"Spring Boot" matching MCP tools:

• entity_profile

  String Db

  "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

  String Db

  "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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• compare_entities

  String Db

  "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

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• get_world_cup_odds

  voxodds

  Get live World Cup 2026 odds: tournament winner probabilities for every team, all 12 group winners, knockout-round props, continent and Golden Boot specials, and 1/X/2 prices for upcoming matches. Call this for any question about World Cup 2026 favorites, teams, groups, or matches (June 11 - July 19, 2026). Updated every 10 minutes from prediction markets with $1.8B+ traded.

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• suggest_questions

  String Db

  What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

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• scan_competitor_ai_presence

  String Db

  Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

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• emem_memory_search

  emem — Earth memory protocol

  Semantic search over /memories/* file contents using BGE-base-en-v1.5 (768-D, L2-normalised) backed by a Lance partition (`memory_text_index_d768.lance`). Matches paraphrases — "rainfall in March" finds "precipitation observed in spring" without an exact substring match. Returns ranked hits with similarity in [0,1], 200-char snippets around the best-matching chunk, and the signing receipt's path / file_cid / signed_at / attester_pubkey_b32 fields. Filters: `kind`, `path_prefix`, `attester_pubkey_b32`. Falls back to a brute-force scan (slower but correct) when the index is empty or `EMEM_DISABLE_LANCE=1` is set; the `via` field of the response reports which path was taken. When to use: Call instead of paging through `memory_view` whenever the agent knows roughly what it wants (a topic, a name, a paraphrase) but not the exact file path. Pair with `memory_view` for the full body once you've narrowed down the candidate — `emem_memory_search` returns a 200-char snippet, not the whole file. The polling indexer hydrates once per minute (configurable via `EMEM_MEMORY_SEARCH_POLL_SECS`), so a file created in the same turn may briefly miss the fast-path — the brute-force fallback still catches it.

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• chieflab_status

  ChiefLab

  SESSION-RECOVERY · FIRST CALL when a session starts and the user mentions launch / users / growth / customers / metrics / revenue / marketing / what next / shipping. Returns a command-center bootCard with `headline`, `priority`, `cards[]` (each carries kind + label + literal user command + runHandle), and `next` (the one-line prompt). Aggregates: pending approvals + ripe measurements + new engagement + queued prospects + recent launches + manual-publish-pending actions. ChiefLab is stateful and re-summonable — even if the conversation was lost, the IDE was switched, or the runId was forgotten, this call recovers the workspace business state. If the user asked to launch the CURRENT repo, compare boot cards to currentRepoContext/projectName; if the open loop is unrelated, start a fresh launch instead of resuming stale work.

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• ask_pipeworx

  String Db

  PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 3,683 tools across 865 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1".

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• setup_agent_fleet

  mcp

  Complete one-shot setup: validates prerequisites, creates a controller VM + worker VMs, auto-creates a public HTTPS URL on port 7070, seeds a starter ROADMAP.md into the repo if absent, and returns the trigger token. Call this when a user says 'set up autocoding agents for my repo' or 'I want agents to work on my codebase'. HOW THE AGENT WORKS: each worker runs Claude Code inside the repo, implements one task, runs the test suite, and opens a pull request. It excels at focused, single-PR, testable units of work — add an endpoint, write tests for a module, fix a specific bug, add a UI page — and is poor at vague/large tasks, design decisions, or anything needing external credentials. TASK FORMAT (strict, one line each): `- [ ] **Title** — short description *(agent-ready)*` — the `- [ ]` checkbox, `**bold title**`, ` — ` separator, and `*(agent-ready)*` are ALL required; `##` headings and plain bullets are ignored. After this returns, the user needs to: (1) authorize the fleet by running the authorize.sh one-liner it returns (it runs `claude setup-token` for a long-lived token installed on the controller) — agents use the user's existing Claude Max/Pro subscription, NOT an API key. This is a shell command the USER runs in their own terminal; do NOT try to read or push the user's credentials yourself. The controller takes ~7 min to boot, so PREFER to poll get_agent_status until it reports the controller is reachable and present the authorize command only once it's ready — that way the user doesn't run it into a long wait. (The command also waits on its own, showing a live progress counter, so a user who runs it early is fine too.) (2) add well-scoped tasks in the format above to ROADMAP.md; (3) call trigger_agent_batch.

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• polymarket_kalshi_spread

  String Db

  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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• list_plans

  EQVPS — pay-per-use VPS for AI agents (API + MCP)

  List all available VPS plans (catalog) with pricing, specs and the OS images each plan can boot. No authentication needed. Use this first to pick a `product` slug and an `os_id` for `order_vps`.

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• subscribe

  String Db

  Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"you@x.com"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).

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• polymarket_arbitrage

  String Db

  REQUIRES one of `event` (single-event mode) OR `topic` (cross-event mode) — call with no args fails. Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. `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}.

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• get_my_schedule

  ssuMCP

  Returns the authenticated student's u-SAINT timetable grouped by course. Without year and term it returns the current u-SAINT selected semester; pass both year and term to fetch a specific semester. Term values: 1=spring, 2=summer, 3=fall, 4=winter. Requires mcp_session_id with the SAINT provider linked via start_auth. Returns AUTH_REQUIRED with a loginUrl if SAINT is not authenticated — show the loginUrl to the user and ask them to open it in a browser, then retry this call with the returned mcp_session_id.

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• ai_visibility_check

  String Db

  Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

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• image_personal

  Colour Memory

  Upload a portrait photo and receive a full personal colour analysis. Determines your seasonal type (Spring, Summer, Autumn, or Winter), colour depth (light, medium, or deep), and undertone (warm, cool, or neutral). Returns a curated palette of archive colours that genuinely suit you — each with full historical provenance and cultural context — plus colours to avoid. Uses Claude Vision for skin, hair, and eye analysis, then matches to the archive by CIEDE2000 perceptual distance. The photo is never stored. Example: a Deep Winter might wear Ottoman Carbon Ink while a True Spring suits Kogi Mango.

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• devloop_switch_to_sandbox

  Keploy

  Switch the app's V1 CI from "boot the real app + deps" mode to sandbox mode (mocks fetched by content-hash from the cloud canonical pool). The doc-stated trigger: ~1 week after CI is wired, when the dev has felt the slow runs / flakes and you can pitch "your CI takes 90s and flaked twice this week — rerecord mocks and CI drops to ~8s." What flips: * The CI workflow YAML gets a --sandbox flag on `keploy test-gen run` and the docker-compose-up step removed. This tool returns the updated YAML; you re-PR it. Pre-condition: every resource you want in CI must have recorded mocks (config.yaml.mockRegistry.mock populated). Resources without mocks will fail in sandbox mode because there's nothing to serve. Run devloop_record_sandbox per resource first; verify via devloop_schema_drift_report-style checks before proposing the switch.

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• ask_pipeworx_grounded

  String Db

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 3,683 across 865 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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• scan_dependency

  String Db

  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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