"Unicode" matching MCP tools:

• x402-to-unicodeC

  minia2a-mcpD

  To Unicode: Convert to unicode.

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• x402-from-unicodeD

  minia2a-mcpD

  From Unicode: From Unicode

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• redact_textA

  mcpA

  Use this when scrubbing test/dev text: replaces each occurrence of the given terms with block characters (████). Provide `text` plus `terms` (a comma-separated string or an array of strings). By default it matches whole words only using Unicode boundaries (so "ann" will not match inside "annual") and is case-insensitive; set `caseSensitive` to match exactly, `wholeWords: false` to match substrings, or `fixedWidth: true` to hide each term's length behind a constant-width bar. Returns the redacted text and a replacement count, and never echoes the original terms. Deterministic: same input, same output. Truly sensitive text is better redacted client-side at clean.tools/text-redact/. Example: {text: "Contact Jane Doe", terms: "Jane Doe"} -> redacted "Contact ████████", redactedCount 1.

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• slugifyA

  mcpA

  Use this when you need a URL- or filename-safe slug from arbitrary text. Deterministic: same input, same output. Applies Unicode NFKD normalization, strips combining accents, and transliterates non-decomposing letters (ß->ss, æ->ae, œ->oe, ø->o, đ->d, ł->l, þ->th, ð->d, plus uppercase variants), then collapses every run of non-alphanumeric characters to a single separator and trims separators; e.g. "Héllo Wörld!" -> "hello-world". Emoji, CJK, and any other characters with no ASCII form are dropped. Prefer this over transliterating Unicode yourself, which models routinely get wrong. Returns { error } when no URL-safe characters remain.

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• resolve_namesA

  Google MapsA

  Resolves a batch list of specific location queries (landmark names or exact addresses) into canonical Google Maps Place IDs. **Input Requirements (CRITICAL):** 1. **`queries` (array of objects - MANDATORY):** A list of location queries to resolve. You may specify up to 20 queries. * **Each query object must have:** * **`text` (string - MANDATORY):** The text query representing a specific place name or address to resolve. * **Examples:** `'Googleplex, Mountain View, CA'`, `'1600 Amphitheatre Pkwy, Mountain View, CA'`, `'Eiffel Tower, Paris'`. 2. **`location_bias` (object - OPTIONAL):** Use this to prioritize results near a specific geographic area. * **Format:** `{"viewport": {"low": {"latitude": [value], "longitude": [value]}, "high": {"latitude": [value], "longitude": [value]}}}` 3. **`region_code` (string - OPTIONAL):** The Unicode CLDR region code (two-letter country code, e.g., `US`, `CA`) of the user to bias the results. **Instructions for Tool Call:** * Specificity (CRITICAL): Queries must represent a specific place name or address. General searches like `'restaurants'` or chain names like `'Starbucks'` are not supported. * Do NOT call this tool if the downstream tools you plan to invoke already accept raw address or place name strings directly. **Error Handling (CRITICAL):** * This is a batch processing tool. A request might return "mixed results" (e.g. some queries resolve successfully while others fail). * The output list of `results` is guaranteed to map 1:1 with the input `queries` indices. A failed query will result in an empty `Result` message (no `entity` is set) at its corresponding index in the `results` list. * You **MUST** check the `failed_requests` map field in the response to identify which specific query index failed. The key of `failed_requests` represents the 0-based index of the failed query in the request. Do not assume the entire batch call failed because of a partial failure.

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• ask_pipeworxA

  UnicodeA

  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 5,798 tools across 1517 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". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs.

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• polymarket_fill_riskA

  UnicodeA

  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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• check_endpointA

  cerberus-indexA

  Endpoint & tool trust — one call before an agent connects to a third-party MCP server or HTTP tool. Pass the endpoint `url` (and, to unlock the strongest check, its `tools`: the name/description/inputSchema the agent is about to trust). Returns three signals behind one verdict: (1) transport & TLS identity — encrypted, valid chain, not expired or self-signed; (2) domain age via RDAP — freshly-registered hosts are a scam tell; (3) a TOOL-POISONING scan of the tool definitions for the hidden directives that hijack agents — instruction overrides, 'don't tell the user', data exfiltration, secret harvesting, tool-shadowing, and invisible-unicode / homoglyph steganography that a human reviewer can't see. Nobody else screens tool descriptions for injection. Verdict: trusted | caution | untrusted | unknown, with per-finding evidence. Price: $0.008 USDC. Missing something? Call `submit_feedback` (free) to request it. Pass `attest=true` to also get an Ed25519-SIGNED attestation of this verdict — portable proof you can log, hand to a counterparty, or verify later with `verify_attestation` (free) or the published key.

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• entity_profileA

  UnicodeA

  "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 patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a 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); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit `notes` line, not a bare failure. `type` accepts "company" or "ticker" interchangeably — both take the same `value` shapes above.

