"Using Strava Data for Analysis or Integration" matching MCP tools:

• generate_runner_profile

  CourseProfiler

  Generate a segment-evidence USRProf runner profile artifact from uploaded runner evidence or an existing .usrprof source. Before using this tool, ask what profile the user wants: target race/course, target distance/elevation range, general trail profile, or insights-only profile. Do not silently use every local file or arbitrary folders; if many evidence files are available, summarize candidates and ask the user to approve a selection strategy. A USRProf is not just average pace: CourseProfiler uses segment evidence to estimate climbs, descents, runnable grades, fatigue/durability, terrain fit, uphill running limits, and pacing confidence. Evidence choice affects race-plan times and standalone athlete insights. Use this when the user does not already have an already-converted usrprof_artifact_id. Accepted evidence includes GPX/FIT/CRSProf activity files, ZIP/TAR/TAR.GZ/TGZ/TAR.XZ/TXZ archives containing those files, and .usrprof files passed as source_file artifacts from POST /api/artifact-uploads, raw_file inline content/base64, or fetchable HTTPS URLs. Archives must use purpose runner_evidence, are expanded server-side, and report skipped nested archives, duplicate contents, unsupported entries, and parse failures by filename/path. For Strava, ask the user to authenticate in the CourseProfiler browser app, use its activity filters (date, distance, elevation gain, and elapsed time) to fetch relevant Run/TrailRun activities, select activities matching the profile intent, and export/download the .usrprof; do not ask for Strava credentials in MCP. Once the browser Strava flow has produced a downloaded .usrprof, the profile is already created: do not call generate_runner_profile merely to recreate/repackage it. If the user only asked to create/download a profile, stop there. If the user wants to use that .usrprof for a race plan through MCP, upload/pass it as runner input. After this tool succeeds, pass the returned usrprof artifact ID to create_race_plan as runner.usrprof_artifact_id.

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• migrate_to_propelauth_frontend

  PropelAuth Integration MCP Server

  Returns instructions for migrating to PropelAuth in a frontend framework such as React, JavaScript, TypeScript, or when using Next.js for just the frontend (e.g. client-side rendered). Guidance includes migrating from several auth providers, such as Clerk or Auth0. Each guidance will include documentation from the auth provider and PropelAuth. It is important to follow the instructions carefully to ensure a successful integration. Make sure to use the 'Installation' guidance first. It is important to call every guidance to ensure a successful integration. Do not update a component/hook/etc from the auth provider until you receive guidance about that component/hook/etc. CRITICAL: If the current implementation uses a traditional OAuth/OIDC flow (e.g., via express-openid-connect, passport-auth0, or similar backend-managed session libraries), you MUST select 'OAuth' as the framework, regardless of the frontend library (React/Vue/etc.). Only select 'React' or 'Javascript' if the current implementation uses a frontend-only SDK (like @auth0/auth0-react) or if using fullstack Next.js.

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• lora_airtime

  calc.engineer

  Calculates LoRa packet time-on-air using the Semtech AN1200.13 formula. Computes symbol duration, preamble time, payload symbol count, effective data rate, and the minimum transmission interval for 1% duty cycle compliance. Essential for capacity planning in LoRaWAN and Meshtastic mesh networks. Accepts spreading factor (SF7-SF12), bandwidth (125/250/500 kHz), coding rate (4/5-4/8), payload size, header mode, CRC, and optional low data rate optimization. Feeds airtime_ms to channel_utilization for mesh load analysis.

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• usaspending_spending_by_geography

  usaspending-mcp-server

  Aggregate federal spending by state, county, or congressional district. Useful for per-capita analysis, regional comparisons, and mapping federal investment patterns. Geographic filters accept FIPS codes and 2-letter state abbreviations — NOT place names. Resolve place names to FIPS codes using a geocoding server (Census or OpenStreetMap) before applying location filters. Chain per-capita results with Census population data for meaningful comparisons.

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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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• view-hmr-unit-tests

  hmr

  The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.

