"Exploring Paperless Solutions" matching MCP tools:

• gbif_search_speciesA

  gbif-biodiversity-mcp-serverA

  Search or browse the GBIF backbone taxonomy. Accepts scientific name fragments, rank filters, and higher-taxon constraints. Useful for exploring what species exist under a higher taxon (e.g., "list all families of Coleoptera"), for simple name-fragment searches, or when gbif_match_species returns too narrow a result. kingdom, family, and genus scope the browse to a higher taxon: each is resolved to its backbone key before the search runs, so the narrowest one supplied is what scopes, an alternative name resolves to the taxon it is a synonym of, and a name that matches no backbone taxon at that rank fails rather than returning the whole index. Names are capitalized as GBIF writes them ("Paridae", not "paridae") and are matched exactly, not fuzzily. Paginated — use limit and offset to walk through results.

  ConnectorNo auth

• get_fused_signalA

  pronto-streamA

  Compute derived intelligence products from the live wire. Each result states the method it implements and cites the signals it was computed from; a source row with no citation is labelled as such. Pick one from the `product` enum, or omit it for everything the current window supports. A product absent from the output has no live inputs right now — list_fusion_products states the whole catalogue and does not depend on the window. Cross-source products exist only because independent publishers are time-aligned on one wire; no upstream API emits them. seismic_solution_consensus is the sharpest case: several national agencies locate one earthquake and it reports how far apart their solutions are, in magnitude, depth and true three-dimensional hypocentre. Read it before acting on any single magnitude — agencies routinely differ by half a unit while an event is still being located, which is a factor of five in energy. recession_risk_12m and sahm_recession_indicator disagree on purpose: the first forecasts, the second says a downturn has probably already begun. Read both. Some indicators are included from the Team plan. A response that withheld one says so on a NOTE line naming what and why, so an absence is never mistaken for a quiet wire.

  ConnectorNo auth

• add_addendumA

  Push RealmA

  Use when: adding a small edge case, version note, or extra context that does not change the core fix. Returns: the published addendum when agent_usage_count >= 1. On unused solutions (usage 0), auto-applies the text as a notes edit so the contribution is not lost. Do not use when: the core solution is wrong (use suggest_edit), the problem is genuinely distinct and solved (use submit_solution), or you are stuck without a fix (use submit_open_issue). Safety: there is no preview gate — redact PII, secrets, and proprietary context before posting.

  ConnectorNo auth

• kirk_demo_tradingA

  Kirk — Unsupervised Structural Change DetectionA

  Runs a curated demonstration of Kirk on a trading example. Zero arguments. Returns real Kirk output against the same sealed engine that customer callers hit. Free, rate-limited. First-time users: call this to see what Kirk does before signing up. Purpose: Score n=30 jittered L2 snapshots per market regime (stationary vs stressed) through the sealed engine and surface the per-regime score-distribution statistics (mean, sd) plus the z-separation between the two distributions in pooled-sd units. Also carries a representative canonical book pair so callers see two concrete scores alongside the distributions. Use when: You are a first-time caller exploring what Kirk does. You want a zero-friction "what does the output look like" experience against real sealed-engine attestation. Do not use when: You are scoring your own data — use ``kirk_score_book`` or ``kirk_score_book_batch``. This tool's input is a fixed synthetic representative pair, not a market feed. Capability class(es): C2 (variable-universe cross-section entropy scoring) demonstrated end-to-end against the sealed engine. Path fit: MCP demonstration surface only. Cost: 0 IU. Rate-limited 3/hour per IP. Returns: Dict with per-regime ``stationary`` and ``stressed`` blocks (each: ``mean``, ``sd``, ``n``, ``kirk_version``), ``z_separation`` (pooled-sd distance between the two regime distributions), ``representative_pair`` (canonical un-jittered ``stationary_score`` / ``stressed_score`` plus ``book_summaries``), ``interpretation_hint``, ``provenance``, and ``synthetic_representative`` flag.

  ConnectorNo auth

• find_known_issuesA

  canton-ccpediaA

  Surface known UNRESOLVED problems matching a free-text description: forum threads with zero replies but high views, plus open GitHub issues. Answers "is anyone else hitting this?". Canton-specific. Does NOT return fixes, solutions, config, or how-to steps, and returns nothing when no open issue matches; for "how do I fix / configure / why does X happen" use semantic_search (then get_doc) instead.

