PyPI Package: Python package metadata + links
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649,985 tools. Updated 2026-10-09 20:06
"PyPI" matching MCP tools:
- Look up one exact PyPI package, current version, license, Python requirements and recent releases. maxItems caps releases and dependency strings at 50. No maintainers, contacts, long description, package downloads or PyPI search crawl. Price: $0.01 USDC per successful call on Base. Requires an x402-capable wallet client.ConnectorNo auth
- pkg:pypi package-url shape, value discardedConnectorNo auth
- Pypi Package Info: Pypi Package InfoConnectorNo auth
- 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}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. 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.ConnectorNo auth
- Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources, at different times, with different precision — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.ConnectorNo auth
Matching MCP Servers
- FlicenseCqualityDmaintenanceAn MCP server built with FastMCP that provides basic terminal abilities like running commands, running Python, and working with files and folders.9-
- AlicenseNot gradedqualityDmaintenanceA security-focused MCP server that enables AI assistants to search PyPI packages, scan for vulnerabilities, audit dependencies, and ensure security across Python projects.3MIT
Matching MCP Connectors
PyPI MCP — wraps the Python Package Index (PyPI) JSON API (free, no auth).
PyPI Stats MCP.
- Search a registry for packages matching q. registry=all fans out to npm, Docker Hub, and the VS Code Marketplace and merges the results. PyPI has no public search API, so registry=pypi returns 400 not_supported — look a PyPI package up by name via get_package instead. Results are normalized PackageSummary items (npm adds a relevance score; Docker adds isOfficial).ConnectorNo auth
- Known vulnerabilities (CVE / GHSA / PYSEC / GO advisories) for a package, via OSV.dev. Pass version to filter to advisories affecting that exact version, or omit it for the package's full advisory history. Each result carries the OSV id, cross-id aliases, a severity word grade (LOW|MODERATE|HIGH|CRITICAL), the cvss vector string, affectedRanges with fixed-version events, references, and cwes. A clean package returns count: 0 with an empty list (not an error). ecosystem is CASE-SENSITIVE — use OSV's spelling (npm, PyPI, Go, crates.io, Maven, NuGet, RubyGems, …). Use scan_vulnerabilities_batch for lockfile batch scans.ConnectorNo auth
- 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). FEES ARE NOT MODELLED HERE: vwap_fill_price/profit_usd are GROSS of Polymarket's own taker fee (rate 0.04-0.07 by category — see polymarket_edges/fees.ts), on top of which this tool prices depth-crossing cost; a thin-margin fill that looks clean here can still be net-negative after the fee.ConnectorNo auth
- Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").ConnectorNo auth
- 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.ConnectorNo auth
- Supply-chain GUARDRAIL for AI coding agents and CI pipelines: check whether a dependency (npm or PyPI) is on the DugganUSA malicious-package deny-list BEFORE you install it. This is the runtime defense against slopsquatting / HalluSquatting / hijacked-package attacks — an AI agent about to run `npm install` or `pip install`, or a CI pre-install hook, calls this FIRST and blocks on a hit. Returns a crisp, machine-actionable verdict: {ecosystem, package, version, malicious, verdict:"block"|"allow"|"review", reason, advice, source}. `malicious:true` = the exact package is on our OSV-curated deny-list (215k+ named-not-heuristic entries across npm + PyPI). `malicious:false` = not on our known-bad list — absence is NOT proof of safety, so still pin and review new deps. If a `version` is supplied and the entry is version-scoped, the check is version-aware; all-versions-malicious packages block on any version. Designed to be the easiest AI-supply-chain guardrail to wire in: one MCP tool call, no auth, in the agent's pre-install step. Same data is available for CI at /api/v1/stix-feed/packages.json. Examples: {"ecosystem":"npm","name":"cxp-jquery"} → malicious:true, verdict:block. {"ecosystem":"pypi","name":"requests"} → malicious:false, verdict:allow.ConnectorNo auth
- Free preview of breaking changes / new releases for a software dependency. Pass an npm/PyPI `package` (resolved and fetched live if not already tracked) or a GitHub `repo` (owner/repo). Returns up to 5 recent changes plus the package's current version. Full history, significance filtering, and the LLM brief are paid via x402.ConnectorNo auth