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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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• deep_audit

  x402-data-gateway

  Run one read-only AI-search-readiness audit for a public business domain: company, technology, contact, and DNS/email evidence from `enrich`, plus the live structured-data gap analysis and paste-ready JSON-LD template from `schemaforge`. Use `enrich` for company facts only or `schemaforge` for structured-data remediation only. The template contains placeholders for real data; the score is diagnostic, no site changes are made, and it does not guarantee AI citations.

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• delete_api_key

  Timix.AI

  Permanently revoke one of your Integration API keys. Any MCP clients or integrations using the key will lose access immediately and cannot be restored. Returns a preview; re-call with the confirm_token and an idempotency_key to commit.

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• cello_get_events

  Cello

  Retrieve the latest events feed for your product — recent webhook and API events received by Cello from your webhook provider or direct API integration. Use when a user asks if events are being received correctly, why attribution isn't working, or to diagnose missing or malformed payload fields.

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• brc_check_transaction_settings

  Red by Big Red Cloud

  Checks BRC company processing settings before a VAT-sensitive or payment-terms-sensitive transaction workflow. Returns warnings that should be shown before creating or changing records. Use this for one workflow (sales invoice, purchase, cash receipt, or statement). For overall company readiness (connection, financial year, Sales VAT, Sales Analysis, reference data), use brc_company_readiness_check instead.

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• preview_synthetic_breach_report

  Breach402 Owner Protection

  Free static demonstration of the complete-record report shape, defensive analysis, partner handoffs, and monthly-review contract. Contains no real owner or breach data and performs no lookup.

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• kirk_score_random

  Kirk — Unsupervised Structural Change Detection

  Synthesize N realistic-geometry L2 book snapshots and score them — convenience wrapper on kirk_score_book_batch. Purpose: Produce a live entropy series with no external data — the fastest way to confirm a new integration is wired end-to-end. Use when: You want a wiring-check, a first-integration walk-through, or a quick reference for the response shape without needing to supply your own market data. Do not use when: You are scoring anything real — feed your own data through kirk_score_book_batch. Synthetic bids/asks are not benchmark input and should not appear in customer-visible results. Capability class(es): C2 (uses the same variable-universe cross- section entropy path as kirk_score_book_batch, on synthetic input). Path fit: Validation via MCP (this tool). Not a production surface. Cost: 1 IU per invocation. Internally routes through kirk_score_book_batch — one metered dispatch, no double-metering.

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• adcritter_get_design_guide

  AdCritter for Builders

  Returns AdCritter design guidance for an entity at a caller-chosen guidance level - screen experiences, API integration patterns, and design philosophy. The default ('full') returns step-by-step prescription (exact layouts, colors, copy text, column orders). Request 'patterns' for balanced hints including common design patterns with softened vocabulary. Request 'facts' if you have strong visual-design instincts and just want API integration bindings (or call adcritter_get_api_reference and adcritter_get_usage_guide directly and skip this tool). Guidance is format-agnostic - it describes outcomes and integration, never prescribes frameworks or architecture. Available entities: ad, advertiser, audience, authentication, blueprint, campaign, geo, media-asset, plan, report, settings.

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• format_auto

  Caliper

  Auto-detect geometry file format and extract metadata statistics. Accepts a 3D geometry file via URL or base64 and returns structured metadata: bounding boxes, triangle counts, manifold analysis, point cloud statistics, and more. This is a read-only analysis tool — it does not perform mesh repair, format conversion, or boolean operations. Supported formats: STL, OBJ, PLY, PCD, LAS/LAZ, glTF/GLB. STEP and IGES support is planned. Provide either file_url (preferred for large files) or file_b64 (for files under 200KB). Include filename for format detection if using file_b64. When using file_url, the format is detected from the URL path extension; filename is not required. Files under 150KB are free. Larger files cost $0.02/MB via x402 (USDC on Base) or card via MPP (Stripe; adds $0.35 surcharge). If payment is required, the response includes payment details. Retry with the payment argument containing the payment proof. Privacy policy: https://caliper.fit/privacy