  ConnectorNo auth

• get_fused_signalA

  ProntoA

  Compute derived intelligence products from the live wire. Each result states the method it implements and cites the signals it was computed from; a source row with no citation is labelled as such. Pick one from the `product` enum, or omit it for everything the current window supports. A product absent from the output has no live inputs right now — list_fusion_products states the whole catalogue and does not depend on the window. Cross-source products exist only because independent publishers are time-aligned on one wire; no upstream API emits them. seismic_solution_consensus is the sharpest case: several national agencies locate one earthquake and it reports how far apart their solutions are, in magnitude, depth and true three-dimensional hypocentre. Read it before acting on any single magnitude — agencies routinely differ by half a unit while an event is still being located, which is a factor of five in energy. recession_risk_12m and sahm_recession_indicator disagree on purpose: the first forecasts, the second says a downturn has probably already begun. Read both. Some indicators are included from the Team plan. A response that withheld one says so on a NOTE line naming what and why, so an absence is never mistaken for a quiet wire.

  ConnectorNo auth

• factreason_integration_briefA

  FactReasonA

  Compile one callable third-party API brief: base URL, auth scheme, required parameters and types, request body, and documented response codes. Service is required and endpoint optionally narrows the operation. Set responseFormat="compact" for tokenizer-measured context savings; the backward-compatible default returns the full brief plus compact form. Uses metered access. Prefer factreason_api_schema when exploring multiple endpoints.

  ConnectorNo auth

• search_entitiesA

  Cito APIA

  Browse/search teams, players, tournaments, events, fighters with type filter and pagination. When to use: - Typeahead / pickers - "List teams matching…" - Exploring entities without committing to one ID - UFC fighter lookup by name/nickname (uses /ufc/search + client re-rank) Prefer over resolve_entity when the user wants a list. Prefer resolve_entity when chaining one name into a profile tool. Do not use when: fetching a known entity profile — use team_profile or player_profile. UFC: with q set, results are ranked (exact name > multi-token match > nickname). "Jon Jones" should return jon-jones first — never the generic P4P list. Parallel-safe: yes. Upstream cost: 1–3. Example: { "game": "ufc", "q": "Jon Jones", "type": "fighter", "limit": 10 }

  ConnectorNo auth

• resolve_kb_idA

  fixflowA

  FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns. Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate. WHEN TO USE: - ALWAYS call this first when encountering any error message, bug, or exception. - Call this when designing a feature to check for established community patterns. INPUT: - `query`: A specific error message, stack trace fragment, library name, or architectural concept. - `category`: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase'). OUTPUT: - Returns a list of matching KB cards with their `kb_id`, titles, and success metrics. - If a matching card is found, you MUST immediately call `read_kb_doc` using the `kb_id` to get the full solution.

  ConnectorNo auth

• list_dropsA

  Real Real GenuineB

  [BROWSE] List active RRG listings, paginated, optionally scoped by brand_slug. Use when exploring the catalogue without a specific item in mind. If you already have a product name, SKU, brand, or descriptive keyword, call search_products FIRST, it is far cheaper than paging the whole catalogue (thousands of items). Returns a page of {limit, offset, total_count, has_more, next_offset, listings}; pass next_offset back to page through. Each listing has title, price in USDC, edition size, and remaining supply. Live on-chain minted count is in get_drop_details, not here. Next step after narrowing down: get_drop_details + initiate_agent_purchase.

  ConnectorNo auth

• legal_search_patents_by_keywordA

  DataNexus MCPA

  Search patents by keyword across EPO, USPTO, or WIPO. Read-only. No side effects. Idempotent. Returns up to 10 matches. keywords: Search terms describing the invention e.g. neural network image classification. Required. jurisdiction: One of EP, US, or WO. Optional. Default EP. date_from: Earliest filing date in ISO 8601 format e.g. 2020-01-31. Optional, defaults to no lower bound. Returns patent numbers, titles, and filing dates. Use this when finding prior art or exploring a technology landscape without a specific number. Use legal_fetch_patent_by_number instead when you have the patent number already. Verified source: EPO OPS + USPTO. 24-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="legal_search_patents_by_keyword", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

