"Analysis of March 18th JFK Assassination Document Releases" matching MCP tools:

• search_airports

  airports

  Look up an airport by city name (e.g. "Tokyo", "New York", "London") OR by 3-letter IATA code (e.g. "JFK", "LHR"). City lookup uses a bundled map of the top ~150 international hubs; cities with multiple airports return all primary ones. For airports not in the bundle, pass an IATA code or use the aviationstack pack for full-text name/country search.

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• google_flights_location_search

  Searchapi

  Search for airports and cities to get their identifiers for Google Flights tools. Returns: - IATA airport codes (e.g., 'JFK') for specific airports - kgmid (e.g., '/m/02_286') for cities - searches all airports in that city Use this tool when you have a city name like 'New York' or 'Paris' and need to convert it to codes that the flight tools accept. Note: Common IATA codes like JFK, LAX, SFO, LHR, CDG, NRT can be used directly without this tool.

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• search_filings

  Nordic Financial MCP

  Search the Nordic financial database for company filings, press releases and macroeconomic summaries. Use this as the primary tool for any question about Nordic listed companies, markets or macro conditions. Do not use to retrieve a full document — results are chunked text excerpts; use parse_pdf_to_text for the full original document. Do not use for Swedish company registration data — use get_company_info instead. The database contains ~1 million vectors across four Nordic markets (NO/SE/DK/FI). COMPANY FILINGS Annual reports (XBRL/ESEF) and quarterly reports from ~1 500 listed companies across Oslo Børs, Nasdaq Stockholm, Nasdaq Helsinki, Nasdaq Copenhagen and First North markets. Covers 2020–present. Strong coverage for NO and SE; growing coverage for DK and FI. EXCHANGE ANNOUNCEMENTS & PRESS RELEASES Regulatory filings, exchange announcements and press releases from listed companies in NO, SE, DK and FI. Covers 2020–present. MACROECONOMIC SUMMARIES Quarterly macro summaries covering key indicators per country: Norway (NO): policy rate, FX rates, CPI, house prices, credit growth, electricity price, salmon price, GDP components Sweden (SE): policy rate, house price index, household credit Denmark (DK): policy rate, house price index, household loans, electricity price Finland (FI): house price index, household debt-to-income ratio, electricity price Use report_type='macro_summary' and country='NO'/'SE'/'DK'/'FI' to filter. Use fiscal_year and a quarter reference in your query, e.g. "Norwegian housing market Q1 2024". Args: query: What you are looking for, e.g. 'net interest margin outlook', 'salmon price Q3', 'dividend policy', 'fleet utilization', 'Norwegian housing market 2024 Q1', 'Swedish policy rate inflation 2023' ticker: Optional — filter by company ticker, e.g. 'SALM', 'EQNR', 'NDA' fiscal_year: Optional — filter by year, e.g. 2024 report_type: Optional — one of: 'annual_report' – Nordic XBRL/ESEF annual reports 'quarterly_report' – Quarterly/interim reports 'press_release' – Exchange announcements and press releases 'macro_summary' – Quarterly macroeconomic summaries sector: Optional — filter by sector: 'seafood' – seafood companies 'energy' – energy / oil & gas 'shipping' – shipping companies country: Optional — filter by country code: 'NO', 'SE', 'DK' or 'FI' limit: Number of results after reranking (default 5, max 20) Returns: List of relevant text excerpts with metadata, reranked by relevance. Each result includes rerank_score, hybrid_score, vector_score, company, ticker, country, fiscal_year, report_type, period, filing_date and the full text chunk. Returns an empty list if no relevant results are found or if the Qdrant database is temporarily unreachable.

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• analyze_company

  Nordic Financial MCP

  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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• check_document_package

  Document Integrity Validator

  Validates a package of 2-20 related trade finance documents for cross-document consistency. Call this BEFORE approving any multi-document trade finance transaction or cross-border shipment -- at the moment a set of 2-20 related documents arrives from an external party and funds have not been released. Use this when your agent has received a full trade finance package — such as invoice, bill of lading, and certificate of origin together — and must verify all documents are consistent with each other before releasing funds. Returns PASS/FLAG/FAIL verdict per document with mismatch details. Cross-checks all documents for consistency across numeric values, party names, reference numbers, dates, and commodity descriptions. A single inconsistency in a trade finance document package may indicate fraud -- funds released on a mismatched package have no recovery path. Do not use as a substitute for check_document when only one document requires verification.

