"Zend" matching MCP tools:

• remember

  Send That Email

  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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• compare_entities

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  "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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• recent_changes

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  "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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• linux_os

  ZEN SecDB

  List supported Linux operating systems and their corresponding versions for use with the `linux_audit` tool. ## What this tool does Returns an array of supported OS/version pairs, each in the form: {"os":"name", "versions":["version or codename"]} This allows the LLM and the user to know exactly which inputs are valid for the `linux_audit` tool. ## When to use this tool Use this tool when: - the user does not know which OS names or versions are supported - the user provides unclear or ambiguous OS information - you need to validate `os`/`version` before performing a Linux audit This tool should typically be called **before `linux_audit`** whenever parameters are uncertain. ## Inputs This tool does not require any input. ## Outputs Returns an array of objects: - **os**: supported Linux distribution identifier - **versions**: corresponding list of supported release or codename Example: [ {"os": "ubuntu", "versions": ["noble","focal"]}, {"os": "debian", "versions": ["bookworm","sid"]}, {"os": "redhat", "version": ["redhat-9.0"]} ] ## LLM usage guidelines - Use this tool to validate or suggest correct OS/version combinations before calling `linux_audit`. - If the user provides invalid or misspelled OS names, retrieve the official list here and ask them to select one. - Do not guess operating system identifiers-always rely on this tool to confirm correctness.

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• pipeworx_feedback

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  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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• entity_profile

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  "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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• polymarket_kalshi_spread

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  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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• scan_dependency

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  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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• deep_research

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  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 1354 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 5,141 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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• polymarket_fill_risk

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  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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• validate_claim

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  "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), the grounded or structured actual value with pipeworx:// citation, and reasoning. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

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• polymarket_arbitrage

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  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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• send

  Apuchat

  Send a message to another agent on the channel you joined, or to 'all' to broadcast. Requires a prior join() in this session. The 'to' field accepts: a callsign ('front'), an index ('#1' or '1') from roster(), or 'all'. If omitted, defaults to 'all' (broadcast — walkie-talkie default). Optional `priority` tags urgency (min|low|default|high|urgent). Optional `suggested_replies` hints up to 4 canned replies that human-in-the-loop UIs (like the /remote phone view) render as tappable chips — agent receivers can read them too and pick one. Optional `attachments` carries up to 4 small inline files (≤512KB base64 total) — designed for sporadic screenshots / PDFs; bigger files should be hosted externally and pasted as a URL. Optional `kind`: set 'status' to send an ephemeral 'working on it' signal instead of a normal message (see the `kind` field).

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• pipeworx_trending

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  What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

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• linux_audit

  ZEN SecDB

  Perform a Linux package vulnerability audit using SecDB. ## What this tool does Analyzes the installed packages of a Linux system-identified by OS and OS version-and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided by the user. ## When to use this tool Use this tool when the user wants to determine: - whether installed packages contain known vulnerabilities - whether a host, VM, container, or base image is affected by security advisories - which packages require patching or upgrading If the user does not know the valid values for `os` or `version`, first call the `linux_os` tool to retrieve the exact supported combinations. ## Inputs - **os**: Linux distribution identifier supported by SecDB (use `linux_os` to obtain allowed values). - **version**: OS version or codename corresponding to the selected distribution. - **packages**: list of installed packages, **one per line**, generated using the appropriate system command: ### For RPM-based distributions (RHEL, CentOS, Rocky, Alma, SUSE) rpm -qa --qf '%{NAME}-%{VERSION}-%{RELEASE}.%{ARCH}\n' ### For DEB-based distributions (Ubuntu, Debian) dpkg-query -W -f='${Package} ${Version} ${Architecture}\n' ### For Alpine Linux apk list -I The raw output of these commands can be passed directly as the `packages` input (one package per line). ... python3 3.12.3-0ubuntu2.1 amd64 systemd 255.4-1ubuntu8.10 amd64 tmux 3.4-1ubuntu0.1 amd64 ... ## Outputs - **report**: structured objects describing the advisories affecting the audited packages. - **summary**: Markdown summary including total vulnerabilities, severity breakdown, and key findings. ## LLM usage guidelines - Never guess whether a package is vulnerable-always call this tool for Linux audits. - If `os` or `version` is unclear or missing, call `linux_os` and ask the user to choose a valid combination. - Normalize the package list to “one entry per line” if the user provides unstructured output. - The `summary` is already Markdown and can be shown directly. - Use `report` when deeper technical analysis is required.