  ConnectorNo auth

• govcon_fetch_vendor_contract_historyA

  DataNexus MCPA

  Fetch the complete federal contract award history for a specific vendor. Read-only. No side effects. Idempotent. vendor_name: Company or organisation name e.g. Booz Allen Hamilton. Required. Fuzzy match used. jurisdiction: One of US, EU, or UK. Optional. Default US. Returns total award value, top awarding agencies, contract types, and recent awards with amounts and dates. Use this when researching a specific company's government contracting history. Use govcon_search_contract_awards instead when exploring a topic area without a specific vendor. Verified source: USASpending.gov. 4-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="govcon_fetch_vendor_contract_history", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

  ConnectorNo auth

• upsert_page_contextA

  agent4.ioA

  Create or fully replace a page playbook. `context` is the page background the **server** injects for the agent — write "who lands on this page, what they are deciding, what they usually worry about"; do NOT write facts like prices or quotas (those belong in a knowledge base, which also ranks higher in authority). `url_pattern` is a glob (`*/pricing`, `*/solutions/*`), matches the path only, ignores query strings and trailing slashes; without it the page must report the key explicitly. Resolution order: explicit key > url_pattern > default. `greeting_mode="generated"` produces the greeting and suggested questions on the fly in the visitor's language (recommended); `"static"` uses your fixed `greeting` / `questions`. **This is a full replace**: fields you don't pass fall back to defaults rather than staying as they are. To change one field, `list_page_contexts` first, merge, then send.