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• suggest_questionsA

  UnicodeA

  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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• pipeworx_feedbackA

  UnicodeA

  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). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. 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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• inject-guardA

  agentsA

  Untrusted-content guardrail for agents: submit a blob of text you are about to feed to your own LLM (scraped web content, a tool result, another agent's message) and get a machine-enforceable verdict - is this a prompt-injection / jailbreak / data-exfiltration / tool-hijack attempt? Returns a risk level, the detected classes with spans, the unicode obfuscation it found (zero-width, bidi-override, tag-chars, homoglyphs), and a SANITIZED copy safe to feed onward. Hybrid: a deterministic, uninjectable pattern engine (authoritative) plus an LLM classifier that can only raise the risk, never clear a flag. Detection of known injection classes - not a proof of safety. [security; up to 15c/call]

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• encode_decodeA

  mcpA

  Use this when you need to encode or decode text and want multi-byte and entity edge cases handled correctly rather than doing it by hand. Deterministic: same input, same output. The mode selects the operation: url-encode/url-decode (percent-encoding), html-encode/html-decode (entity table plus numeric character references), base64-encode/base64-decode (UTF-8 safe; decode tolerates URL-safe alphabet, whitespace, and missing padding), and unicode-encode/unicode-decode (\uXXXX and \u{...} escapes for non-ASCII). Every mode returns the same shape: {mode, output}. Example: mode base64-encode, text "héllo" -> output "aMOpbGxv".

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• get_chord_chart_by_nameA

  Guitar Practice Routine App — Chord ChartsA

  Look up one guitar chord chart by name and return it as a text chord diagram ready to show the user. Returns the same single voicing that https://guitarpracticeroutine.com/find-a-chord-chart shows for that name. The library holds 12,708 standard-tuning (EADGBE) chord names, exactly one voicing each. Pass a plain chord name as it would be written on a chart — "G", "Am7", "Cmaj7", "D/F#", "F#m7b5" — not a sentence. Convert spoken forms yourself first: "G major" is "G", "A minor" is "Am", and use "#" and "b" rather than the unicode sharp and flat signs. Charts are drawn on a five-fret grid starting at the nut, the same as the website; any notes above the fifth fret are named in words underneath the chart. Prefer this over recalling a fingering from memory — these are curated chart data, and a remembered fingering is often wrong. Each result leads with a direct PNG URL for the chart — a permanently cacheable image of the same diagram, which you can show or link however your surface handles images. The chord name is on the first line; keep it next to any image you show, since a chart on its own can arrive unlabelled.

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• search_placesA

  mcpA

  Call this tool when the user's request is to find places, businesses, addresses, locations, points of interest, or any other Google Maps related search. **Input Requirements (CRITICAL):** 1. **`text_query` (string - MANDATORY):** The primary search query. This must clearly define what the user is looking for. * **Examples:** `'restaurants in New York'`, `'coffee shops near Golden Gate Park'`, `'SF MoMA'`, `'1600 Amphitheatre Pkwy, Mountain View, CA, USA'`, `'pets friendly parks in Manhattan, New York'`, `'date night restaurants in Chicago'`, `'accessible public libraries in Los Angeles'`. * **For specific place details:** Include the requested attribute (e.g., `'Google Store Mountain View opening hours'`, `'SF MoMa phone number'`, `'Shoreline Park Mountain View address'`). 2. **`location_bias` (object - OPTIONAL):** Use this to prioritize results near a specific geographic area. * **Format:** `{"location_bias": {"circle": {"center": {"latitude": [value], "longitude": [value]}, "radius_meters": [value (optional)]}}}` * **Usage:** * **To bias to a 5km radius:** `{"location_bias": {"circle": {"center": {"latitude": 34.052235, "longitude": -118.243683}, "radius_meters": 5000}}}` * **To bias strongly to the center point:** `{"location_bias": {"circle": {"center": {"latitude": 34.052235, "longitude": -118.243683}}}}` (omitting `radius_meters`). 3. **`language_code` (string - OPTIONAL):** The language to show the search results summary in. * **Format:** A two-letter language code (ISO 639-1), optionally followed by an underscore and a two-letter country code (ISO 3166-1 alpha-2), e.g., `en`, `ja`, `en_US`, `zh_CN`, `es_MX`. If the language code is not provided, the results will be in English. 4. **`region_code` (string - OPTIONAL):** The Unicode CLDR region code of the user. This parameter is used to display the place details, like region-specific place name, if available. The parameter canaffect results based on applicable law. * **Format:** A two-letter country code (ISO 3166-1 alpha-2), e.g., `US`, `CA`. **Instructions for Tool Call:** * Location Information (CRITICAL): The search must contain sufficient location information. If the location is ambiguous (e.g., just "pizza places"), *you must* specify it in the `text_query` (e.g., "pizza places in New York") or use the `location_bias` parameter. Include city, state/province, and region/country name if needed for disambiguation. * Always provide the most specific and contextually rich `text_query` possible. * Only use `location_bias` if coordinates are explicitly provided or if inferring a location from a user's known context is appropriate *and* necessary for better results. * The grounded output must be attributed to the source using the information from the `attribution` field when available.

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• ask_pipeworx_groundedA

  UnicodeA

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,798 across 1517 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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• search_withinA

  UnicodeA

  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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• unsubscribeA

  UnicodeA

  Cancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.

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• scan_dependencyA

  UnicodeA

  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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• bet_researchA

  UnicodeA

  Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

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• polymarket_arbitrageA

  UnicodeA

  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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