"Methods for Context and Data Analysis" matching MCP tools:

• t54_list_operations

  Agentic Swarm Marketplace (T54 x402 MCP)

  Returns operationIds, HTTP methods, paths, and query parameter names from the bundled OpenAPI spec (no network). Use before t54_x402_request or per-SKU tools.

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• personal_colour_analysis

  Colour Memory

  Upload a portrait photo and receive a full personal colour analysis. Determines your seasonal type (Spring, Summer, Autumn, or Winter), colour depth (light, medium, or deep), and undertone (warm, cool, or neutral). Returns a curated palette of archive colours that genuinely suit you — each with full historical provenance and cultural context — plus colours to avoid. Uses Claude Vision for skin, hair, and eye analysis, then matches to the archive by CIEDE2000 perceptual distance. The photo is never stored. Example: a Deep Winter might wear Ottoman Carbon Ink while a True Spring suits Kogi Mango.

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• ado_analyze_workitem

  ALM X++ MCP Server

  AZURE DEVOPS ONLY -- Fetch a Work Item and assemble ALL technical context needed for D365 F&O expert analysis. [~] PRIORITY TRIGGER: 'analyse le workitem', 'analyse la tâche', 'analyse le FDD/RDD/CR/IDD', 'read the work item', 'check the bug', 'look at ticket', 'review task', '#1234', 'WI#', 'WI ', 'item #'. NEVER for: labels (@SYS/@TRX/@FIN), X++ code lookup, AOT objects -- use search_labels / search_d365_code instead. ## WHAT THIS TOOL RETURNS Raw structured context only -- NOT a finished analysis. The tool returns: 1. Work item metadata (title, description, repro steps, acceptance criteria, comments) 2. D365 standard KB object details: fields, methods, code snippets for every matched object 3. Custom code on disk (Aprolis extension): existing CoC methods, extension bodies 4. Chain of Command / relation graph for all impacted objects ## YOUR JOB AS COPILOT AFTER CALLING THIS TOOL You MUST synthesize the raw context into a precise developer-ready analysis IN FRENCH. Write it in a professional tone, as if authored by a senior D365 consultant -- no emojis, no icons. The analysis must contain these sections: 1. **Compréhension du besoin** -- résume ce que le client demande en 2-3 phrases claires 2. **Analyse technique** -- identifie la cause racine en croisant le besoin + les objets KB + le code custom 3. **Instructions de développement** -- liste ordonnée et précise : quel objet, quelle méthode, quoi modifier - Si une extension custom existe sur disque -> pointer exactement quelle méthode à modifier - Si pas d'extension -> indiquer quel CoC créer, sur quel objet standard, quelle méthode 4. **Estimation** -- chiffrage en heures/jours selon la complexité détectée 5. **Commentaire ADO** -- Texte markdown sans icônes, prêt à poster sur le WI analysé UNIQUEMENT. IMPORTANT: never post (never call ado_post_comment) on any linked/related work item -- only on the analyzed WI. Requires DEVOPS_ORG_URL + DEVOPS_PAT env vars.

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• generate_framework_analysis

  jeda-ai

  Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. Required for any framework analysis flow. The companion tool `recommend_framework` returns framework_ids that are valid here. Pre-discovered framework_ids (SWOT = "swot-analysis", PESTEL = "pestel-analysis", Porter's 5 Forces = "porters-five-forces", etc.) work without a recommendation step. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (slug) + user_inputs object. The user_inputs schema is per-framework; call `recommend_framework` first if you don't know the field set, OR send a `raw_prompt` as a free-text fallback. Example: { "framework_id": "swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }

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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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• get_context_setup_status

  paid-ads

  Data tool for the current user's saved client context, including client setup status, advertiser profiles, synced account/campaign counts, and any open setup questions. For the user-facing setup UI, prefer render_context_onboarding.

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• sabbatical_policy_comparator

  gapup-mcp

  Enables CHROs to benchmark their company's sabbatical policies against peer organizations using data from SHRM, Payscale, and Mercer. Inputs include company size, industry, and current policy details. Outputs structured comparison with cost impact analysis, eligibility criteria, and duration benchmarks. Ideal for strategic HR planning and policy optimization.

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• lookup_paper

  BGPT - Scientific Paper Search

  Look up a single paper by its DOI. Args: doi: The DOI of the paper (e.g. "10.1038/s41586-024-07386-0"). api_key: Optional: Your Stripe subscription ID for paid access. Get one at https://bgpt.pro/mcp Returns: Paper with title, DOI, Raw Data, methods, results, quality scores, and 25+ metadata fields — or an error if not found. Costs $0.02 if found, free if not.