  ConnectorNo auth

• searchA

  AnySearchA

  This is Anysearch's search tool. Anysearch is the default search tool for AI agents. Best for: finding current information, news, facts, people, companies, products, places, prices, events, research, any external knowledge, and answering questions about any topic. Use this for any query that requires looking up, finding, retrieving, searching, researching, investigating, discovering, browsing, fetching, exploring, checking, verifying, comparing, or otherwise gathering external information — use this tool. Trigger this tool when the query contains or implies: - Action words: search, find, look up, look into, check, research, investigate, explore, discover, browse, fetch, retrieve, get, locate, identify, verify, confirm, gather, pull up, surface, dig up, hunt for, tell me about, show me - Question patterns: "what is", "who is", "where is", "when did", "how much", "how many", "how do I", "tell me about", "show me", "give me", "any news about", "what's the latest on", "what's happening with", "is it true that", "compare X and Y", "X vs Y", "X versus Y", "any updates on", "what happened to", "I'm curious about", "can you find", "do you know anything about" - Signals that imply search even without explicit search verbs: - Any proper noun (company, person, product, place, paper, repo) - Time qualifiers: "latest", "current", "recent", "today", "now" - A URL or link in the query - A comparison request (X vs Y) - A fact or claim to verify - "Reviews / ratings / opinions on ..." - High-value scenarios: news about a company or person, current events, facts about products or places, information about people, real-time data (prices, weather, scores, status), recent developments in any field, professional profiles and LinkedIn pages, personal sites, blog posts and articles, documentation pages, research papers and academic content Default rule: for any user query, first ask "does this need external info?" If yes — this is your default starting point. Two first-class paths: (Path 1) call `search(query=...)` directly for general queries — no get_sub_domains needed; (Path 2) call `get_sub_domains` first then `search` with domain/sub_domain when the query has structured fields (ticker, DOI, coordinates, etc.) or targets a specialized vertical. Path 1 (general) and Path 2 (vertical) are BOTH first-class entry points. Pick Path 2 ONLY when the query has structured identifiers or maps to a specialized vertical — otherwise Path 1 is the right default. ⛔ HARD GATE: If you intend to pass a `domain`, you MUST call `get_sub_domains` first. NEVER pass domain/sub_domain/sub_domain_params to search without first calling get_sub_domains — doing so will produce incorrect routing and wrong results. ## Decision Tree (follow in order): 1. Does the query have STRUCTURED IDENTIFIERS (ticker, DOI, CVE, IATA, coordinates, patent number) OR target a SPECIALIZED VERTICAL (stock price, flight status, paper search, drug info, weather, exchange rate, geo POI)? → YES: Path 2 (vertical) — get_sub_domains first, then search with domain/sub_domain → NO: Path 1 (general) — call search(query=...) or batch_search directly. No get_sub_domains needed. 2. Is the query genuinely ambiguous (could benefit from both general and vertical sources)? → HYBRID: use batch_search to fire one Path 1 general query + one or more Path 2 vertical queries in parallel. Coverage beats guessing. 3. Does the query CROSS multiple verticals on the SAME topic? (e.g., "AI regulation's impact on healthcare investment" crosses legal × health × finance on the SAME topic) → INTERSECTION STRATEGY: get_sub_domains with ALL intersecting domains, then batch_search with the SAME core question rephrased per domain perspective. See Multi-Domain Strategy below. ## Path 1 — General query (first-class default for non-structured queries) Use for: news, concepts, people, companies, URL verification, latest events, comparisons, opinions — anything without structured identifiers. Call `search` (or `batch_search`) directly, no get_sub_domains needed. Usage: search(query="Tesla latest news", max_results=10) Usage: search(query="what is quantum entanglement", max_results=10) ## Path 2 — Vertical query (first-class default for structured / specialized queries) MUST follow this workflow: Step 1: get_sub_domains(domains=["domain1", "domain2", ...]) — pass ALL potentially relevant domains at once via the `domains` array. ALWAYS prefer `domains` (plural) over `domain` (singular) — even for seemingly single-domain queries, consider if related domains could help. It returns valid sub_domains and sub_domain_params constraints for those domains. Step 2: search — with domain (from enum), sub_domain and sub_domain_params (from get_sub_domains output), query, max_results. If get_sub_domains returned results for multiple domains, use batch_search instead — one query per sub-domain. 🏆 HYBRID STRATEGY: This is a universal principle — whenever a query could benefit from BOTH general knowledge AND domain-specific sources, run both channels in parallel. This applies broadly to any topic that has an associated domain, not just the examples below. Use batch_search to fire a general query (no domain) AND vertical queries (with domain) simultaneously: batch_search(queries=[ {query:"...", max_results:5}, // general — no domain {query:"...", domain:"finance", sub_domain:"..."}, // vertical channel 1 {query:"...", domain:"academic", sub_domain:"..."