- 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 1685 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 6,474 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 — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "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).ConnectorNo auth
- "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 / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).ConnectorNo auth
- Find the best open-source library for a need before adding a dependency. Give the need in plain words and get up to `limit` GitHub repositories ranked by fit = score x relevance to the need. Each result has a 0-100 score (momentum 40% (stars gained per week; without history, lifetime stars per week scaled by the npm/PyPI download trend), maintenance 25%, adoption incl. npm/PyPI weekly downloads 25%, license 10%), a tier (Strong >=75, Solid >=50, Watch >=25, Avoid <25; New for repos under 30 days old, too new to judge), a one-line verdict and the full breakdown. Use this when you do not yet have candidates; use compare_repos when you already have names. Makes 3 GitHub searches plus npm/PyPI lookups for the shortlist. Hosted: 50 free tool calls per day per user; send header X-GitHub-Token with your own GitHub token for unlimited use, or run the npm package locally (npx -y whichlib).ConnectorNo auth
- Discover projects: the most-starred GitHub repositories created in the last day, week or month, or with period "rising" repositories of any age that gained the most stars this week (like GitHub Trending; risingRank keeps that order). Optionally one language; each scored and returned sorted by score (starsRank keeps the stars order). Each result has a 0-100 score (momentum 40% (stars gained per week; without history, lifetime stars per week scaled by the npm/PyPI download trend), maintenance 25%, adoption incl. npm/PyPI weekly downloads 25%, license 10%), a tier (Strong >=75, Solid >=50, Watch >=25, Avoid <25; New for repos under 30 days old, too new to judge), a one-line verdict and the full breakdown. Not the right tool for picking a dependency, since new repositories have little maintenance history; use recommend_repos for that. One GitHub search (rising: one download of the daily list from raw.githubusercontent.com instead), plus one npm/PyPI lookup per repository when withDownloads is true. Hosted: 50 free tool calls per day per user; send header X-GitHub-Token with your own GitHub token for unlimited use, or run the npm package locally (npx -y whichlib).ConnectorNo auth
- Call this before installing or adding a package (npm install, pip install, a new entry in a manifest), especially one whose name you recalled or that a model suggested. One-call risk check: combines vulnerability_check (OSV.dev), typosquat_check, maintainer_change_check (npm only) and repo_health_check (when the GitHub repo can be resolved) into one overall verdict. A package that does not exist on npm or PyPI gets overall_risk 'package_not_found': the name may be invented and should not be installed. A package first published less than 30 days ago gets the 'new_package' flag and overall_risk 'review_recommended': new packages are where invented and look-alike names get registered, so the name is worth confirming against the project's own documentation before installing (on PyPI the age is that of the oldest release still published; being new does not make a package malicious). Use the individual tools to investigate one signal. Vulnerabilities are checked for the given version, or the latest published one (version_checked, version_source). If a check could not run (rate limit, upstream error), it is listed in unavailable_checks and overall_risk is 'incomplete', never 'no_signals_found'.ConnectorNo auth
- Does this PyPI or npm package name really exist? Deterministic answer from the real published artifact, signed, with typosquat-adjacency flags on misses. Free: 25 calls/day per client IP, shared with the /demo/v1/* HTTP routes, reset 00:00 UTC. Higher volume: paid HTTP routes at https://attester.dev/llms.txt.ConnectorNo auth
- 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.ConnectorNo auth
- Live availability check for a name across domain TLDs (registry RDAP), npm, PyPI, and GitHub usernames. Real registry lookups at call time - not guesses. Each result is registered, unregistered, unknown or invalid and names the source that decided it; "unknown" means no authoritative registry answered, so treat it as "cannot tell" and never as free. Some ccTLDs (.co among them) publish no RDAP and always come back unknown. Note: "unregistered" domains can still be registry-reserved or premium-priced; only a registrar checkout is final. Need another registry or platform checked? Describe it via the request_tool tool.ConnectorNo auth