Search / io.github.pipeworx-io/cta
Cta
R0· no sign-inCTA MCP — Chicago Transit Authority real-time trains ('L') + buses
v0.1.2 · active · repository · website · descriptor JSON · versions
Search / io.github.pipeworx-io/cta
R0· no sign-inCTA MCP — Chicago Transit Authority real-time trains ('L') + buses
v0.1.2 · active · repository · website · descriptor JSON · versions
{
"mcpServers": {
"cta": {
"type": "http",
"url": "https://gateway.pipeworx.io/cta/mcp"
}
}
}claude mcp add --transport http cta https://gateway.pipeworx.io/cta/mcp[mcp_servers.cta]
url = "https://gateway.pipeworx.io/cta/mcp"
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"cta": {
"type": "remote",
"url": "https://gateway.pipeworx.io/cta/mcp",
"enabled": true
}
}
}{
"mcpServers": {
"cta": {
"type": "http",
"url": "https://gateway.pipeworx.io/cta/mcp"
}
},
"deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=cta&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vZ2F0ZXdheS5waXBld29yeC5pby9jdGEvbWNwIn0%3D"
}{
"servers": {
"cta": {
"type": "http",
"url": "https://gateway.pipeworx.io/cta/mcp"
}
}
}{
"mcpServers": {
"cta": {
"httpUrl": "https://gateway.pipeworx.io/cta/mcp"
}
}
}extensions:
"cta":
enabled: true
name: "cta"
type: streamable_http
uri: "https://gateway.pipeworx.io/cta/mcp"
timeout: 300
Not scored: fewer than three checks apply, usually because no remote answered a credential-free probe or the server is a local package.
protocol.modernnewest supported version is 2025-03-26; 2026-07-28 not supported, older versions are deprecated until 2027-07-28protocol.statelessonly applies to 2026-07-28 serversprotocol.transportstreamable HTTPprotocol.list_ttlonly applies to 2026-07-28 serversauth.prm, auth.as_metadata, auth.cimdno remote uses OAuthauth.secret_in_urlno templated URLtools.descriptions, tools.description_length, tools.schemas, tools.annotations, tools.directory_hints, tools.token_cost, tools.api_dumptool list unknown (behind auth, not probed, or empty)stability.changes, stability.rug_pulltool list unknowndeps.known_vulns, deps.mcp_sdk_version, deps.resolvableno npm or PyPI packageunsubscriberemovedCancel 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.
polymarket_edgesremovedScan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (net of slippage AND Polymarket's own taker fee — fees_pp_applied itemises the fee component; see fees.ts for the published per-category schedule), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates, knob dropped them). Cached 1h at the KV level keyed on all knobs.
company_factsremovedTYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the period matters: those answer "the most recent figures" and will happily hand back FY2025 when you asked about FY2019, and neither separates a discrete quarter from a year-to-date figure. This one refuses instead — it NEVER substitutes the latest period for the period requested, NEVER returns 0 for missing data, NEVER lets a 9-month YTD number answer a quarterly question, and NEVER converts a currency. Every answer carries the exact us-gaap concept it came from, what that concept MEASURES (NetIncomeLoss excludes non-controlling interests, ProfitLoss includes them — not synonyms), the accession number and a link to the filing on sec.gov, the restatement trail of any superseded figures, and a contract + derivation version to pin against. Fiscal periods are the FILER'S OWN, anchored on their fiscal-year end, so Walmart's year ending 2026-01-31 is FY2026 and Apple's ending 2025-09-27 is FY2025. Attributes: revenue, net_income, cash, gross_profit, operating_income, eps_diluted, capex, operating_cash_flow, long_term_debt, shares_outstanding (the dei COVER-PAGE share count, so its period end is the cover date, after the quarter closed — that is the most current count the filing carries). Units are not all USD: shares_outstanding is in `shares` with currency null, eps_diluted in `USD/shares`. A discrete quarter the filer never filed — every Q4, and Q2/Q3 of cash-flow lines, which are filed year-to-date — is DERIVED as YTD(q) minus YTD(q-1) and comes back with `reported: false`, the subtraction named in `derivation` (fy_minus_9m / ytd_9m_minus_6m / ytd_6m_minus_3m) and both legs with their filings in `derived_from`; test `reported` if you will only accept a figure lifted from a filing. Per-share and balance-sheet figures are NEVER derived. Every non-answer is a named status — `unavailable` (the filer did not report it for that period; the periods that DO exist are listed, without values), `unsupported` (outside what v1 covers — a non-us-gaap filer, an unknown attribute, a non-USD unit), `ambiguous` (the company name matched two filers equally well; both are named), `conflicting` (two filings the same day disagree; both are returned and neither is picked), `partial` (a value with no accession behind it). Source: SEC EDGAR, XBRL financial facts, one publisher read once — see `corroboration`. Same response is served at POST https://gateway.pipeworx.io/v1/facts for non-MCP callers.
cta_bus_positionsremovedLive Chicago CTA bus positions on a route — every vehicle currently running: lat/lon, heading, destination, delayed flag, and distance along the pattern. Answers "where is the 22 Clark bus right now" via the CTA bus tracker. Example: cta_bus_positions({ route: "22" })
cta_bus_predictionsremovedChicago CTA bus tracker arrival predictions at a bus stop — route, destination, predicted minutes until arrival ("DUE" = arriving now), delay flag, and vehicle id. Pass stop_id (the 4-5 digit stop number posted on CTA bus-stop signs), optionally with route. If you only know the stop by name, pass route + find_stop (a street/intersection fragment like "clark & madison") and the stop is looked up for you. Example: cta_bus_predictions({ route: "22", find_stop: "addison" })
cta_train_arrivalsremovedReal-time Chicago CTA 'L' train arrivals at a station — answers "when is the next train Chicago", next Red Line at Belmont, Blue Line to O'Hare from Clark/Lake. Returns each upcoming train's line color, destination, arrival time and minutes_away, plus approaching / delayed / scheduled-only flags and the platform description. Station accepts a name ("Belmont", "Clark/Lake", "O'Hare") or a 5-digit mapid (e.g. 41320). Example: cta_train_arrivals({ station: "Belmont", route: "Red" })
recallremovedRetrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.
rememberremovedSave 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.
pipeworx_feedbackremovedTell 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.
ai_visibility_checkremovedProbe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
Showing the latest 10. Full history: list_changes · trend: quality JSON.
Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.
+repository +namespace-matches-repo +website +reachable +uptime-97% +updated-24d-ago
Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.
| remote | auth | reachable | uptime 30d | p50 | protocol | last ok |
|---|---|---|---|---|---|---|
https://gateway.pipeworx.io/cta/mcp streamable-http | none | yes | 97% | 30 ms | 2025-03-26 | 2026-10-20 |
No tools observed. Tools appear once a credential-free probe can list them.
Schemas: list_tools.
And 1,855 more from this publisher.
{
"name": "io.github.pipeworx-io/cta",
"title": "Cta",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"remotes": [
{
"url": "https://gateway.pipeworx.io/cta/mcp",
"type": "streamable-http"
}
],
"version": "0.1.2",
"repository": {
"url": "https://github.com/pipeworx-io/mcp-cta",
"source": "github"
},
"websiteUrl": "https://pipeworx.io/packs/cta",
"description": "CTA MCP — Chicago Transit Authority real-time trains ('L') + buses"
}