"Search for an MCP server that can search S&P 500" matching MCP tools:

• searchA

  UseMyContext.aiA

  Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; nothing is written, so it is safe to call.

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• column_valuesA

  DryosA

  The distinct values of one text column, most rows first — what a filter can be. Args: slug: The stream. column: A text column from describe_stream (not the entity — use find_entities). where: Filters as `column=value`, the same as query_stream's. search: `column:text`, a case-insensitive substring. limit: At most this many (1–500).

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• free2aitools_explainA

  Free2AIToolsA

  Explain why one specific entity received its FNI score, returning the 5-factor breakdown: Semantic (S), Authority (A), Popularity (P), Recency (R), Quality (Q). FNI = 0.35*S + 0.25*A + 0.15*P + 0.15*R + 0.10*Q (the S factor is a baseline, surfaced with a caveat, not a measured per-entity value). USE WHEN you already have one entity id (from a search/rank/select result) and want its score rationale. DO NOT USE to search/discover entities, to run a model, or to get a recommendation — this only describes scoring evidence for the caller to interpret. Read-only, no side effects, no billing. Use free2aitools_compare instead for side-by-side differences across multiple entities.

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• search_companiesA

  Valuein — SEC EDGAR Fundamentals & Smart-Money DataA

  Search for US public companies by name, ticker symbol, CIK (SEC identifier), or SIC industry code. Returns ticker, company name, sector, industry, exchange, and current S&P 500 membership status. Use this tool to resolve a company name to ticker/CIK before calling `get_company_fundamentals`, `get_valuation_metrics`, or other tools that require a ticker — they do not fuzzy-match company names. **Use this tool — NOT `get_pit_universe` — when the user asks about CURRENT S&P 500 members.** To list current S&P 500 members, call `search_companies({ is_sp500: true })` (the `is_sp500` filter is itself a valid search parameter, so no other input is required). This returns the live snapshot as of query time. Example: "List 5 current S&P 500 members" → call `search_companies({ is_sp500: true, limit: 5 })`. **Use `get_pit_universe` ONLY when the user explicitly needs a survivorship-free historical universe as of a specific past date** (e.g. "S&P 500 members as of March 2018"). If the user says "current," "today," "now," or gives no date, use `search_companies` instead. **One ticker can return two rows.** A CIK identifies a *registrant*, not a company, so a reincorporation or holdco reorganisation moves the ticker to a NEW CIK while the filing history stays under the old one. Both rows are real. Use `is_active` to tell them apart: `true` is the current listing, `false` is the superseded one and carries `listed_until`. Prefer `is_active` over `status` — `status` is an entity-level flag that is unreliable in both directions. **Data details:** `sic_code` is the 4-digit SIC; `industry` is the human-readable label. `sector` is SIC-derived with GICS-style labels — NOT licensed GICS, so industrial conglomerates may map differently from official GICS (e.g. 3M → 'Health Care' by SIC vs Industrials by GICS). S&P 500 membership is sourced from index_membership.parquet (current SP500 = `index_name='SP500' AND removal_date IS NULL`). Available on all plans. **CIK is the canonical, stable identifier — prefer it for backtests and archival work.** A ticker can be retired and later RECYCLED to an unrelated company (DEC is now Diversified Energy, AMR is Alpha Metallurgical, ARC is a document company today), so a ticker-only reference can silently drift to the wrong issuer years later. A purely-numeric `query` (or the dedicated `cik` param) resolves by CIK. Every company-scoped tool (`get_company_fundamentals`, `get_financial_ratios`, `get_stock_price`, etc.) accepts the `cik` this tool returns in place of `ticker` — carry it forward across a multi-step workflow instead of re-resolving by ticker each call. `include_inactive` defaults to `true`: this is a survivorship-free product, so delisted/superseded listings are never hidden by default.

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• tailwind_canonicalizeA

  LunarWerxA

  Rewrite Tailwind CSS classes into their canonical short form. Turns arbitrary values back into real design tokens: p-[1rem] becomes p-4, rounded-[24px] becomes rounded-3xl, -bottom-[4px] becomes -bottom-1. Backed by 12,438 replacements generated from Tailwind 4.3.3's own design system, so the answers are authoritative rather than guessed. Handles one class at a time and does NOT merge across classes (px-4 py-4 into p-4, w-6 h-6 into size-6) — that needs the surrounding group to be safe to collapse; use NormWind for that.

