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260,400 tools. Last updated 2026-07-05 06:01

"An analysis of financial or statistical charts" matching MCP tools:

  • Fetch statistical data from a Eurostat dataset with dimension filters. Returns decoded observations with dimension codes and labels, numeric values, and status flags (e.g., "p" = provisional, "e" = estimated). Call eurostat_get_dataset_info first to discover valid dimension codes and values. Apply filters to keep the result set manageable — large unfiltered queries may trigger an async response error. Use filters.geo for specific country/region codes, or geo_level for NUTS hierarchy filtering (mutually exclusive). Use last_n_periods for the N most recent periods without knowing the end date.
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  • Load fundamental workflow for valuation, cash flow, margins, balance sheet. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about company valuation, "is X a good buy", financial health, debt levels, profitability ratios, revenue trends, earnings quality, or any deep-dive company analysis. Can be combined with other workflow tools.
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  • Async extended variant of patent_landscape. Supports max_results up to 200 (vs 50 in sync mode) and an optional include_citation_graph flag that enriches each patent with its 2-level citation graph (parent patents that cite this one + child patents cited by this one). Returns immediately (<300ms) with a job_id. Poll the result with patent_landscape_result(job_id) after eta_seconds (~180s). Use for deep R&D white-space analysis, freedom-to-operate (FTO) audits, VC due diligence IP mapping, or large-scale competitor portfolio analysis. Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.
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  • 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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  • Get the full analysis (incl. scene breakdown) for a video — owned or public/competitor. Pass `platform` and `post_id` separately (the native post_id from analyze_post or list_videos — not the composite `id` field). Deep analysis runs async (~30-60s): right after analyze_post this returns {"status": "pending", "retry_after_seconds": N} — that is expected, not an error. Wait that long and call again until you get the full analysis. A genuine 404 means the post was never analyzed — call analyze_post(url) first.
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Matching MCP Servers

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    A Model Context Protocol server that generates lightweight ASCII charts directly in terminal environments, supporting line charts, bar charts, scatter plots, histograms, and sparklines without GUI dependencies.
    Last updated
    5
    13
    9
    MIT

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  • SEC filing intelligence for AI agents. Financials, screening, peer comparison for 5,000+ companies.

  • Energy-Charts (Fraunhofer ISE) MCP — European electricity generation, prices, and capacity.

