
  - mcp
  - deep-research
  - copilot
  - chatgpt
  description: 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.)
- name: check_supplemental_status
  category: Status
  bills_as: free
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - copilot
  - chatgpt
  description: 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.)
- name: confirm_themes
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: 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.
- name: consult_analyst
  category: Expert
  bills_as: expert_agent
  profiles:
  - mcp
  - expert-consult
  - copilot
  - chatgpt
  description: Direct 1-on-1 consultation with named human or synthetic expert analysts grounded in specialist knowledge
    graphs.
- name: consult_human_agent
  category: Expert
  bills_as: human_agent_consult
  profiles:
  - mcp
  - expert-consult
  - copilot
  - chatgpt
  description: 'Consult an authorized Human Agent (created directly with the named expert''s consent, participation,
    and curated knowledge graph). The expert answers in their voice — one-off questions or multi-turn engagements
    (pass session_id back to continue). Each human agent has a unique methodology, domain expertise, and analytical
    lens with a curated evidence base. Supports deep homework mode (pass deep: true or ask to "do your homework"
    to trigger background research across specialist graphs and market data). Call list_analysts or find_expert
    first to find the right expert ID. Responses include structured next_moves containing recommended follow-up
    angles, adjacent graphs, and drill-downs, and may include coverage status, source attribution, referrals, or
    `book_a_call` for booking time with the real person.'
- name: deep_research_topic
  category: Research
  bills_as: deep_research_light/heavy
  profiles:
  - mcp
  - deep-research
  - copilot
  - chatgpt
  description: Full multi-pass autonomous research agent producing an executive narrative brief complete with quantitative
    data tables and inline citations. (Multi-pass autonomous research agent (Plan -> 6 Graph queries -> 8 Supplemental
    APIs -> 2 Earnings DB queries -> 2 LLM synthesis & citation passes).)
- name: discover_adjacent_trends
  category: Ideation
  bills_as: adjacent_trends
  profiles:
  - mcp
  - grok-brand-context
  description: 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.)
- name: draft_linkedin_article
  category: Content
  bills_as: linkedin_article
  profiles:
  - mcp
  description: 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.)
- name: draft_linkedin_post
  category: Content
  bills_as: linkedin_post
  profiles:
  - mcp
  description: 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.)
- name: expert_onboarding_research
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: Initiate background research on the expert's public work and domain insights to support expertise
    and voice modeling.
- name: finalize_byo_mcp_onboarding
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: Finalize Bring-Your-Own-MCP (BYO-MCP) expert onboarding and submit the profile to Fodda. Creates
    the Human Agent record in Airtable with external_mcp live grounding. This is the sole submission step; profile
    and MCP details are not saved to Fodda until this tool executes.
- name: find_expert
  category: Expert
  bills_as: free
  profiles:
  - mcp
  - expert-consult
  - copilot
  - grok-brand-context
  description: Find 2–3 genuine candidate experts for a question, brief, or situation with domain-grounded rationale.
    Discovery tool ("who should I ask") across Human Agents (living practitioners), Classic Agents (historical thinkers),
    C-Suite, and Synthetic domain specialists. Evaluates lane overlap, filters out declared blind spots, and fails
    honestly when no expert matches. Call this when deciding which expert to consult, then pass the matched analyst_id
    to consult_human_agent or consult_analyst.
- name: generate_visual
  category: Visual
  bills_as: free
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - chatgpt
  description: '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.)'
- name: get_capabilities
  category: Other
  bills_as: free
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - copilot
  - chatgpt
  - grok-brand-context
  description: Returns Fodda's capabilities, offerings, and what they cost. Call this for any question about what
    Fodda can do, what's available, or how much something costs. (Platform capability and pricing catalogue read
    (free).)
- name: get_company_earnings
  category: Financial
  bills_as: earnings_company
  profiles:
  - mcp
  - earnings-intelligence
  - copilot
  - chatgpt
  description: 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.