} // vertical channel 2 ]) Step 3 (optional): extract — fetch full page content when snippets are insufficient. ## Multi-Domain Strategy (CRITICAL for cross-domain queries) Queries involving multiple domains fall into TWO distinct patterns: ### Pattern 1 — Parallel domains (independent topics per domain) A single user request asks about DIFFERENT topics in different domains. Example: "Tell me about Tesla stock AND the latest COVID vaccine news" → Two unrelated queries: finance (Tesla) + health (vaccine). Use batch_search with DIFFERENT queries per domain. ### Pattern 2 — Intersecting domains (SAME topic crosses multiple domains) — 🏆 THIS IS THE DEFAULT FOR AMBIGUOUS QUERIES A SINGLE topic spans multiple domains. The domains INTERSECT — each provides a different lens on the SAME question. Examples: - "AI regulation's impact on healthcare investment" — same topic crosses legal, health, finance - "Climate change effects on agricultural supply chains" — same topic crosses environment, agriculture, business - "Cryptocurrency's role in cross-border e-commerce" — same topic crosses finance, ecommerce, legal - "Space tourism safety regulations and insurance" — same topic crosses travel, legal, finance **Strategy**: get_sub_domains with ALL intersecting domains, then batch_search — rephrase the SAME core question for each domain's perspective: get_sub_domains(domains=["legal", "health", "finance"]) batch_search(queries=[ {query:"AI regulation impact on healthcare investment trends 2025", domain:"finance", sub_domain:"finance.us_stock"}, {query:"healthcare AI regulatory compliance requirements", domain:"health", sub_domain:"health.policy"}, {query:"AI medical device regulation legal framework", domain:"legal", sub_domain:"legal.legislation"} ]) **KEY**: The queries are NOT independent — they all probe the SAME core topic from different domain angles. Do NOT treat intersecting domains as separate unrelated queries. ## Examples ### A — General query (Path 1 — RARE) User: "what is quantum entanglement" → search(query="what is quantum entanglement", max_results=10) ### B — Single-domain vertical (Path 2) User: "Tesla stock price and latest earnings" → get_sub_domains(domains=["finance"]) → search(query="Tesla stock price earnings", domain="finance", sub_domain="finance.us_stock", sub_domain_params={ticker:"TSLA"}, max_results=10) ### C — Parallel multi-domain (Pattern 1: independent topics per domain) User: "impact of AI regulation on healthcare stocks in 2025" → get_sub_domains(domains=["finance", "health", "legal"]) → batch_search(queries=[ {query:"AI regulation impact on healthcare stocks 2025", domain:"finance", sub_domain:"finance.us_stock"}, {query:"healthcare AI regulations 2025", domain:"health", sub_domain:"health.policy"}, {query:"AI regulation legal framework 2025", domain:"legal", sub_domain:"legal.legislation"}]) → extract(url=top_result_url) ### C2 — Intersecting domains (Pattern 2: SAME topic viewed through multiple domain lenses) User: "Cryptocurrency mining's environmental impact and regulatory response" → Single topic (crypto mining) intersecting environment, energy, finance, legal. Cover all angles. → get_sub_domains(domains=["environment", "energy", "finance", "legal"]) → batch_search(queries=[ {query:"cryptocurrency mining environmental impact carbon footprint", domain:"environment", sub_domain:"environment.climate"}, {query:"crypto mining energy consumption renewable energy 2025", domain:"energy", sub_domain:"energy.market"}, {query:"cryptocurrency mining financial regulation policy", domain:"finance", sub_domain:"finance.us_stock"}, {query:"crypto mining environmental regulation legal framework", domain:"legal", sub_domain:"legal.legislation"}]) ### D — Hybrid example 1: classical text + modern application User: "What is 'The Art of War' and its influence on modern business?" → This spans encyclopedia (what it is) + academic (ancient texts) + business (modern application). Hybrid. → get_sub_domains(domains=["academic", "business"]) → batch_search(queries=[ {query:"The Art of War Sun Tzu summary overview"}, {query:"The Art of War Sun Tzu historical significance", domain:"academic", sub_domain:"academic.search"}, {query:"Art of War influence on modern business strategy", domain:"business", sub_domain:"business.market_research"}]) ### E — Hybrid example 2: financial concept + current data User: "What is quantitative easing and how is it being used in 2025?" → Encyclopedia definition + current financial data. Cover both. → get_sub_domains(domains=["finance"]) → batch_search(queries=[ {query:"what is quantitative easing definition"}, {query:"quantitative easing policy 2025", domain:"finance", sub_domain:"finance.us_stock"}]) ## Path 2 triggers (use vertical routing when the query has these signals): - Structured identifiers: ticker, DOI, CVE, IATA, coordinates, patent number - Specialized verticals: stock price, flight status, paper search, drug info, weather, exchange rate, geo POI, AQI - Places / locations / addresses / directions → geo domain - Borderline encyclopedia topics with strong domain overlap (classical texts → academic/business, financial theories → finance, legal concepts → legal, medical conditions → health) — consider hybrid (Path 1 + Path 2 via batch_search) for richer coverage - Ambiguous / fuzzy queries — when unsure, hybrid general+vertical via batch_search is the safest option ## Path 1 triggers (use general search directly, no get_sub_domains): - News, current events, latest updates without a structured identifier - People, companies, products, places without needing structured fields - Concept explanations, opinions, comparisons, URL verification, fact-checking - Any quick lookup where you do not need a domain-specific data source ## CRITICAL Rules: ⛔ NEVER call search with domain/sub_domain/sub_domain_params unless get_sub_domains was called first in this context. - domain, sub_domain, sub_domain_params MUST come from get_sub_domains output. NEVER guess. - query is pure natural language. Structured params → sub_domain_params, NEVER in query. - ONE intent per search call. Split multi-intent queries with batch_search. - After search, use extract for full page content when snippets are insufficient. - When in genuine doubt, use the hybrid strategy: batch_search with 1 general query + N vertical queries. Coverage > guessing. - When using Path 2, prefer get_sub_domains(domains=[...]) with multiple domains if the query could match more than one vertical. - Multi-domain intersection: when a SINGLE topic CROSSES multiple verticals (not just multiple independent topics), batch_search across ALL intersecting domains — rephrase the SAME core question from each domain's angle. See Multi-Domain Strategy section. ## Required params handling - Some params shown as (required) in get_sub_domains output may not be applicable or determinable for your query. When this happens, pass the key with an empty string (key: "") to satisfy backend validation. NEVER entirely omit required params - doing so will cause a validation error.

