"namespace:ai.ai-portal" matching MCP tools:

• submit_correction

  Namaste America

  Propose a correction to a listing field (agents/users reporting bad data). `vertical` is the category key (default 'restaurants'); `listing_id` is the id from that category's get_/search_ tools. Correctable restaurant fields: phone, email, website, menu_url, address_full, city, state, region_tag, price_range, cuisine_type, festival_specials. The correction is stored and applied by the Feedback agent — automatically for unclaimed listings, or routed to a human for claimed/featured ones. Identity fields (name, coordinates) are not correctable. Structured field corrections currently apply to restaurants; for other categories use submit_review to flag issues (or the owner portal). Returns {ok, feedback_id|error}.

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• scan_dependency

  Complex Portal

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

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• polymarket_arbitrage

  Complex Portal

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• pipeworx_feedback

  Complex Portal

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

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• polymarket_fill_risk

  Complex Portal

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

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• ibge_cidades

  IBGE Brasil MCP

  Queries municipal indicators from IBGE (similar to Cidades@ portal). Features: - General overview of a municipality (population, HDI, GDP, etc.) - Query specific indicators - Historical indicator data over years - List available surveys and indicators Available indicators: populacao, area, densidade, pib_per_capita, idh, escolarizacao, mortalidade, salario_medio, receitas, despesas Examples: - São Paulo overview: tipo="panorama", municipio="3550308" - Population history: tipo="historico", municipio="3550308", indicador="populacao" - View surveys: tipo="pesquisas" - Available indicators: tipo="indicador" This tool is the panel for a SINGLE municipality (Cidades@). Use a different tool when: - Real-time Brazil population → ibge_populacao - Census themes / historical series → ibge_censo - Comparing multiple municipalities → ibge_comparar - A macro indicator time series → ibge_indicadores Behavior: read-only and idempotent — a live GET against the public IBGE APIs (Cidades@/agregados). Returns Markdown plus a typed structuredContent payload.

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• entity_profile

  Complex Portal

  "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

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• recent_changes

  Complex Portal

  "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

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• deep_research

  Complex Portal

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1293 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 4,927 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• portal_list_networks

  SQD Portal

  Find the right network or chain name to use across EVM, Solana, Bitcoin, Substrate, and Hyperliquid. COMMON USER ASKS: - Find Base-like networks - Show Solana mainnets - Show Substrate mainnets FIRST CHOICE FOR: - finding the correct network before any other query WHEN TO USE: - You are not sure which network name, chain name, or alias to use. - You want to filter networks by VM family, network type, or real-time availability. DON'T USE: - You already know the exact network and want live data from that network. EXAMPLES: - Find Base-like networks: {"query":"base","limit":10} - Show Solana mainnets: {"vm":"solana","network_type":"mainnet"} - Show Substrate mainnets: {"vm":"substrate","network_type":"mainnet"}

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• get_buyer_account

  Opedd — Licensed Content for AI

  Fetch the authenticated buyer's account profile + masked API key list via GET /buyer-account. Returns the enterprise_buyers row (contact_email, buyer_org, created_at, etc.) plus a list of all buyer-side API keys with masked prefixes (NEVER plaintext post-issuance — only the 12-char key_prefix is returned, e.g. 'opedd_buyer_'). Use cases: post-signup verification ('what was just issued to me?'), buyer dashboard mental model ('what licenses do I currently hold?'), audit prep ('show me the key list before rotation'). For full mid-lifecycle license details (filter_rules, billing, payouts), buyers consult the buyer portal at opedd.com/buyer. Requires OPEDD_BUYER_JWT.

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• search_datasets

  Nova Scotia Data Explorer

  Search the Nova Scotia Open Data catalog (data.novascotia.ca) for datasets by keyword, category, or tag. Returns dataset names, IDs, descriptions, column names, and direct portal links. Use list_categories first to see valid category and tag names. Use the returned dataset ID with query_dataset or get_dataset_metadata for further exploration.