- name: get_detected_themes
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: Fetch the detected themes derived from the expertise analysis and background research for expert
    review and confirmation.
- name: get_domain_intelligence
  category: Intelligence
  bills_as: domain_intelligence
  profiles:
  - mcp
  - grok-brand-context
  description: '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.'
- name: get_earnings_divergence
  category: Financial
  bills_as: earnings_divergence
  profiles:
  - mcp
  - earnings-intelligence
  - chatgpt
  description: 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.)
- name: get_earnings_intelligence
  category: Financial
  bills_as: earnings_intelligence
  profiles:
  - mcp
  - earnings-intelligence
  - chatgpt
  - grok-brand-context
  description: 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, or sector filters.
    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.
- name: get_evidence
  category: Graph
  bills_as: standalone_evidence
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - copilot
  - chatgpt
  - grok-brand-context
  description: Semantic search across 100+ knowledge graphs returning top trends, signal scores, and hard publisher
    citations with source URLs.
- name: get_expert_intelligence
  category: Intelligence
  bills_as: expert_intelligence
  profiles:
  - mcp
  description: 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.)
- name: get_label_values
  category: Graph
  bills_as: free
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - chatgpt
  description: 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.)
- name: get_my_account
  category: Account
  bills_as: free
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - copilot
  - chatgpt
  description: '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.)'
- name: get_my_earnings
  category: Other
  bills_as: free
  profiles:
  - mcp
  description: Check the expert's Fodda earnings.
- name: get_neighbors
  category: Graph
  bills_as: free
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - chatgpt
  description: 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.)
- name: get_node
  category: Graph
  bills_as: free
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - chatgpt
  description: 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.)
- name: get_onboarding_status
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: Check the current progress and status of the expert onboarding process.
- name: get_report_intelligence
  category: Intelligence
  bills_as: report_intelligence
  profiles:
  - mcp
  description: '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.'
- name: get_specialist_intelligence
  category: Intelligence
  bills_as: specialist_intelligence
  profiles:
  - mcp
  description: '[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.'
- name: get_supplemental_context
  category: Supplemental
  bills_as: standalone_supplemental
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - copilot
  - chatgpt
  description: 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.)
- name: get_validated_trends
  category: Financial
  bills_as: earnings_intelligence
  profiles:
  - mcp
  - topic-research
  - earnings-intelligence
  - copilot
  - chatgpt
  description: 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.
- name: list_analysts
  category: Expert
  bills_as: free
  profiles:
  - mcp
  - expert-consult
  - copilot
  - chatgpt
  description: 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.)
- name: list_graphs
  category: Account
  bills_as: free
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - copilot
  - chatgpt
  description: '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.)'
- name: manage_scheduled_reports
  category: Account
  bills_as: free
  profiles:
  - mcp
  description: Create, list, cancel, update, pause, or resume scheduled intelligence briefings. Users can set up
    autonomous research that runs weekly (Mondays) or daily (Mon-Fri) at 9am in their timezone, delivered via email
    or Slack. Costs 20 API calls per run. Supports topic research or brand intelligence report types. (Scheduled
    report CRUD operation.)
- name: read_url
  category: Web
  bills_as: url_as_prompt
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - copilot
  - chatgpt
  description: 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.
- name: request_deliverable
  category: Expert
  bills_as: skill_deliverable
  profiles:
  - mcp
  - expert-consult
  description: 'Commission a finished document from an analyst — a skill-based deliverable like a marketing plan,
    deck review, or trend briefing. Specify offering_key (see the `offerings` list on each analyst from list_analysts),
    a brief (2–5 sentences: audience, goal, constraints), and optional attachments. The analyst researches on your
    behalf, then produces the document in the background. Returns a job_id — poll with check_deliverable_status
    until status is "completed" to get the artifact links. The offering price is charged on acceptance; the analyst''s
    research is included, not billed separately. Example brief: "Marketing plan for a DTC skincare launch targeting
    Gen-Z, $50k budget, 90-day horizon." (Expert analyst workflow dispatch, research query execution, and deliverable
    template rendering.)'