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• get_board_testA

  Edgar & Edgarette: SEC Filing BoardsB

  The boards’ own calls, locked at the 6 Oct 2026 close with a SHA-256 receipt and scored against what happened: places for cash for 1, 3 and 6 months (paid what it said, no loss), cash cows and cash machines (beat the S&P 500 fund on total return, same dates), and Edgarette’s 12-month expected-return calls (landed inside her 80% range). A test of the boards, not advice.

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• expand_rruleA

  TanodA

  utilpeek: Expand an iCalendar RRULE into dates. For cron use cron_next_runs; to build an event use build_ics_event. Returns an RFC 5545 RRULE and a start expanded into its occurrence list with python-dateutil in an IANA time zone: wall-clock times keep across DST changes. Up to 1,000 occurrences, an optional [from, to] window, extra until and exdates. Returns ISO 8601 times with offset and whether more exist. Pure computation: no network access, nothing stored. The search stops at search_horizon (1 hour for SECONDLY up to 100 years for YEARLY) and after 1.5 s. A bad rule, a rule that never matches (Feb 31) or a bad date or zone is a 422 (not charged). Typically under 0.1 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/ics-calendar-api.html

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• expand_rruleA

  Developer Utilities & Validators (Tanod)A

  utilpeek: Expand an iCalendar RRULE into dates. For cron use cron_next_runs; to build an event use build_ics_event. Returns an RFC 5545 RRULE and a start expanded into its occurrence list with python-dateutil in an IANA time zone: wall-clock times keep across DST changes. Up to 1,000 occurrences, an optional [from, to] window, extra until and exdates. Returns ISO 8601 times with offset and whether more exist. Pure computation: no network access, nothing stored. The search stops at search_horizon (1 hour for SECONDLY up to 100 years for YEARLY) and after 1.5 s. A bad rule, a rule that never matches (Feb 31) or a bad date or zone is a 422 (not charged). Typically under 0.1 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/ics-calendar-api.html

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• searchA

  ScrapeMole QuotesA

  Use to resolve a company or asset name to a tradable ticker before calling /v1/quote or /v1/history: "apple" -> AAPL, "s&p 500" -> ^GSPC, "volkswagen" -> VOW3.DE, "bitcoin" -> BTC-USD. Returns up to 20 matches with symbol, short/long name, quote type (EQUITY, ETF, INDEX, MUTUALFUND, CURRENCY, CRYPTOCURRENCY, FUTURE), exchange, sector and industry where known. Paid tool: x402 USDC on Base, $0.002 per successful call; call without payment to receive the PaymentRequired object.

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• expand_rruleA

  Tanod MCP Server (Deprecated)A

  utilpeek: Expand an iCalendar RRULE into dates. For cron use cron_next_runs; to build an event use build_ics_event. Returns an RFC 5545 RRULE and a start expanded into its occurrence list with python-dateutil in an IANA time zone: wall-clock times keep across DST changes. Up to 1,000 occurrences, an optional [from, to] window, extra until and exdates. Returns ISO 8601 times with offset and whether more exist. Pure computation: no network access, nothing stored. The search stops at search_horizon (1 hour for SECONDLY up to 100 years for YEARLY) and after 1.5 s. A bad rule, a rule that never matches (Feb 31) or a bad date or zone is a 422 (not charged). Typically under 0.1 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/ics-calendar-api.html

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• get_universeA

  TickerDBA

  Get selected fields for EVERY asset on one date in a single call (about 11,000 rows), ordered by ticker: the input of a daily or weekly trading model run. Fields use the same names as get_search and are tier-gated the same way; at most 60 per call. Costs 5 credits per 10 fields. member_of / held_by restrict it to an index's or a fund's holdings on that date ("S&P 500 members with garp_core_score"). A row dated D reflects everything known by 04:00 UTC on D+1, so a backtest acts at the next session's open. For filtered or ranked subsets use get_search; for one ticker use get_summary.

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• axint.registry.searchA

  AxintA

  Search the Axint Registry for already-published packages that match a natural-language query. Use this BEFORE calling axint.feature or axint.compile so the agent can install an existing package instead of regenerating Swift the community has already shipped. Use: use before generating code to find reusable packages; not for validating local Swift. Inputs: query drives ranking; kind and platform narrow results without changing the registry source. Effects: read-only local registry search using AXINT_REGISTRY_PATH or sibling checkout; no network by default.

