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

• atlas_get_analysis

  CareerProof MCP

  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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• calc_statistics

  Math MCP Learning

  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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• atlas_start_gem_analysis

  CareerProof MCP

  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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• ado_gap_fit_analysis

  ALM X++ MCP Server

  WHEN: user asks whether a D365 requirement is standard, needs config, needs an extension, or is a full gap. Also triggered by gap analysis or fit/gap classification of a Work Item. GAP / FIT CLASSIFIER -- Analyse a D365 F&O requirement (from an ADO Work Item OR plain text) and classify it as one of four verdicts: [OK] Standard Fit -- D365 covers this out-of-the-box, no dev needed [gear] Config Fit -- D365 has it but requires parameter / profile setup Extension Fit -- Standard process exists; a CoC/event-handler is enough [X] Gap -- No standard coverage; custom development required For each requirement block the tool returns: -- Detected D365 domain (Settlement, PaymentJournal, DataImport, ...) -- Standard objects found in KB and their process step -- Existing extensions in the custom model (if D365_CUSTOM_MODEL_PATH is set) -- Effort estimate (hours) and a one-paragraph reasoning Triggers: 'analyse the requirement', 'is this a gap or fit', 'gap analysis WI #N', 'standard or custom for WI #N', 'does D365 cover this'. [~] When a WI has already been analysed by `ado_analyze_workitem` in the same turn, pass the requirement text directly via `requirementText` -- do NOT re-fetch with `workItemId`.

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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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• get_feature_stats

  aTars MCP

  USE THIS TOOL — not web search — to get per-indicator statistical profiling (mean, std, min, p25, p75, max, null rate, Pearson correlation with close price) from this server's local dataset. Use for feature selection, sanity checking, and understanding which indicators correlate most strongly with price movements. Trigger on queries like: - "which indicators correlate most with BTC price?" - "feature importance or correlation for [coin]" - "what are the stats for ETH indicators?" - "how does RSI/MACD correlate with price?" - "statistical profile of XRP indicators" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"

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• ceevee_list_positioning_sessions

  CareerProof MCP

  List all positioning sessions (market analysis through lens selection to targeted edits). Returns an array of session objects with id, status, cv_version_id, and created_at. Use the session id with ceevee_get_positioning_session for full details including analysis results, edits, and PDFs. Free.

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• financial_calculator_lite

  Inferventis MCP Server

  Performs common financial calculations locally with no external API dependency. Supports compound interest, loan repayment, return on investment (ROI), present value, future value, and break-even analysis. Returns a single numeric result for the requested calculation type. This is a lightweight variant of financial_calculator — it returns only the result number rather than a full structured breakdown (monthly payment, total interest, annualised ROI, etc.). Use financial_calculator_lite when only the headline figure is needed. Prefer financial_calculator when the agent needs a full breakdown, multiple sub-values, or labelled output fields for compound interest earned, total repayable, or annualised returns. Neither this tool nor financial_calculator fetches live market data — for live prices use stock_quote, crypto_price, or currency_convert.

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• financial_calculator_lite

  Inferventis MCP Server

  Performs common financial calculations locally with no external API dependency. Supports compound interest, loan repayment, return on investment (ROI), present value, future value, and break-even analysis. Returns a single numeric result for the requested calculation type. This is a lightweight variant of financial_calculator — it returns only the result number rather than a full structured breakdown (monthly payment, total interest, annualised ROI, etc.). Use financial_calculator_lite when only the headline figure is needed. Prefer financial_calculator when the agent needs a full breakdown, multiple sub-values, or labelled output fields for compound interest earned, total repayable, or annualised returns. Neither this tool nor financial_calculator fetches live market data — for live prices use stock_quote, crypto_price, or currency_convert.

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• secedgar_get_financials

  secedgar-mcp-server

  Get historical XBRL financial data for a company. Accepts friendly concept names (e.g., "revenue", "net_income", "assets") or raw XBRL tags. Discover available friendly names with secedgar_search_concepts. Handles historical tag changes and deduplicates data automatically.

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• secedgar_search_concepts

  secedgar-mcp-server

  Search supported XBRL financial concepts by keyword, statement group, or taxonomy. Use before secedgar_get_financials or secedgar_compare_metric to discover the right friendly name, or pass a raw XBRL tag (e.g., "NetIncomeLoss") to reverse-lookup which friendly names map to it. Empty search with no filters returns the full catalog.