- name: request_expert_intro
  category: Expert
  bills_as: free
  profiles:
  - mcp
  - expert-consult
  - copilot
  description: Capture and submit an inquiry or advisory request to connect with a human expert on Fodda (for 1-on-1
    consultations, advisory projects, or when an expert does not have an instant calendar booking link). Fodda concierge
    coordinates the introduction and sends next steps via email.
- name: schedule_interview
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: Schedule a 15–20 minute expertise deep-dive audio interview with the Fodda AI interviewer. Can specify
    an ISO datetime, human-readable local time, or request an instant interview now.
- name: search_graph
  category: Search
  bills_as: topic_research
  profiles:
  - mcp
  - brand-intelligence
  - topic-research
  - deep-research
  - earnings-intelligence
  - expert-consult
  - copilot
  - chatgpt
  description: Find trends, signals, and expert insights across 100+ curated knowledge graphs covering retail, beauty,
    tech, food, travel, sports, and 30+ specialist domains. Returns trend data with cited evidence, source attribution,
    lifecycle stage (emerging/building/mature/fading), and structured next_moves containing recommended follow-up
    angles, adjacent graphs, and drill-downs that can be surfaced to the user. If graphId is omitted, searches ALL
    accessible graphs in parallel (recommended default). When the query names a company or brand, brand_tracker
    is the entry point. Use for market trends, competitor analysis, innovation signals, consumer behavior, cultural
    shifts, or any topic where you want curated, cited expert intelligence.
- name: search_insights
  category: Search
  bills_as: standalone_insights
  profiles:
  - mcp
  - topic-research
  - copilot
  - chatgpt
  description: '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.'
- name: search_statistics
  category: Search
  bills_as: standalone_statistics
  profiles:
  - mcp
  - topic-research
  - copilot
  - chatgpt
  description: '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.'
- name: send_feedback
  category: Account
  bills_as: free
  profiles:
  - mcp
  description: 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.)
- name: sign_up_free_account
  category: Account
  bills_as: free
  profiles:
  - mcp
  description: '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.)'
- name: submit_basic_info
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: Submit basic expert details (name, role, knowledge area, consultation call rate, and optional bio/headshot)
    to initialize the Human Agent onboarding session.
- name: submit_expertise_analysis
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: Submit the analyzed voice study and expertise map. Requires explicit review and acceptance of Fodda
    Terms of Service (https://www.fodda.ai/terms) and Privacy Policy (https://www.fodda.ai/privacy).
- name: submit_mcp_source
  category: Onboarding
  bills_as: free
  profiles:
  - mcp
  description: 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.
- name: toggle_graph_preference
  category: Account
  bills_as: free
  profiles:
  - mcp
  description: 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.)
- name: update_user_profile
  category: Account
  bills_as: free
  profiles:
  - mcp
  description: '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.)'
- name: verify_claim
  category: Expert
  bills_as: human_agent_consult
  profiles:
  - mcp
  - expert-consult
  - grok-brand-context
  description: Verify a factual claim, hypothesis, or strategic assertion against primary evidence held in Fodda's
    expert knowledge graphs. Supported by an authorized Human Agent (a verified, living practitioner) who stands
    behind the structured verdict ("confirms", "contradicts", "partial", or "no_coverage") with cited evidence and
    rationale. When analyst_id is provided, routes to that specific Human Agent. When omitted, automatically routes
    to the best matching Human Agent based on domain relevance; if no Human Agent covers the domain, fails honestly
    without falling back to synthetic personas. Returns structured verdict, confidence level (full, partial, thin),
    one_line summary, rationale, sources, expert details, and booking information. If confidence is thin, present
    this to the user as limited domain coverage in plain language without echoing technical tags.