"Coca-Cola" matching MCP tools:

• search_companies

  MeritValue

  Resolve a company name or partial ticker to covered companies — 'coca cola' finds KO. Use this before the other tools when you have a name rather than a symbol. Coverage is US filers with SEC filings.

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• deep_research

  Data Nola

  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 1393 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 5,334 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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• entity_profile

  Data Nola

  "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

  Data Nola

  "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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• remember

  Data Nola

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• infrastructure_status

  agent-cold-email

  Warmup + provisioning progress per mailbox. New mailboxes are ramp-limited server-side: 5 sends/day week 1 rising to 40/day after 4 weeks; current dailyCap for each mailbox is in the response below. Returns { domains, mailboxes, sendReady, mailboxHealth[] }; each mailbox: warmupDay, dailyCap, sentToday, sendReady, delivStatus (healthy/throttled/paused), complaint/bounce/softBounce rates (first-party measured), vendorReputationScore + vendorPlacementRate (VENDOR-REPORTED approximations, not first-party measurements — the control loop uses local signals only), lastPolledAt. Use account/metrics for account-wide rollups.

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• campaign_results

  agent-cold-email

  Outcome counts for ONE campaign. Input: campaignId (from launch_campaign). Returns { campaignId, sent, reply, bounce, complaint, unsubscribe, failed, soft_bounce } — bounce = HARD only, soft_bounce separate, opens not tracked. 404 if unknown. Use metrics for account-wide totals, list_campaigns for every campaign at once.

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• metrics

  agent-cold-email

  Account-wide outcome totals across ALL campaigns: { sent, reply, bounce, complaint, unsubscribe, failed, soft_bounce } — same shape as campaign_results but summed tenant-wide (bounce = hard only, opens not tracked). Use campaign_results for one campaign, list_campaigns per-campaign, or account for billing/quota.

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• remove_mailboxes

  agent-cold-email

  Downgrade: release your N NEWEST live mailboxes now and lower the billed quantity. Inputs: count, acknowledged (must be true — this is a quoted, irreversible-this-cycle consent: the release is immediate for provisioning but there is NO mid-cycle credit; the lower price takes effect next renewal, minimum 5 mailboxes / $99). Returns { releasedCount, quote } where quote is the new projected monthly. To ADD mailboxes use setup_infrastructure / configure_byo_domain (request_managed_mailboxes).

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• configure_dashboard

  agent-cold-email

  Write a saved dashboard view. action = create (needs name+layout) | update (needs id+rev+layout; optional name renames) | promote (id → default) | delete (id). update is rev-CAS: a stale rev returns { currentRev, currentLayout } to rebase and retry. Optional note. Read the current rev+layout via get_dashboard first.

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• suppress_lead

  agent-cold-email

  Permanently suppress an email address tenant-wide (every current and future campaign) — the manual/free-text 'stop emailing me' path for opt-outs the strict typed-unsubscribe matcher misses. Inputs: email, reason (fixed 'manual' — the only value this tool honestly claims; bounce/complaint/unsubscribe are recorded automatically elsewhere), note (accepted, not persisted). Cancels every pending send + marks every campaign-lead row 'suppressed'. Last-write-wins: re-suppressing a bounce/complaint/unsubscribe row relabels its reason to 'manual'. There is no un-suppress tool.

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• pay_merchant

  Coal — Payments for AI agents

  Send USDC on Base to any merchant payout address (the `payoutAddress` field shown by discover_merchants — NOT the merchantId). Use this whenever the user has confirmed they want to buy, purchase, pay, or send money for something. Gasless for you — Coal pays gas. Returns the on-chain tx hash. Auto-uses the wallet key from the X-Coal-Agent-Key header in your Claude config (no need to ask the user for a key). Max $5 per tx. After this succeeds for a digital product, immediately call download_product with the returned tx hash to give the user their file.

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• recent_alerts

  Data Nola

  Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.

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• scan_dependency

  Data Nola

  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

  Data Nola

  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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• polymarket_kalshi_spread

  Data Nola

  Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning fires in two cases — (a) matched_pairs:0 with skipped_cross_type>0 means the venues frame the topic with non-equivalent bet shapes (e.g. Kalshi range_bucket point-in-time vs Polymarket cumulative_threshold touch-anywhere — no arb exists), (b) matched_pairs:0 with skipped_cross_type:0 and both venues >5 legs means the token-overlap matcher found nothing in common — events likely semantically unrelated despite the topic keyword. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period; aligned:false means spreads are mathematically meaningless across the temporal gap. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.

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• polymarket_fill_risk

  Data Nola

  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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• activity

  agent-cold-email

  Unified activity feed: campaign events (sent/reply/bounce/...) merged with deliverability loop actions (pause/throttle/replace-domain). Cursor-paginated → { items[], nextCursor }; each item { id, kind:'event'|'deliverability', label, ts, target, detail }. Filters: kind, limit (default 50, max 200). Use inbox for replies only.

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• list_leads

  agent-cold-email

  List/export leads with their contact-level disposition, cursor-paginated. Returns { leads[], nextCursor }; each row: leadId, email, firstName, company, campaignId, campaignName, globalStatus, interestStatus, notes, tags, suppressed, lastEventType, lastEventTs, createdAt. Filters: campaign, interestStatus, suppressed, replied. This IS the export surface — paginate to dump the full book of business as JSON (no separate CSV endpoint). Use update_lead to write disposition, suppress_lead to opt an address out.

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• ask_pipeworx_grounded

  Data Nola

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,334 across 1393 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

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