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• get_sub_domainsA

  AnySearchA

  This is Anysearch's domain discovery tool. IMPORTANT: Step 1 of vertical search. REQUIRED before any search that uses a domain. Returns valid sub_domains and sub_domain_params for the specified domain(s). Call this when the query targets a specialized vertical or needs structured parameters: stock prices, financial data, academic papers, legal cases, medical/drug info, flight status, weather, exchange rates, geographic POIs, code repositories, or any domain where a structured identifier (ticker, DOI, CVE, IATA, coordinates) is involved. ## When to call — pick the domain(s) that match what the user is asking about: academic agriculture business code energy environment film finance gaming health ip legal resource security social_media travel ## Input — choose from the list above and pass via the domain or domains parameter: - domain: single domain string (use only when 100% certain the query is single-domain) - domains: batch query for up to 5 domains in one call (takes priority over domain) 🏆 ALWAYS prefer the `domains` (plural, array) parameter. Pass ALL potentially relevant domains at once — even for seemingly single-domain queries, consider related domains: - Query about "cryptocurrency regulations" → domains=["finance", "legal", "security"] - Query about "best gaming laptops" → domains=["gaming", "tech", "ecommerce"] - Query about "climate change impact on agriculture" → domains=["environment", "energy", "academic"] ## Returns Markdown table filtered to the specified domains: sub_domain | description | params ## CRITICAL: How to use results - sub_domain is the PRIMARY routing key — always pass it to search - params column shows available structured parameters — pass them via sub_domain_params in search, NEVER embed in query - If multiple sub_domains returned (especially from multiple domains), use batch_search — one query per sub_domain — instead of multiple sequential search calls - Params marked (required) in the output MUST be passed when using that sub_domain in search. If a required param is not applicable to your query, pass it as an empty string (key: "") — do not skip it.

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  Destructive

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• originatorsA

  QuotewiseA

  Lookup, search, or browse originators. Handles people, proverbs, anonymous sources, and institutions. Use name= for exact match, search= for fuzzy, neither for browsing. When to use: User asks about a person/author, wants to find who said something, or needs to browse by category (poets, philosophers, etc). Behaviors: - `name` provided → resolve and return single originator details - `search` provided → fuzzy search, return ranked list (optionally filtered by category tags) - Neither → browse by filters (popular, language, min_quotes, category tags, etc.) Category tags filter by originator type (e.g., ["Poets", "Politicians", "Catholic Bishops"]) - works with all modes. Gender filter accepts natural language (e.g., "female", "women", "queer", "trans") - resolved to Wikidata Q-IDs internally. Response format: - Concise (default): slug, full_name, sort_name, quote_count, descriptions_i18n, web_url - Detailed: + biography (500 char excerpt), confidence_tier, similarity_score Response includes ai_hints with suggested next actions and quality signals for agent workflows. Date filters (`born_on`, `died_on`, `born_year_gte`, `born_year_lte`, `died_year_gte`, `died_year_lte`) combine with every other filter via AND. Negative year bounds represent BCE; year 0 is rejected. Examples: - `originators(name="Einstein")` - exact lookup - `originators(search="Shake")` - fuzzy search for "Shakespeare" - `originators(tags=["Poets"], gender="female")` - browse female poets - `originators(sort="popular", limit=10)` - top 10 by quote count - `originators(born_on="04-20")` - originators born April 20 (any year) - `originators(born_year_gte=-500, born_year_lte=-300)` - originators born between 500 BCE and 300 BCE inclusive

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• get_insiderA

  Edgrapi — US Government Data APIA

  Parsed SEC Form 4 insider trades for a ticker: owner, role, transaction code (P open-market purchase, S sale, A award, M exercise, F tax withholding), shares, price, dollar value, and a 10b5-1 plan flag. Code P is the only own-money buy signal; A/M/F are automatic compensation. Set form=3/5 for holdings statements or form=144 for notices of proposed sale (an intent-to-sell signal that precedes the Form 4). Pass ticker='latest' for the market-wide feed of the biggest open-market buys, or ticker='clusters' for companies where several insiders bought at once.

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• get_insiderA

  edgrapi-skillsA

  Parsed SEC Form 4 insider trades for a ticker: owner, role, transaction code (P open-market purchase, S sale, A award, M exercise, F tax withholding), shares, price, dollar value, and a 10b5-1 plan flag. Code P is the only own-money buy signal; A/M/F are automatic compensation. Set form=3/5 for holdings statements or form=144 for notices of proposed sale (an intent-to-sell signal that precedes the Form 4). Pass ticker='latest' for the market-wide feed of the biggest open-market buys, or ticker='clusters' for companies where several insiders bought at once.

