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260,211 tools. Last updated 2026-07-05 05:07

"Search for 'dia' (unspecified context)" matching MCP tools:

  • <tool_description> Search and discover products, recipes AND services in the Nexbid marketplace. Nexbid Agent Discovery — search and discover advertiser products through an open marketplace. Returns ranked results matching the query — products with prices/availability/links, recipes with ingredients/targeting signals/nutrition, and services with provider/location/pricing details. </tool_description> <when_to_use> Primary discovery tool. Use for any product, recipe or service query. Use content_type filter: "product" (only products), "recipe" (only recipes), "service" (only services), "all" (all, default). For known product IDs use nexbid_product instead. For category overview use nexbid_categories first. </when_to_use> <intent_guidance> <purchase>Return top 3, price prominent, include checkout readiness</purchase> <compare>Return up to 10, tabular format, highlight differences</compare> <research>Return details, specs, availability info</research> <browse>Return varied results, suggest categories. For recipes: show cuisine, difficulty, time.</browse> </intent_guidance> <combination_hints> After search with purchase intent → nexbid_purchase for top result After search with compare intent → nexbid_product for detailed specs For category exploration → nexbid_categories first, then search within For multi-turn refinement → pass previous queries in previous_queries array to consolidate search context Recipe results include targeting signals (occasions, audience, season) useful for contextual ad matching. </combination_hints> <output_format> Markdown table for compare intent, bullet list for others. Products: product name, price with currency, availability status. Recipes: recipe name, cuisine, difficulty, time, key ingredients, dietary tags. Services: service name, provider, location, price model, duration. </output_format>
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  • Fetch full markdown of a doc by `path` (as returned by `browse`, `semantic_search`, or `grep_docs`). Use to retrieve full content after a search snippet looks promising. Pass `heading` (full breadcrumb like `Character Management > Inventory Management`, or just the leaf — case-insensitive, fuzzy) to fetch only that section. Deep-heading matches auto-prepend the H2 parent's intro for context. For individual script natives prefer `lookup_native`. The largest rdr3_discoveries lua data tables are keyed catalogs: call with no `heading` to list their top-level keys, then pass a key as `heading` to fetch that one entry; use `grep_docs` to search values inside. For code symbols (`addItem`) use `grep_docs`. Community findings use `learning:N` paths, not `learnings/<slug>.md`. On 404 returns available headings + cross-file hints.
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  • Fetch full markdown of a doc by `path` (as returned by `browse`, `semantic_search`, or `grep_docs`). Use to retrieve full content after a search snippet looks promising. Pass `heading` (full breadcrumb like `Character Management > Inventory Management`, or just the leaf — case-insensitive, fuzzy) to fetch only that section. Deep-heading matches auto-prepend the H2 parent's intro for context. For individual script natives prefer `lookup_native`. The largest rdr3_discoveries lua data tables are keyed catalogs: call with no `heading` to list their top-level keys, then pass a key as `heading` to fetch that one entry; use `grep_docs` to search values inside. For code symbols (`addItem`) use `grep_docs`. Community findings use `learning:N` paths, not `learnings/<slug>.md`. On 404 returns available headings + cross-file hints.
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  • AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'
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  • Search hearings, floor sessions, and committee meetings. Experimental — most states do not publish event data to Open States. Use after and before to scope to a date range. Set require_bills=true to filter to events with bills on the agenda, which is the most useful filter for tracking legislation through committee. Use include=agenda,participants for full meeting context. Empty results often indicate the state lacks event data rather than no events occurring.
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  • Use this when a ChatGPT user wants to see what Influship can return before linking an account. Fetches one configured sample creator with social profile context. This is a low-cost preview tool and should not be used for search, discovery, matching, or lookalike requests. After showing the preview, tell the user that real live creator data, search, lookalikes, matching, posts, and transcripts require connecting an Influship account. Explain that they can authorize either an Influship SaaS subscription, where usage counts against monthly bundled credits, or an Influship API account, where usage is billed pay-as-you-go under API billing.
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  • Stop re-explaining yourself to Agents. Give it the right context, right when needed.

