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356,129 tools. Last updated 2026-08-01 01:11

"SemVer" matching MCP tools:

  • Browse recent CRS (Congressional Research Service) bill summaries — plain-language summaries of bills at each legislative stage, useful for answering "what's happening in Congress?". The fromDateTime/toDateTime filters apply to the summary's update time, not the bill's action date, so results include recently rewritten summaries of older bills. Defaults to summaries updated in the last 7 days. Each item shows both the bill's action date and the summary update date. For summaries of one specific bill, use congressgov_bill_lookup with operation='summaries' instead.
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  • Get bias analysis for a specific article by its URL. Use this when you have a direct link to an article and want to know its political leaning, credibility, emotionality, and other bias dimensions — without needing to know the source name first. On success (found=true), returns: - article_id, classification_id, requested_url, matched_url, title, source, date, link, category - teaser: article excerpt - summary: one-sentence AI summary - context: AI-generated context for the article - extracted_data: structured quantitative/qualitative facts extracted from the article - raw_data: legacy serialized form of extracted_data - bias_description: narrative description of this specific article's bias - bias_values: dict of per-dimension article scores using canonical plain-text keys, e.g. {"liberal conservative bias": 4, "overall credibility": 7, "emotional bias": -5, ...} Article scores use -10 to +10 for bipolar dimensions and 0 to 10 for unipolar dimensions. Positive values lean toward the second pole of each dimension (conservative, authoritarian, etc.). - bias_analysis_status: 'evidence_ready', 'evidence_unverified', 'evidence_partial', 'scored_legacy', or 'pending' - bias_dimensions when include_evidence=true: each dimension's score, scale, evidence status, claim, verbatim evidence, counterevidence, confidence, and rationale. Quotes include verification method and exact character offsets when raw-text matching succeeds. Dimension evidence_status is one of: verified, provided_unchecked, quote_mismatch, metadata_incomplete, metadata_only, or missing. - bias_analysis: contract/schema/model/prompt provenance, generation and review status, input scope/hash/size, analysis target, quote-verification method, explicit missingness and evidence coverage, and case-specific limitations - total_shares: total social shares - wayback_link: Wayback Machine archive URL if available - image: article image URL if available On failure (found=false, HTTP 404): - found: false - message: explanation string The URL is automatically queued for ingestion; retry after ~24 hours. Tip: if you want source-level bias (not article-level), use get_source_bias instead. Tip: bias_values keys here use plain-text format (e.g. 'liberal conservative bias') shared with the other bias tools where that dimension is available. Args: url: Full article URL, e.g. 'https://www.nytimes.com/2024/01/01/us/politics/example.html'. include_evidence: Include claim-level evidence and limitations. Defaults to true.
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  • Use this tool when the user shares an image that contains text they need extracted, read, or processed. Triggers: 'read the text in this image', 'extract text from this screenshot', 'what does this scanned page say', 'transcribe this handwritten note'. Accepts base64-encoded PNG/JPEG/WEBP/BMP/TIFF. Returns extracted text, confidence score, and word count. Prefer this over vision model text extraction for accuracy on scanned docs. Free, no API key, no signup; the image is processed in memory and never stored.
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  • Audit a frontend package.json for security risks — returns a single SHIP/CAUTION/BLOCK verdict with licence risks and abandonment signals. Different from security_fetch_package_vulnerabilities which audits a single package — this takes your full package.json. manifest: Contents of package.json as a string. Required. 500 KB max. lockfile: Contents of package-lock.json or yarn.lock (optional). If provided, audits pinned versions; otherwise audits semver ranges. BLOCK: any critical CVE in direct deps OR GPL-3.0 in commercial context. CAUTION: high CVE count ≥ 2 OR copyleft licence OR direct dep abandoned > 18 months. Sources: OSV.dev (CVEs), deps.dev (licences), npm registry (abandonment). Read-only. No side effects. Idempotent. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="frontend_security_audit_manifest", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".
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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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  • 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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  • Semantic Versioning (semver) MCP.

  • Search posts, profiles, feeds, threads, and trending topics on Bluesky.