  ConnectorNo auth

• fetchA

  MaplithA

  Read one map document by the id a search result handed you. Ids: object:<name>, finding:<id> or finding:<finding_key> (stable across rescans), process:<object type>, metric:<name>, statement:<id> (run-scoped: search again after a rescan), playbook:<id>. It is the second half of the search/fetch pair that connector clients such as ChatGPT require, and returns id, title, text, url and metadata with the direct tools' data flattened to text. If your client can call the direct tools, use them instead (structured, no search first): get_object for an object you can name, get_process for a process, get_metric for a metric, list_findings for findings by status. Use fetch for a result search handed you, or a finding_key.

  ConnectorOAuth

• financial_searchA

  aether-developerA

  Search SEC filings (10-K, 10-Q, 8-K, registration statements/prospectuses and press exhibits; S&P 500 coverage, ~10 years) with hybrid semantic + keyword retrieval. TWO MODES. Search mode (pass `query`): results ranked by relevance, highest first. FETCH MODE (omit `query`): no ranking at all — pass an issuer plus any of form_type / fiscal_year / accession_number / section and get that filing's sections back in filing-date-desc then document order, in tens of milliseconds. Use fetch when you already know WHICH document you want and only need its text. ALWAYS pass `issuer` (ticker, cik or company_name) — the company the question is about — plus `fiscal_year` and/or `form_type` when you know them. Scoped calls are fast and precise. A call with no issuer runs as scope=cross_company: slower, ranked by relevance only, and the response sets `quality_caveat` — check for that field. Use scope=cross_company deliberately only for questions that are genuinely about many companies ("which filers name NVIDIA as a supplier"). Built for agents: every hit is a ready-to-cite payload — exact filing-section text, form type, filing date, accession-numbered citation, source URL and a 0-1 confidence score — no HTML parsing, no EDGAR pagination. Use for revenue/segment figures, risk factors, M&A and contract terms, customer/supplier concentration, and any claim that must trace to a primary source. Fetch mode pages: pass the response's `next_offset` as `offset` to read past `limit`. Returned windows overlap; counts of a term over returned text are not document counts. `partial: true` means retrieval degraded; an empty result is then not proof of absence. Coverage is NOT US-only: Sweden/Bolagsverket, Japan/EDINET, Korea/DART annual reports are included. Those issuers have no US ticker — pass `issuer.company_name` (e.g. "Sivers Semiconductors", "Samsung Electronics", "Ajinomoto") or scope a whole market with `jurisdiction` (["SE"], ["JP"], ["KR"]).

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• members_vs_marketA

  Coldpine: Congressional Stock DisclosuresA

  Per-member 90-day return on disclosed stock purchases versus the S&P 500 over the same windows (alpha), with the 30-day alpha and hit rate. Only members with at least min_trades priced purchases that have a full 90-day window. Most members do not beat the market; this is the measured record. Same data as coldpine.io/research/members-who-beat-the-market. Use it when the question is about performance; congress_leaderboard ranks activity and carries no returns. Rows come back highest 90-day alpha first, each with the number of purchases measured, the average 90-day return, the S&P 500 average over the same windows, the 90-day and 30-day alpha, the share of purchases that beat the index, and the member's page. The response states the method. These are measured past outcomes on disclosed purchases only (sales and trades younger than 90 days are excluded), not a forecast and not a reason to trade. Access: needs the agent token from a free Coldpine account, sent as 'Authorization: Bearer <token>'; without it the call returns setup steps instead of data. Read-only, cached for up to an hour.

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• get_filesearch_grid_filesA

  gdex-mcpA

  Search for data files containing "grid" datatype data, filtered by parameter code(s) and optionally by time range, product, grid, or level. Results are paginated; use get_filesearch_result_page with the returned result_id to fetch additional pages. Use get_filesearch_grid_filters first to find valid parameter/product/grid/level codes for this dataset. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 parameters: Parameter code(s) to search for (required, at least one) valid_datetime_min: Restrict to data valid on/after "YYYY-MM-DD HH:MM" valid_datetime_max: Restrict to data valid on/before "YYYY-MM-DD HH:MM" products: Restrict to specified product code(s) grids: Restrict to specified grid code(s) levels: Restrict to specified vertical level code(s)

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• traceable_library_searchA

  TraceableA

  Find Reference Library items you can access (org-scoped items plus items in projects you are a member of). With query: keyword search across each item's name, REF code, and locally-extracted text (case-insensitive substring — no semantic/embedding search), returning matches with a short snippet around the first hit. WITHOUT query: lists every accessible item — REF code, name, kind (file | webpage), file type, scope (org | project), and whether its text has been indexed. Search only finds text that has been indexed, so if a search comes back empty, list first and check the indexed flag: an unindexed item is invisible to search until traceable_library_index runs on it.

  ConnectorOAuth