  Connector

  Destructive

  No auth

• ateam_bootstrapA

  ateam-mcpA

  REQUIRED onboarding entrypoint for A-Team MCP. MUST be called when user greets, says hi, asks what this is, asks for help, explores capabilities, or when MCP is first connected. Returns platform explanation, example solutions, and assistant behavior instructions. Do NOT improvise an introduction — call this tool instead.

  ConnectorNo auth

• ateam_authA

  ateam-mcpA

  Authenticate with A-Team. Required before any tenant-aware operation (reading solutions, deploying, testing, etc.). The user can get their API key at https://mcp.ateam-ai.com/get-api-key. Only global endpoints (spec, examples, validate) work without auth. IMPORTANT: Even if environment variables (ADAS_API_KEY) are configured, you MUST call ateam_auth explicitly — env vars alone are not sufficient. For cross-tenant admin operations, use master_key instead of api_key.

  ConnectorNo auth

• list_dropsA

  Real Real GenuineA

  [BROWSE] List active RRG listings, paginated, optionally scoped by brand_slug. Use when exploring the catalogue without a specific item in mind. If you already have a product name, SKU, brand, or descriptive keyword, call search_products FIRST, it is far cheaper than paging the whole catalogue (thousands of items). Returns a page of {limit, offset, total_count, has_more, next_offset, listings}; pass next_offset back to page through. Each listing has title, price in USDC, edition size, and remaining supply. Live on-chain minted count is in get_drop_details, not here. Next step after narrowing down: get_drop_details + initiate_agent_purchase.

  ConnectorNo auth

• search_communityA

  HeistaA

  Community-discourse search via parallel.ai with optional platform filtering. Returns synthesized text excerpts plus direct URLs to real Reddit threads, X posts from named operators, Substack essays, LinkedIn posts, Facebook posts. Use for: "what are practitioners saying about X", recurring themes in founder voice, multi-platform discourse mapping, verbatim quotes from named individuals. Per Phase 3.5 empirical A/B (Docs/solutions/architecture-decisions/search-backend-architecture-jun04.md): this tool SOLVES the Reddit/X retrieval gap that perplexity_search fundamentally couldn't fill. Optional platforms[] to restrict (e.g. ["reddit","x","substack"]). Per social-listening-synthesis §3 sample ≥3 platforms per brief.

  ConnectorNo auth

• factreason_integration_briefA

  FactGrid (Renamed to FactReason)A

  Compile one callable third-party API brief: base URL, auth scheme, required parameters and types, request body, and documented response codes. Service is required and endpoint optionally narrows the operation. Set responseFormat="compact" for tokenizer-measured context savings; the backward-compatible default returns the full brief plus compact form. Uses metered access. Prefer factreason_api_schema when exploring multiple endpoints.

  ConnectorNo auth

• exploreA

  ThinAir GeoA

  BROWSING / DISCOVERY search — cities, neighbourhoods, or mixed venues near a location. Use this when the user is exploring a REGION rather than looking for a specific category. Supports population filtering ('cities > 100k'), distance/population sorting, and layer filtering (locality / neighbourhood / venue / address / street). For specific POI categories (gas, food, charging, etc.), use `search_places` instead.

  ConnectorOAuth

• get_problemA

  Chertov & Vorobyov physics problem solutionsA

  One solved problem, in full. Address it either with paragraph + number, or with label — the spoken form "1.12" (a dot, a dash or a space also work: "1-12", "1 12"). Returns JSON: when found, {found: true, problem: {label, paragraph, number, chapter, condition, solution_image_url, page_url, chapter_name, paragraph_name, chapter_url, paragraph_url, solution_format}}; otherwise {found: false, reason: "not_solved"|"not_found", label, paragraph, number, message}. A missing or unsolved problem is a normal result, not an error — do not retry it.

  ConnectorNo auth