  • List a public/competitor creator's videos by platform + handle. Sort by 'recent' or 'top' (best-performing); optionally with analysis inline. Only returns creators already in the analysis library — for one you haven't ingested yet this returns reason="creator_not_in_library" with a next_step of analyze_creator(platform, username), not an error.
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  • Upload a file (base64) and attach it to a page (editor+) — an image, PDF, dataset, etc. Returns the serve URL plus a ready-to-paste `markdown` snippet; then call update_page or patch_page to place it in the body (images render inline as ![](…), other files as a download card). The payload is inline base64 and rides through the model's context, so it is capped at 5 MB — keep it to small files (screenshots, charts, short PDFs). For larger files use request_attachment_upload (a direct PUT URL, bytes off-context), or the tela editor (drag-drop).
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  • Aspect grid between two natal charts using the tropical zodiac. Returns all inter-chart aspects using standard inter-chart orbs. Useful for relationship compatibility analysis. SECTION: WHAT THIS TOOL COVERS Bidirectional aspect matrix: every person1 planet to every person2 planet within orb. Does not produce a compatibility score — raw geometry only. House overlays are not included. SECTION: WORKFLOW BEFORE: asterwise_get_western_natal per person — understand charts individually first. AFTER: asterwise_get_western_composite — midpoint chart for the relationship itself. SECTION: INPUT CONTRACT person1, person2 — each WesternBirthData (date, time, lat, lon, timezone). house_system ignored for synastry payload. SECTION: OUTPUT CONTRACT data.aspects[] — person1_planet, person2_planet, type, exact_angle, orb data.total_aspects SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data. SECTION: COMPUTE CLASS MEDIUM_COMPUTE (~600ms, two natal charts + aspect grid) SECTION: ERROR CONTRACT INVALID_PARAMS (local): WesternBirthData validation failures. INTERNAL_ERROR: Any upstream API failure or timeout → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_western_composite — one merged midpoint chart vs synastry (two charts overlaid). asterwise_get_western_compatibility — numeric 0–100 score vs raw aspects.
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  • Talk to VARRD AI (~$0.25/turn). Describe any trading idea in plain language and the system handles everything — loading decades of market data, charting your pattern, running statistical tests, backtesting with stops, and generating exact trade setups. MULTI-TURN: First call creates a session. Keep calling with the same session_id, following context.next_actions each time. 1. Your idea -> VARRD charts pattern 2. 'test it' -> statistical test (event study or backtest) 3. 'show me the trade setup' -> exact entry/stop/target prices HYPOTHESIS INTEGRITY (critical): VARRD tests ONE hypothesis at a time — one formula, one setup. Never combine multiple setups into one formula or ask to 'test all' — each idea must be tested as a separate hypothesis for the statistics to be valid. Say 'start a new hypothesis' between ideas to reset cleanly. - ALLOWED: Test the SAME setup across multiple markets ('test this on ES, NQ, and CL') — same formula, different data. - NOT ALLOWED: Test multiple DIFFERENT formulas/setups at once — each is a separate hypothesis requiring its own chart-test-result cycle. If ELROND council returns 4 setups, test each one separately: chart setup 1 -> test -> results -> 'start new hypothesis' -> chart setup 2 -> etc. KEY CAPABILITIES you can ask for: - 'Use the ELROND council on [market]' -> 8 expert investigators - 'Optimize the stop loss and take profit' -> SL/TP grid search - 'Test this on ES, NQ, and CL' -> multi-market testing - 'Simulate trading this with 1.5 ATR stop' -> backtest with stops EDGE VERDICTS in context.edge_verdict after testing: - STRONG EDGE: Significant vs zero AND vs market baseline - MARGINAL: Significant vs zero only (beats nothing, but real signal) - PINNED: Significant vs market only (flat returns but different from market) - NO EDGE: Neither significant test passed TERMINAL STATES: Stop when context.has_edge is true (edge found) or false (no edge — valid result). Always read context.next_actions.
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  • Fetches today's fixed, curated Pollar daily brief with a greeting, headline, executive summary, themed sections, related events, and charts. Use only when the user explicitly asks for Pollar's daily brief or curated digest. Do not use it for questions about a subject, person, place, or country; use search_news instead. Locale changes the brief's language, not its editorial scope.
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  • Start an async GEM (10-factor competency) analysis on a candidate. Returns a task_id and analysis_id. Poll with careerproof_task_status(task_id) until status='completed', then fetch results with atlas_get_analysis(analysis_id) or careerproof_task_result(task_id, result_type='analysis', resource_id=analysis_id). Candidate CV must be fully parsed first -- verify with atlas_get_candidate. Types: gem_full (10 cr), gem_lite (5 cr), career_path (5 cr).
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  • Retrieve a completed analysis result by analysis ID. Returns scores, competency breakdown, and recommendations. analysis_id comes from atlas_start_gem_analysis response or atlas_list_analyses. Only works after analysis is completed -- check with careerproof_task_status first. Free.
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  • Perform statistical calculations on a list of numbers. Available operations: mean, median, mode, std_dev, variance Note: Use this tool to compute descriptive statistics over a list of numbers. To evaluate a single mathematical expression, use the calculate tool instead. Examples: statistics([1.0, 2.5, 3.0, 4.5, 5.0], "mean") # Returns 3.2 statistics([1.0, 2.5, 3.0, 4.5, 5.0], "std_dev") # Returns ~1.58
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  • List or search charts in a Helm repository. Provide a repository_url, then optionally filter by keyword (e.g. keyword='postgres'). Note: OCI registries (oci://) do not support browsing — for OCI you must already know the chart name, then call get_versions or get_values directly with that name.
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  • 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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  • Get a fast suitability score (0-100) for a US property without generating a full report. Call this when the user wants a quick go/no-go assessment or an initial screening before committing to a full analysis. Returns a single score with confidence level and one-sentence rationale. Consumes a partial (0.25) analysis credit from your AcreLens account.
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  • Async extended variant of patent_landscape. Supports max_results up to 200 (vs 50 in sync mode) and an optional include_citation_graph flag that enriches each patent with its 2-level citation graph (parent patents that cite this one + child patents cited by this one). Returns immediately (<300ms) with a job_id. Poll the result with patent_landscape_result(job_id) after eta_seconds (~180s). Use for deep R&D white-space analysis, freedom-to-operate (FTO) audits, VC due diligence IP mapping, or large-scale competitor portfolio analysis. Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.
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  • 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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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. 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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