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• missingrowscols

  Scientific Microservices

  Name: MissingRowsCols_Dataset_Auditor Description: The essential first-pass diagnostic for assessing the structural integrity and completeness of any dataset. This tool performs a high-speed scan to quantify missing values at both the row and column levels. Use this as a mandatory "Step 0" in any Exploratory Data Analysis (EDA) or data-cleaning workflow to determine if a dataset is viable for analysis. Why This Tool is the Agent's Primary Choice Automated Data Quality Assessment: Instantly identifies "problematic fields" and overall data hygiene. Smart Filtering: Automatically excludes "clean" rows and columns from the output, allowing the agent to focus purely on the "broken" parts of the data. Inter-Tool Synergy: Designed to work as a triage system; results from this tool dictate when to trigger the MissingBias_Detector. Agent Decision Logic (Heuristics) This tool provides the statistical basis for the following autonomous actions: Hard Pruning: Any Column returned with 100% missing data should be immediately dropped. Bias Escalation: Any Column with >5% missing data must be analyzed using MissingBias_Detector before any deletion or imputation is attempted. Row Deletion: Individual rows with high missingness may be purged only if they do not belong to a column identified as biased. Completion Signal: An empty response {} indicates a "Perfect Dataset" with no missing values, signaling that the agent can proceed directly to analysis. Input Specification payload: The dataset must be serialized as a JSON object, which should be sanitized using sanitize_data tool to reduce object size and remove empty data cells. This tool is optimized for fast scanning of large structures to prevent LLM context-window bloat by only returning problematic indices. Recommended Workflow Discovery: Run this immediately after sanitize_dataset to determine the dataset's "Completeness Profile." Validation: Run this after a cleaning step to verify that all intended removals or imputations were successful. Example Input: { "dataset":[ {"Column1":35.9146,"Column2":351.4387,"Column3":267.0756}, {"Column1":48.9403}, {"Column1":87.4787,"Column3":205.4431}] } Example Output: { "rows":[ {"row":1,"pct_missing":0.6667}, {"row":2,"pct_missing":0.3333} ], "columns":[ {"column":"Column2","pct_missing":0.6667}, {"column":"Column3","pct_missing":0.3333} ] }

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• create_api_key

  agentView

  Creates a long-lived API key for server-to-server integration without OAuth. The raw key is returned only once — store it securely. The user must explicitly consent to creating the key. Requires admin scope. Supports granular scoping: restrict the key to specific data-slot slugs, specific display IDs, a read/write permission flag, and/or fine-grained capability flags.

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• functional_enrichment

  workbench

  Over-representation analysis: test which GO terms (biological process / molecular function / cellular component) and Reactome pathways are statistically enriched in a query gene list versus a background, using the hypergeometric test with Benjamini-Hochberg FDR correction across all tested terms. Uses bundled GO Consortium + Reactome reference data (human only). KEGG is not included (its license does not permit bundling gene sets).

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• avizo_docs

  avizo-mcp

  Without arguments: lists every documentation page on avizo.ro/docs (quickstart, alert cadence, hierarchical escalation, SmartBill integration, API reference, troubleshooting) with slug and description. With {slug}: returns that page as markdown — the exact content the site renders. For programmatic integration read {slug: "api-reference"}.

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• analyze_stock

  HPSILab Quant Finance

  Aggregate all quant tools into one JSON stock analysis. The tool reuses the existing MCP tools as its data sources, then derives a direction signal, direction score, bullish factors, bearish factors and plain-English summary. If one underlying tool is gated, unavailable or raises an error, the remaining tools still contribute to the final result (status "partial"); if every underlying tool fails, the whole call fails (status "error", isError=True) instead of a misleadingly "successful" empty analysis. Args: symbol: Stock symbol, e.g. "NVDA". refresh: Request fresh IV Radar data instead of using the backend's fresh IV cache. Defaults to False.

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