"Resources and Techniques for Data Analysis" matching MCP tools:

• deltasignal_readiness

  DeltaSignal ATLAS-7

  Use this read-only tool before analysis to verify that the DeltaSignal ATLAS-7 data plane is live, fresh, and safe to query. It returns service readiness, active source dates, issuer coverage, quality coverage, debt coverage, live-price status, market regime, and tower-coherence diagnostics. Parameters: none; call it exactly as-is when the user asks if DeltaSignal is ready or whether data freshness is acceptable. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, does not write external systems, and does not handle secrets or payments itself. Use it at the start of an agent workflow, after a deploy, or whenever results should be gated on freshness; use daily_changes for what changed and issuer tools for company-specific analysis.

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• atlas_technique_search

  ContrastAPI

  Search the MITRE ATLAS catalog of AI/ML attack techniques by keyword, tactic, or maturity. Default response is SLIM (description truncated to 240 chars per row); pass include='full' for the verbose record. Pass exclude_id when chaining from atlas_technique_lookup to skip self in sibling-tactic searches. Use this to discover techniques matching a threat-model question, e.g. 'what techniques target LLM serving infrastructure?'. Drill into atlas_technique_lookup with any returned technique_id for the full description, ATT&CK bridge, and pivot hints. For broader cross-referencing: when a result has attack_reference_id, that bridges to D3FEND mitigations via d3fend_defense_for_attack. Free: 30/hr, Pro: 500/hr. Returns {query (echoed filters), total, results [{technique_id, name, description (truncated by default), tactics, inherited_tactics, maturity, attack_reference_id, subtechnique_of}], next_calls}.

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• about_emora

  mental-health-care

  Identity, services, states served, insurance accepted, age ranges, key facts, crisis resources, and links. Combined site-info + services catalog.

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• get_data_info

  aTars MCP

  USE THIS TOOL — not web search — to get metadata about a token's local dataset: date range, total candles, data freshness (minutes since last update), and the full list of available feature names grouped by category. Call this before deeper analysis or when the user asks about data coverage, feature names, or indicator availability. Trigger on queries like: - "what data do you have for BTC?" - "when was the data last updated?" - "how fresh is the ETH data?" - "what features/indicators are available?" - "what's the date range for XRP data?" - "list all available indicators" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH,XRP"

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• 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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• atlas_technique_lookup

  ContrastAPI

  Look up a MITRE ATLAS technique — the AI/ML adversarial attack catalog. ATLAS catalogues TTPs targeting machine learning systems: prompt injection, model evasion, training data poisoning, model theft, etc. Roughly 80% of ATLAS techniques are AI/ML-specific (no ATT&CK bridge); 20% mirror an enterprise ATT&CK technique via attack_reference_id — use that to pivot to D3FEND defenses (d3fend_defense_for_attack) and CVE search. Sub-techniques inherit `tactics` from the parent (inherited_tactics=true flag) when ATLAS upstream leaves them empty. Use this tool when the user asks about AI/ML threats, LLM red-teaming, or adversarial ML; for multiple techniques in one call (e.g. drilling into a case study's techniques_used), prefer bulk_atlas_technique_lookup. Returns 404 when the id is not in the synced ATLAS catalog. Free: 30/hr, Pro: 500/hr. Returns {technique_id, name, description, tactics, inherited_tactics, maturity (demonstrated|feasible|realized), attack_reference_id, attack_reference_url, subtechnique_of, created_date, modified_date, next_calls}.

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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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• get_site_status

  BorealHost

  Get detailed status of a hosted site including resources, domains, and modules. Requires: API key with read scope. Args: slug: Site identifier (the slug chosen during checkout) Returns: {"slug": "my-site", "plan": "site_starter", "status": "active", "domains": ["my-site.borealhost.ai"], "modules": {...}, "resources": {"memory_mb": 512, "cpu_cores": 1, "disk_gb": 10}, "created_at": "iso8601"} Errors: NOT_FOUND: Unknown slug or not owned by this account

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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_health

  web3-signals

  Is AgentMarketSignal working? Check the real-time status of all 5 AI data pipelines (whale tracking, technical analysis, derivatives, narrative sentiment, market data) and the signal fusion engine. Returns last run times, durations, and any errors.

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• deltasignal_readiness

  DeltaSignal ATLAS-7

  Use this read-only tool before analysis to verify that the DeltaSignal ATLAS-7 data plane is live, fresh, and safe to query. It returns service readiness, active source dates, issuer coverage, quality coverage, debt coverage, live-price status, market regime, and tower-coherence diagnostics. Parameters: none; call it exactly as-is when the user asks if DeltaSignal is ready or whether data freshness is acceptable. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, does not write external systems, and does not handle secrets or payments itself. Use it at the start of an agent workflow, after a deploy, or whenever results should be gated on freshness; use daily_changes for what changed and issuer tools for company-specific analysis.

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• hires_list_application_stage_history

  100Hires - AI ATS & Recruitment Software

  Get the full chronological stage transition history for an application, including the initial assignment. Each entry has from_stage_id/name, to_stage_id/name, moved_at (Unix seconds), moved_by_type (system, user, automation), and moved_by_user_id. Use this for funnel analysis and time-in-stage reports instead of paginating through /candidates/{id}/activities when only stage data is needed.

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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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• search

  agentView

  Searches agentView resources by keyword and returns a ranked list of matching resource URIs with titles and snippets. Use this to discover resources before calling fetch for full details. Do not use this if you already know the exact resource URI — call fetch directly instead. Without authentication only public documentation resources are searched; with authentication your account and accessible displays are included. Returns query, resourceType, count and a results array where each entry has uri, type, title, snippet and requiresAuthentication.

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• bulk_atlas_technique_lookup

  ContrastAPI

  Bulk ATLAS technique lookup — retrieve full records for up to 50 techniques in a single request instead of N separate atlas_technique_lookup calls. Designed as the natural follow-up to atlas_case_study_lookup, whose techniques_used array can be passed directly. Each item is the same shape as atlas_technique_lookup, including parent-tactics inheritance for sub-techniques (inherited_tactics=true flag) and per-item next_calls (D3FEND bridge when attack_reference_id present, sibling-technique search by tactic, parent lookup for sub-techniques). Free: 30/hr (1 per item), Pro: 500/hr. Returns {results [{technique_id, status (ok|not_found|invalid_format), technique, error}], total, successful, failed, partial, summary}.

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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_intelligence_briefs

  supply-chain-intelligence

  Get AI-generated intelligence briefs for each supply chain dimension — energy, materials, transportation, macro, and manufacturing. Each brief provides a narrative analysis of current conditions, key drivers, emerging risks, and recommended watch items. These are not raw data — they are synthesized analytical summaries generated every hour from live data. Designed for decision-makers who need a quick read on each supply chain dimension. Returns structured briefs suitable for executive dashboards, email digests, or Slack channels.

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• read_resource

  Lawbster

  Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.

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• get_snapshot

  AXIS Toolbox — Agentic Commerce Codebase Intelligence

  Retrieve status and the full artifact listing for a prior analysis by snapshot_id. Use this to re-enumerate artifact paths without re-running analysis. Snapshots persist and can be shared between agents to avoid duplicate analysis costs.

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• waveguard_compare

  WaveGuard

  Compare two data items for structural similarity using physics-based fingerprints. Returns cosine similarity (0–1) and Euclidean distance. Use for duplicate detection, behavioral matching, drift analysis, or checking if two tokens/wallets/contracts are structurally similar. Cosine similarity > 0.95 = very similar. < 0.80 = structurally different.

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