  • 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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  • 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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  • 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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  • 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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  • Browse congressional committees and their legislation, reports, and nominations. Committee codes follow the pattern chamber-prefix (h/s/j) + abbreviation + 2-digit number — use 'list' (with optional 'filter' for name→code resolution) to discover codes, then 'get' or drill into 'bills', 'reports', or 'nominations' ('nominations' is Senate-only). 'get' and sub-resources only need committeeCode (chamber is inferred from the prefix); pass chamber explicitly to override. The 'bills' sub-resource defaults to 'recent' order (newest update-date first); pass order='oldest' for ascending update-date order. Upstream omits bill titles from the 'bills' sub-resource — rows carry only {congress, billType, billNumber, actionDate, relationshipType, url}; chain 'congressgov_bill_lookup get' per row to retrieve titles and policy area.
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  • Search Helium's balanced news stories — AI-synthesized articles that aggregate multiple sources. Unlike search_news (which returns individual RSS articles), this returns Helium's own synthesized stories: each one draws from multiple sources and includes an AI-written summary, takeaway, context, evidence breakdown, potential outcomes, and relevant tickers. Returns a list of stories, each with: - title, simple_title, date, category - page_url: full URL to the story on heliumtrades.com - image: story image URL (when available) - summary: Helium's synthesized overview - takeaway: key conclusion - context: background context - evidence: numbered evidence items - potential_outcomes: forward-looking outcomes with probabilities - relevant_tickers: related stock tickers - num_sources: number of source articles synthesized - rank: search relevance score Args: query: Search keywords (required). limit: Max results (1-50, default 10). category: Filter by category. One of: 'tech', 'politics', 'markets', 'business', 'science'. days_back: Only include stories from the last N days. 0 means no date filter.
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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 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 5,319 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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  • Publishing an app as a community template, taking your own listing back down, installing a template, and, for relay operators, reviewing submissions. Actions: publish, unpublish, get_config_contract, install, list_pending, get_submission, approve, reject, set_trust_level. publish captures a live app (html, manifest, the seed rows of its seedOnInstall collections, and listing metadata) into a pending template. It is installable by the returned direct link but is not listed in the public gallery until an operator approves it, and it requires a verified email and no more than a few pending submissions at once. Privacy consequence: an approved template's content and its captured seed rows become public to every platform user, so seed data in a published app must be example-only rather than real personal data. attest_example_only:true records that this was checked. A template may take a per-publisher `slug` (namespaced as <handle>/<slug>) and a semver `version` defaulting to 1.0.0, and a republish under the same slug must bump the version. unpublish is the publisher's own undo for a live listing, taken down by snapshot_id: it leaves the public gallery, search, and the direct snapshot install link. It works only on your own submissions, and a snapshot that does not exist or belongs to someone else reads as not found either way. Existing installs are unaffected, because an install is a fresh private copy rather than a live reference, so unpublishing never breaks an app someone already installed. It is idempotent, and publishing a new version is the way to put the listing back. get_config_contract reads what a template needs at install, meaning its settings collection and its ordered config and upload steps, by `ref`. install creates a fresh private copy of a template for the caller's owning human, passing answers as `config`, where a 'config' value is a string and an 'upload' value is a pre-uploaded attachment id from the attachments tool. The review actions are limited to the relay's configured community reviewers: list_pending returns the queue; get_submission returns a submission's full content by snapshot_id; approve lists it in the gallery, where a re-publish supersedes the app's prior approved version; reject takes a required note that lands in the publisher's app feed.
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  • Returns all industry categories and their business types with IDs. Use the business type IDs in search_businesses (businessTypeIds) to filter listings by category. Call this first when you need to discover which IDs to use for a given industry or business type.
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  • Start a patient session by providing their contact information. Sends a 6-digit verification code to the patient's email. Returns a session_id (NOT a token). The session_id is used with auth_verify_otp to prove email ownership and get a bearer token. The code is in the email subject line: 'Chia Health: Your code is XXXXXX'. If you have access to the patient's email (e.g. Gmail MCP), search for this subject. No authentication required. Call this when the patient is ready to proceed with their medical intake — after browsing medications and checking eligibility.
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  • Complete payment using Stripe ACP (Shared Payment Token). Only use this if your platform supports Stripe Agentic Commerce Protocol and can provision an SPT. If your platform does NOT support ACP, use the `payment_url` from checkout_create instead, then poll checkout_status. Requires authentication.
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  • Retrieve U.S. congressional roll call votes and individual member voting positions for either chamber. Set 'chamber' to 'house' (default, from the Congress.gov API) or 'senate' (from the Senate's official LIS feed). Use 'list' to find votes by congress and session (newest first by default), 'get' for vote details (question, result, tallies, party breakdown, associated bill/nomination/amendment), or 'members' for how each member voted.
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  • Browse presidential nominations to federal positions and track the Senate confirmation process. Use 'list' to browse, 'get' for nomination detail, 'actions'/'committees'/'hearings' for confirmation pipeline data, or 'nominees' to retrieve individual appointees in a multi-nominee batch. Nominations use 'PN' (Presidential Nomination) numbering. Most nominations carry confirmation activity on the parent (e.g., PN1000); multi-part parents (e.g., PN851) carry no activity of their own — their actions, committees, hearings, and nominees live on partitioned children (PN851-1, PN851-2, …). 'get' on a parent that has no `nominees` array signals the partitioned form is needed for everything below it.
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  • Search NHTSA defect investigations from the ODI flat file — covering Preliminary Evaluations (PE), Engineering Analyses (EA), Defect Petitions (DP), Recall Queries (RQ), Audit Queries (AQ), and additional ODI types. make, model, and component are structured filters against the investigation record's vehicle associations. All filters are ANDed. Use nhtsaId to fetch one investigation by its exact ID — including the investigationId nhtsa_search_recalls returns for a campaign. Investigations may link to a resulting recall campaign via recallCampaign.
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