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• ssvc_calculator

  ZEN SecDB

  Compute CISA SSVC (Stakeholder-Specific Vulnerability Categorization) for a CVE. ## What this tool does Calculates the SSVC decision (Track, Track*, Attend, Act) using: - exploitation status - technical impact - automatable exploitation - mission prevalence (user-provided) - public well-being impact (user-provided) This reflects CISA's official SSVC prioritization model. ## When to use this tool Use this tool when the user asks about: - how urgently a CVE should be remediated - CISA SSVC priority or risk category - a structured decision model for remediation ## Inputs - **cve_id**: the vulnerability to evaluate (`CVE-YYYY-NNNNN`) - **mission_prevalence**: `M`, `S`, or `E` (must be provided by the user) - **public_well_being_impact**: `M`, `A`, or `I` (must be provided by the user) ## Outputs - `decision`: one of **Track**, **Track\***, **Attend**, **Act** - `exploitation` - `technical_impact` - `automatable` - `mission_prevalence` - `public_well_being_impact` - `mission_and_well_being_impact_value` - `vector_string` - `summary`: Markdown explanation of the outcome ## LLM usage guidelines - Always ask the user for **mission_prevalence** (M/S/E) and **public_well_being_impact** (M/A/I) before calling. - Never guess these values—SSVC depends on user context. - Use the `summary` to explain clearly why the decision was returned. - Combine with `vulnerability_score` or `sightings_search` if the user needs additional context.

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• vulnerability_score

  ZEN SecDB

  Get CVSS and current EPSS score for a specific CVE. ## What this tool does Returns a full risk snapshot for a CVE, including: - CVSS version - CVSS base score - CVSS severity - CVSS vector string - human-readable explanation of the CVSS vector - current EPSS score The field **`cvss_explain`** provides a natural-language interpretation of the CVSS vector (attack conditions, privileges, user interaction, impact breakdown). Example: For `CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H`, the explanation may read: *"The vulnerability can be exploited remotely over the network with low complexity, without authentication and without user interaction. Exploitation may lead to high impact on confidentiality, high impact on integrity, and high impact on availability."* ## When to use this tool Use this tool when the user asks: - "What is the CVSS/EPSS of this CVE?" - "Explain the CVSS vector of this vulnerability." - "What is the severity and why?" - "Give me the risk profile for this CVE." For EPSS historical trends, use `epss_timeseries`. ## Inputs - **cve_id**: valid CVE identifier (`CVE-YYYY-NNNNN`). ## Outputs - `cvss_version` - `cvss_base_score` - `cvss_base_severity` - `cvss_vector_string` - `cvss_explain` - human-readable explanation of the CVSS vector - `epss_score` ## LLM usage guidelines - Never guess CVSS or EPSS values—always call this tool. - Use the `cvss_explain` field directly when the user wants an interpretation of the vector string. - If multiple CVEs are referenced, call the tool once per CVE. - Combine this tool with `sightings_search` or `ssvc_calculator` for more complete risk assessments.

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• purl_audit

  ZEN SecDB

  Perform a software package vulnerability audit using SecDB. ## What this tool does Analyzes a list of software packages identified by PURL (Package URL) and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided. ## When to use this tool Use this tool when the user wants to determine: - whether application dependencies contain known vulnerabilities - whether a project is affected by security advisories - which packages require patching or upgrading ## Supported ecosystems - **npm** - Node.js packages (e.g. pkg:npm/lodash@4.17.21) - **maven** - Java/JVM packages (e.g. pkg:maven/org.apache.logging.log4j/log4j-core@2.14.1) - **pypi** - Python packages (e.g. pkg:pypi/django@4.2.0) - **gem** - Ruby gems (e.g. pkg:gem/rails@7.0.0) - **cargo** - Rust crates (e.g. pkg:cargo/openssl-src@111.10) - **nuget** - .NET packages (e.g. pkg:nuget/Newtonsoft.Json@13.0.1) - **golang** - Go modules (e.g. pkg:golang/github.com/gin-gonic/gin@1.9.1) - **composer** - PHP packages (e.g. pkg:composer/symfony/symfony@6.4.0) ## Inputs - **purls**: list of Package URLs, one per entry. Generate them from your project manifest files: - Node.js: package.json / package-lock.json - Python: requirements.txt / Pipfile.lock / pyproject.toml - Ruby: Gemfile.lock - Go: go.mod / go.sum - Rust: Cargo.lock - PHP: composer.lock - Java: pom.xml / build.gradle - .NET: *.csproj / packages.lock.json ## Outputs - **report**: structured JSON objects describing the advisories affecting the audited packages. - **summary**: Markdown summary including total vulnerabilities, severity breakdown, and key findings. ## LLM usage guidelines - Never guess whether a package is vulnerable — always call this tool. - Only submit PURLs from the supported ecosystems listed above; others will be ignored. - The `summary` is already Markdown and can be shown directly. - Use `report` when deeper technical analysis is required.

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• ask_pipeworx_grounded

  Send That Email

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,141 across 1354 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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• suggest_questions

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  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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