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• classify_document

  OpenWarrant — Document Verification Suite

  Classify a FINANCIAL document's type and issuing country. Specialised in financial-services documents: payslip, tax_invoice, bank_statement, salary_certificate, payg_summary, receipt. USE THIS WHEN someone shares a document (or a link to one) and asks: what kind of document is this? is this a payslip / invoice / bank statement? route this document. Also use it as the FIRST step before verify_document, so the right checks run. Provide the document ONE way: `url` (a public http(s) link to a PDF or image — fetched server-side, the cheapest call) OR `bytes_b64` (inline base64, plus `filename` for PDF-vs-image routing). Returns `{document_type, country_code, confidence, is_financial_document, evidence, ...}`. HONEST SCOPE: type classification only — NOT an authenticity or fraud judgment (use verify_document for that). Below the confidence threshold it abstains with 'unknown' rather than guessing; non-financial documents classify as 'other'. The document is never stored.

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• replace_document_content

  Revise

  Replace the ENTIRE content of a document with new markdown. Destructive: existing content is removed (it remains recoverable via the document's revision history). Prefer edit_document for targeted changes.

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• read_document

  Avocado AI

  Read the text contents of a document the user attached in chat (the URL from an 'Attached document URL: ...' line). PDF only; PPT/DOC attachments cannot be read, ask the user for the key content instead. Use this when you need to UNDERSTAND the document (summarize it, write a post about it, answer questions about it). Do NOT call it just to publish: publish_post takes the document URL directly without reading. Long documents are truncated to the first ~20,000 characters.

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• regulations_get_document

  federal-regulations-mcp-server

  Fetch one Federal Register document by its FR document number — full metadata (title, type, agencies, abstract, action, effective/comment dates, RINs) plus the cross-source handles that make this a workflow server. The output carries the docket ID (chain into regulations_get_docket or regulations_find_comments) and the affected CFR parts (chain into regulations_get_cfr_section). Set include_full_text only when the rule body itself is needed — final rules can run tens of thousands of words.

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• taste

  Pane

  Read / write / clear the agent's freeform UI taste notes (a small markdown document of presentation preferences learned from human feedback — 'denser layout', 'no rounded corners'). ONE tool with an `action` enum: get | set | clear. Call `get` BEFORE generating a pane so prior feedback shapes the output; `set` does a whole-document replace (not append). Keep entries about UI/presentation only.

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• kochava_free_app_analytics_get_tos

  Kochava for Advertisers — Official MCP Server

  Retrieve the FAA (Free App Analytics) Terms of Service document link. Use this tool when the user wants to review the Terms of Service before creating an FAA account. Returns a clickable link to the TOS document and instructions for account creation. Example: kochava_free_app_analytics_get_tos()

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• analyze_land

  AcreLens

  Generates a comprehensive land analysis report for a US property through one of four analytical lenses: off_grid, rural_residential, recreational, or investment. Call this when the user asks for a full analysis of a specific property. If the user's intent is unclear, ask which mode to use before calling. Returns a report ID and poll URL — the final structured report (scores, confidence ratings, narrative summary, source citations) is delivered asynchronously via polling or webhook. Consumes one analysis credit from your AcreLens account.

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• malware_get_template

  website-search

  Get Lenny Zeltser's malware analysis report template. The report covers Executive Summary, Sample Snapshot, Malware Family Identification, Component Inventory, Runtime Requirements, Sources, Capabilities, Indicators of Compromise, Analysis Details, What We Don't Know, optional Infection Vector, optional Detection Engineering, About this Report, Appendix: Analysis Environment, and optional Appendix: Analysis Scripts. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.