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• search_patents

  patents

  Search USPTO patent applications and grants. Use `query` for free-text keywords ("lithium battery", "crispr"). Optional structured filters: `applicant` (company name — use ALL CAPS like "APPLE INC." for best match), `filed_after` / `filed_before` (filing date range), `granted_after` / `granted_before` (grant date range). Results include title, application number, filing date, first applicant, all applicants, inventors, status, classification. Note: ODP filtering is approximate (weighted match, not strict equality) — counts and ordering are best-effort. Powered by the USPTO Open Data Portal (data.uspto.gov).

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• list_categories

  opendata-cat

  List all dataset categories and themes with counts per portal. Great first step to discover what data types are available before searching with search_datasets. Returns total datasets, count per portal and category list with counts. No parameters required.

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• ambr_create_contract

  Ambr

  Generate a Ricardian Contract from a template. Creates a dual-format contract (human-readable legal text + machine-parsable JSON) using AI, linked by SHA-256 hash. The contract is stored on Ambr and accessible via the Reader Portal. Requires a valid API key (X-API-Key header on the HTTP request) with available credits. Use ambr_list_templates first to discover templates and their required parameters. Args: - template (string, required): Template slug (e.g. "c1-agent-delegation") - parameters (object, required): Template-specific parameters matching the schema - principal_declaration (object, required): { agent_id, principal_name, principal_type } - parent_contract_hash (string, optional): SHA-256 hash of parent contract for amendments - amendment_type (string, optional): "original" | "amendment" | "extension" Returns: - contract_id: Unique ID (e.g. "amb-2026-0042") - sha256_hash: SHA-256 hash for verification - status: Contract status - reader_url: URL to view in Reader Portal - credits_remaining: Remaining API credits Legibility: Output is dual-format by construction and replayable to the original SHA-256 hash — the basis of Ambr's legibility guarantee.

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• check_signup_status

  Utilify

  Check where a previously-started Utilify signup stands — use when the user asks whether their enrollment went through. Use when the user says 'did my electricity signup go through', 'is my power on for move-in day yet', or 'what's the status of the enrollment we started'. Returns current status (pending, confirmed, failed) plus any next-step instructions from the provider. Requires a signup_id from a prior initiate_signup call; if the user doesn't have one (asks status without ever signing up), tell them no enrollment exists and offer to start one. If status is 'pending' for >48h or 'failed', recommend the $49 concierge at https://utilify.io/concierge to take it over rather than guessing at the provider's own portal.

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• socrata_query_dataset

  socrata-mcp-server

  Execute a SoQL query against any dataset on any Socrata portal. Use the search parameter for quick full-text lookup, or combine select/where/group/having/order for full analytical control. Returns rows plus the assembled SoQL string so you can learn the pattern. All SODA 2.1 row values are strings even for numeric columns — check dataType from socrata_get_dataset to determine correct WHERE quoting: Number columns use bare literals (year=2023), Text columns use single-quoted strings (year='2023'). To enumerate distinct values, use select="col, count(*) as n" with group="col" and order="n DESC". When CANVAS_PROVIDER_TYPE=duckdb and rows fill the limit, results spill to a DataCanvas table for SQL-based analysis.

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• socrata_list_portals

  socrata-mcp-server

  List known Socrata-powered government open-data portals with their domain, organization name, and approximate dataset count. The catalog is a curated list of 40 well-known portals; dataset counts are fetched from the Discovery API and cached for ~24 hours. Filtering is client-side substring match on the query parameter. Use this first when you do not know which portal to target, then pass the domain to socrata_find_datasets.

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• billing.get_portal

  Admin Substitute

  Get a Stripe Billing Portal URL for the human to manage their subscription — update payment methods, view invoices, change plans, or cancel. Requires an existing Stripe subscription.

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• search_within

  Complex Portal

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

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