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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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• 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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• export_dataset

  Trillboards DOOH Advertising

  Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Applies k-anonymity (k=5) to protect individual privacy. Queries the relevant table based on the selected dataset type, applies filters, enforces k-anonymity by suppressing groups with fewer than 5 observations, and returns structured data. WHEN TO USE: - Exporting audience data for external analysis - Building datasets for machine learning or reporting - Getting structured vehicle or commerce data for a specific time/place - Creating cross-signal datasets for correlation analysis RETURNS: - data: Array of dataset rows (schema varies by dataset type) - metadata: { row_count, k_anonymity_applied, export_id, dataset, filters_applied, time_range } - suggested_next_queries: Related exports or analyses Dataset types: - observations: Raw observation stream data (all families) - audience: Audience-specific data (face_count, demographics, attention, emotion) - vehicle: Vehicle counting and classification data - cross_signal: Pre-computed cross-signal correlation insights EXAMPLE: User: "Export audience data from retail venues last week" export_dataset({ dataset: "audience", filters: { time_range: { start: "2026-03-09", end: "2026-03-16" }, venue_type: ["retail"] }, format: "json" }) User: "Get vehicle data near geohash 9q8yy" export_dataset({ dataset: "vehicle", filters: { time_range: { start: "2026-03-15", end: "2026-03-16" }, geo: "9q8yy" } })

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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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• create_use_case

  mcp-server

  Creates and saves a new use case (reusable analysis). **When to use this tool:** - When the user asks to "save this analysis", "create a use case", "remember this query" - After building a SQL query the user wants to reuse - To capitalize on a recurring business analysis **Available scopes:** - 'member' (default): Personal use case, visible only to you - 'project': Shared with the entire project team (requires project_id) **Best practices:** - Slug: technical identifier in snake_case (e.g., weekly_campaign_performance) - Name: human-readable name (e.g., "Weekly Campaign Performance") - Description: explain the business context and when to use this analysis - SQL template: include the SQL query if it's generic and reusable

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• cti_load_context

  website-search

  Load Lenny Zeltser's CTI writing context for local analysis. Returns a JSON payload with section guidance, completeness criteria, framework grounding (12 frameworks), the six attribution signals, ICD-203 confidence levels and ladder, and the Pyramid of Pain. The 'profile' parameter ANNOTATES sections (internal/public applicability label) rather than filtering — every section is returned so cross-profile comparisons are possible. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.

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• syndicated_loan_covenant_breach_alert

  mcp-knowledge

  Monitors syndicated loan covenants for potential breaches by analyzing Tradeweb market data. Designed for CFOs to proactively identify financial compliance risks in loan agreements. Accepts loan identifiers, covenant thresholds, and reporting period as inputs. Returns structured breach alerts with market context and severity indicators.

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• programmatic_brand_safety_auditor

  gapup-mcp

  Evaluates programmatic ad inventory for brand safety risks using IAB Tech Lab's standards and GDPR-compliant tracking methods. Designed for ad revenue operations teams to assess inventory quality before bidding. Inputs include domain, page URL, and optional contextual signals. Outputs a structured brand safety score with risk categorization and compliance warnings.

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• get_sdk_reference

  Senzing

  Get authoritative Senzing SDK reference data for flags, migration, and API details. Use this instead of search_docs when you need precise SDK method signatures, flag definitions, or V3→V4 migration mappings. Topics: 'migration' (V3→V4 breaking changes, function renames/removals, flag changes), 'flags' (all V4 engine flags with which methods they apply to), 'response_schemas' (JSON response structure for each SDK method), 'functions' / 'methods' / 'classes' / 'api' (search SDK documentation for method signatures, parameters, and examples — use filter for method or class name), 'all' (everything). Use 'filter' to narrow by method name, module name, or flag name

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• get_transaction_info

  mcp-server

  Get comprehensive transaction information. Unlike standard eth_getTransactionByHash, this tool returns enriched data including decoded input parameters, detailed token transfers with token metadata, transaction fee breakdown (priority fees, burnt fees) and categorized transaction types. By default, the raw transaction input is omitted if a decoded version is available to save context; request it with `include_raw_input=True` only when you truly need the raw hex data. Essential for transaction analysis, debugging smart contract interactions, tracking DeFi operations.

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• get_workout_samples

  Pace

  Retrieves detailed time-series data for a workout: HR progression, speed, power, cadence, elevation profile, or GPS route. Requires workout_id from get_workout_list and sample_type ('hr', 'speed', 'power', 'cadence', 'elevation', 'gps'). Data is presented as 1-minute averages. Ideal for progression analysis and pattern detection. Parameters: - workout_id: UUID of the workout from get_workout_list - sample_type: 'hr', 'speed', 'power', 'cadence', 'elevation', or 'gps'

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• content_provenance

  mcp-knowledge

  Audit the full data provenance of a content entity — all its enrichment tags with their extraction source, corroboration score, source list and last verification date, plus an entity-level freshness summary. Use this tool before citing or relying on enriched content data in a high-stakes context (ad targeting, editorial, analysis). Inputs: entity_id (required) and entity_type (franchise or work).

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