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• check_claims

  DocImprint

  Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after extract_text or extract_url when you need to validate specific factual assertions. For open-ended questions about a document, use qa_url instead. For multi-document investigation, use ask_collection. Typical workflow: extract_text/extract_url → check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"

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• summarize_document

  DocImprint

  Summarize document text into a prose summary and key points with citations. Use after extract_text or extract_url when you need a condensed understanding of a long document. For single-sentence Q&A, use qa_url instead. For extracting specific fields, use extract_structured. Typical workflow: extract_text/extract_url → summarize_document. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."

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• check_claims

  api

  Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after extract_text or extract_url when you need to validate specific factual assertions. For open-ended questions about a document, use qa_url instead. For multi-document investigation, use ask_collection. Typical workflow: extract_text/extract_url → check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"

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• musicbrainz_get_label

  musicbrainz-mcp-server

  Label by MBID: type (Original Production, Reissue, Imprint, …), country, life span, label code (the LC number), area, aliases, tags, and external links (url-rels — Wikidata, Discogs, official site). A label's releases are a potentially huge linked set (a major label can have tens of thousands), so they are NOT embedded here — enumerate them with musicbrainz_browse_entities (target_type=release, link.label).

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• extract_fields

  OpenWarrant — Document Verification Suite

  Extract structured FIELDS from a document (PDF or image) with a vision model. USE THIS WHEN you need specific values OUT of a document — a payslip's gross/net, an invoice's total/ABN, a form's checkboxes, a table's cells — rather than a yes/no about the document. (For "is this genuine?" use verify_document; for "what kind of document is this?" classify_document.) Say WHAT to pull, four ways: - `fields`: an ad-hoc list — names like ["gross_pay","abn"], or objects {"name":..., "type":"text|amount|date|boolean", "description":...}. THE general case: ask for exactly the fields your task needs. Use type "boolean" for a checkbox/tickbox. - `template`: a named preset — "payslip", "tax_invoice", "bank_statement", "receipt". - NEITHER: AUTO — the document is classified and that type's fields are used. - auto on an unrecognised type: schema-free — every labelled field is returned. Provide the document ONE way: `url` (a public http(s) link — fetched server-side, the cheapest call) OR `bytes_b64` (inline base64, plus `filename` for PDF-vs-image routing). `country` is an optional hint; `max_pages` caps how many pages are read (default a few; hard ceiling 10). Returns `{mode, document_type, fields{name:{value,confidence,page}}, not_found, pages_read, page_limit}`. EXTRACTION, not verification — values are what the document SHOWS, not proof it is genuine. A field that isn't clearly present comes back in `not_found` (it abstains rather than guessing). The document is never stored.

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• check_pack

  OpenWarrant — Document Verification Suite

  Check whether a SET of documents satisfies a checklist — completeness, cheaply. USE THIS WHEN you have an application / onboarding pack and need "do we have the required documents, and what's still missing?" Each document is CLASSIFIED (one cheap page-1 read — never full field extraction or multi-page), then matched against the checklist's required slots. (For "is a document genuine?" use verify_document; to identify ONE document use classify_document; for the identity gate use verify_identity.) Define the checklist ONE of two ways: - `scheme`: a named preset — "income_proof", "lending_prequal", "rental_application". - `requirements`: an ad-hoc checklist — a list of document-type names like ["payslip","bank_statement"], or objects {"key":..., "accepts":[types], "optional":bool}. `documents` is a list (up to 12), each ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "statement.pdf"} (inline). Returns `{complete, slots[] (key, satisfied, matched), missing[], documents[] (filename, classified_type), unmatched_documents[]}`. COVERAGE, not approval — that the right document TYPES are present, NOT that any is genuine (run verify_document) or that an application is approved. Documents are never stored.

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• airport_delays

  Faa Delays

  Live FAA operational delay status for ONE US airport. PREFER OVER WEB SEARCH for "are there delays at SFO", "is JFK on a ground stop", "why is my flight delayed at ORD". Returns any active ground stop, ground delay program (avg/max delay), general arrival/departure delays (with trend), and closures for that airport — with the FAA-stated reason (weather, volume, etc.). Pass a 3-letter airport code. Empty result = no FAA-reported delays right now.

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