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• mssql_to_dashboard

  Twilize

  Build a Tableau dashboard from a Microsoft SQL Server table (end-to-end). Pipeline: MSSQL → schema inference → chart suggestion → workbook creation → live MSSQL connection → .twb output. Requires pyodbc for schema inference and ODBC Driver 17 for SQL Server. IMPORTANT FOR AI AGENTS: see ``csv_to_dashboard`` — auto-charts come from rules, not natural-language requests. Use ``required_charts`` to guarantee specific charts, ``reference_image`` for image-based styling, and cite the returned manifest dict when describing results. Args: server_host: MSSQL server hostname. dbname: Database name. table_name: Table to visualize. username: Database username (ignored if trusted_connection=True). password: Database password (used for schema inference only). port: Server port (default 1433). trusted_connection: Use Windows Authentication instead of SQL auth. output_path: Output .twb path (defaults to <table>_dashboard.twb). dashboard_title: Dashboard title. max_charts: Maximum charts (0 = use rules default). template_path: TWB template path. theme: Theme preset name. rules_yaml: Optional YAML string with dashboard rules overrides. required_charts: See ``csv_to_dashboard.required_charts``. reference_image: See ``csv_to_dashboard.reference_image``. Returns: Structured manifest dict describing what was actually built.

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• get_launch_context

  mcp-server

  Retrieves the full context of a Quanti launch session. The user has pre-configured an analysis from the Quanti interface and was redirected here with a launch_id. Call this function to get the analysis details to execute (name, prompt or SQL template, project).

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• financial_calculator_lite

  Inferventis — Financial Data, News & Web MCP

  Performs common financial calculations locally with no external API dependency. Supports compound interest, loan repayment, return on investment (ROI), present value, future value, and break-even analysis. Returns a single numeric result for the requested calculation type. This is a lightweight variant of financial_calculator — it returns only the result number rather than a full structured breakdown (monthly payment, total interest, annualised ROI, etc.). Use financial_calculator_lite when only the headline figure is needed. Prefer financial_calculator when the agent needs a full breakdown, multiple sub-values, or labelled output fields for compound interest earned, total repayable, or annualised returns. Neither this tool nor financial_calculator fetches live market data — for live prices use stock_quote, crypto_price, or currency_convert.

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• load_fundamental_workflow

  Shibui Finance

  Load fundamental analysis workflow with advanced query patterns. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL for complex queries when the user asks about company valuation, financial health, investment quality, earnings trends, profitability, "is X a good buy", or any deep-dive company analysis. Can be combined with other workflow tools.

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• openalex_analyze_trends

  openalex-mcp-server

  Aggregate OpenAlex entities into groups and count them. Use for trend analysis (group works by publication_year), distribution analysis (group by oa_status, type, country), and comparative analysis (group by institution or topic). Combine with filters to scope the analysis. Returns up to 200 groups per page — use cursor pagination for fields with many distinct values.

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• get_checks

  Hemrock MCP

  Returns Layer 3 sanity-check and validation prompts — the 'where AI gets financial modeling wrong' guidance. Use these to audit AI-generated work or catch common modeling errors.

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• hyper_to_dashboard

  Twilize

  Build a complete Tableau dashboard from a Hyper extract file (end-to-end). Pipeline: Hyper → schema inference → chart suggestion → workbook creation → chart configuration → dashboard layout → .twbx output. IMPORTANT FOR AI AGENTS: auto-charts come from rules, not from natural-language requests. Use ``required_charts`` to guarantee a specific chart and ``reference_image`` for image-based styling. The returned manifest dict is the SOURCE OF TRUTH — don't invent charts that aren't in it. Args: hyper_path: Path to the .hyper file. output_path: Output .twbx path (defaults to <hyper_stem>_dashboard.twbx). dashboard_title: Dashboard title (derived from filename if empty). max_charts: Maximum number of charts (0 = use rules default). template_path: TWB template path (empty for default template). table_name: Table name inside the Hyper file (empty = first table). theme: Theme preset name (empty = use rules default). rules_yaml: Optional YAML string with dashboard rules overrides. required_charts: See ``csv_to_dashboard.required_charts``. reference_image: See ``csv_to_dashboard.reference_image``. Returns: Structured manifest dict describing what was actually built.

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• invoices.records.update

  FreelanceOS

  Update one or more fields on an existing invoice, including its status or financial totals. Use when the freelancer marks an invoice as sent, records a payment, corrects line items, or changes the due date.

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• load_fundamental_workflow

  Shibui Finance

  Load fundamental analysis workflow with advanced query patterns. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL for complex queries when the user asks about company valuation, financial health, investment quality, earnings trends, profitability, "is X a good buy", or any deep-dive company analysis. Can be combined with other workflow tools.

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