  • MCP server for accessing curated awesome list documentation

  • Call once to register the user's birth date/time/gender. Each call is a complete replacement — all 6 fields must be supplied. For fields the user does not know or prefers not to share, pass `null` explicitly (e.g. `hour: null` for unknown birth time, `gender: null` for unspecified). To update just one field, first call `intentions_get_profile` to fetch the current values, merge the user's new input, then call this tool with the full merged payload. Identity-changing edits (year/month/day/hour/gender) are limited to 3 per calendar month; adding precision (e.g. filling in a previously-null hour) is always free. Returns the element profile.
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  • Keyword and semantic search across the connected repository's generated docs, conventions, documentation gaps, AI-context notes, and indexed code. Read-only; no side effects. Returns ranked matches in Markdown grouped into Documentation and Code sections, each with a title, snippet, and source paths. Use for open-ended lookups when you don't know which category holds the answer; when you do, the specific getters (get_conventions, get_doc_gaps, get_documentation_opportunities) are more direct. Omitting query returns recent context instead.
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  • AUTHORITATIVE portfolio holdings of a US ETF or mutual fund (SEC Form N-PORT) — what the fund actually owns. Pass the FUND's ticker (e.g. "ARKK", "QQQ", "VTI", "VOO", "IVV"). Returns the latest monthly portfolio: net assets, holdings count, and top positions by weight — each with name, CUSIP, value (USD), and % of fund. Use for "what does ARKK hold", "top holdings of QQQ", "is $STOCK in VTI". Distinct from edgar_institutional_holdings (13F = what an investment MANAGER like Berkshire owns); this is a registered fund's own N-PORT. Covers US-registered open-end funds + ETFs; data is ~30-60 days delayed. Note: a few legacy ETFs structured as unit investment trusts (e.g. SPY, DIA) don't file N-PORT and won't resolve — use IVV or VOO for S&P 500 exposure.
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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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  • Fetch a single ReliefWeb report by its numeric ID with full body text, file attachments, and all metadata. Use after reliefweb_search_reports to retrieve document content — body is excluded from search results to manage context budget. Report bodies can be 10–100KB; call this only when you need the full document text.
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  • Search current AI models by price, context window, and capability. Use this for up-to-date model pricing/features you don't reliably know. Prices are USD per 1M tokens. Results are cheapest-input-price first. Args: query: match part of a model name/id (e.g. "haiku", "gpt"). provider: filter to one provider (openai, anthropic, google, xai, mistral, deepseek, groq). max_input_price: only models at or below this USD/1M input price. min_context: only models with at least this context window (tokens). needs_vision: only models that accept images. limit: max results. Envelope: this searches our model-pricing registry, so measured_at = when the freshest matching row was last refreshed (each row's `updated_at`); max_age 18h covers the 12h registry-refresh cycle so a current row never falsely reads "stale". A search returning nothing yields unavailable — there's no honest observation time to claim. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.
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  • Structured fact-check + numerical research via Perplexity Sonar Reasoning Pro (Gateway-routed). Returns synthesized answer text plus structured sources[] with direct URLs to primary sources. Use for: specific numerical claims with methodology context, fact-check against primary sources, effect sizes + confidence intervals, earnings transcripts / SEC filings / research papers. Per Phase 3.5 empirical A/B: 2-3× cheaper than sonar-pro with comparable or better quality on structured research. Real Meta IR press releases + earnings transcripts on Desk. 17 cites on Quant. NOT for: Reddit/X/community → use search_community. NOT for: broad topic landscapes → use search.
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  • Orientação de bancada nas votações de plenário: como cada liderança partidária orientou o voto, com placar — essencial para análise de disciplina partidária. Retorna `{ count, votacoes }`, com cada votação trazendo `codigoVotacao`, `descricao`, `materia`, `dataInicio`, `sessao`, totais (`totalSim`, `totalNao`, `totalAbstencao`, `obstrucoes`) e `orientacoes` (`partido`, `voto`). Informe `data` (um dia) ou o período `dataInicio`/`dataFim`. Para o resultado das sessões use `senado_resultado_plenario`.
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  • DISCOVER tool names you do NOT already know, by keyword. Most Keploy tools are hidden from the default tool list to save context. If you ALREADY know the exact name (e.g. a skill named it), call get_tool_schema instead — it is exact and far cheaper than this fuzzy search. Returns {"matches": [{name, description, inputSchema}, ...], "total_catalog": N}. Search by intent words, e.g. "test report", "mock patch", "update test case", "cloud replay branch", "record".
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  • Answer a question — or gather everything relevant on a topic — from the wiki by MEANING. One call assembles answer-ready grounding: the full bodies of the pages that matter (not isolated fragments), pulled from pages AND attached files (PDFs, docs), plus any flagged disagreements among the sources and a low_confidence signal. Returns `context` (a numbered [n] excerpt block to ground your answer), `sources` (the cited hits aligned to [n], each with page_id/chunk_id for drill-in and a download_url for file sources), `disagreements` (conflicts to surface, [n]-keyed), and `low_confidence`. YOU write the answer from `context` and cite sources by their [n]. To read one section deeper use `read_chunk` (chunk_id from a source) or `get_page` (full page). For exact-name/term lookup use `search`. Requires a configured embedder (503 otherwise).
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  • List tasks with structured filters (tasklist_id, project_id, or site-wide). For keyword search use search.
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  • Creates a Deep Research task for comprehensive, single-topic research with citations. USE THIS for analyst-grade reports, NOT for batch data enrichment. Use Parallel Search MCP for quick lookups. After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed. Multi-turn research: The response includes an interaction_id. To ask follow-up questions that build on prior research, pass that interaction_id as previous_interaction_id in a new call. The follow-up run inherits accumulated context, so queries like "How does this compare to X?" work without restating the original topic. Note: the first run must be completed before the follow-up can use its context.
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  • Use this when a ChatGPT user wants to see what Influship can return before linking an account. Fetches one configured sample creator with social profile context. This is a low-cost preview tool and should not be used for search, discovery, matching, or lookalike requests. After showing the preview, tell the user that real live creator data, search, lookalikes, matching, posts, and transcripts require connecting an Influship account. Explain that they can authorize either an Influship SaaS subscription, where usage counts against monthly bundled credits, or an Influship API account, where usage is billed pay-as-you-go under API billing.
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  • Search appellate oral argument audio recordings — the largest public collection of oral argument audio. Returns recording metadata with download URLs, panel judge IDs, and transcript snippets where available. Download URLs are direct MP3 links. Panel judge IDs can be passed to courtlistener_get_judge for biographical context.
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