What Is MCP? Why Every AI Company Is Racing to Adopt It

The Model Context Protocol (MCP) standard developed by Anthropic has rapidly expanded into health wearables and smart sports watches. Strava launched the first official MCP connector for runners and cyclists, followed less than a month later by COROS releasing an official beta connector. Meanwhile, although Garmin has yet to release an official response, open-source community solutions like garmin_mcp demonstrate that full AI integration across fitness hardware is becoming mainstream.What is MCP and Why Fitness Brands are Racing to IntegrateMCP (Model Context Protocol) is an open protocol that allows Large Language Models (LLMs) like Claude, ChatGPT, and Gemini to directly interface with external real-time data sources and tools. Instead of providing generic advice based on manual text inputs, AI can read your entire personal activity history.For athletes, instead of opening apps, filtering heart rate graphs, and comparing metrics manually, you can simply ask AI: "Did my training load increase or decrease this week compared to last week?" or "Was my Easy Run pace optimal for recovery?". AI provides precise feedback backed by personal metrics, acting like a 24/7 personal coach.Strava MCP: Ecosystem Pioneer and API TighteningThe wearable MCP race escalated on June 1, 2026, when Strava debuted its official read-only MCP connector for paid subscribers. Secured by OAuth authentication, AI models can inspect workouts, GPS tracks, elevation, and power meter data.Thanks to open MCP standards, Strava MCP integrates seamlessly with Anthropic tools like Claude Cowork and Claude Code. Concurrently, Strava tightened third-party API terms by implementing monthly fees and 90-day limits on select endpoints to prevent uncompensated AI scraping.Strava MCP currently operates strictly in Read-Only mode for maximum security. AI can analyze numbers but cannot edit, delete, or create workouts inside your Strava account.Coros MCP Beta: Fast Setup with Data LimitationsJoining the movement, COROS launched an official MCP Beta in May 2026, linking COROS accounts directly to Claude and ChatGPT. Setup is simple: copy your regional MCP URL (e.g., https://mcp.coros.com/mcp), paste it into Claude Connectors or ChatGPT Developer Mode, and authorize.However, Coros MCP has key limitations:Data Granularity: Provides workout-level summaries only, lacking lap-by-lap or second-by-second telemetry.Permissions: Strictly Read-Only, unable to push workouts or schedules to watches.Platform Compatibility: Requires paid ChatGPT accounts, while Web Gemini lacks custom MCP connector support (requiring Gemini CLI).Garmin garmin_mcp: Powerful 110-Tool Community SolutionGarmin — the market leader in sports watches — has not released an official MCP connector. However, open-source developers filled this gap with the garmin_mcp project by Taxuspt on GitHub.With over 1,000 stars and 324 forks, garmin_mcp on GitHub packs over 110 tools covering 90% of python-garminconnect. It enables features unsupported by official read-only connectors:Analyze power zone distribution from your latest ride.Compare CTL (Chronic Training Load), ATL (Acute Training Load), and TSB (Training Stress Balance) over 6 weeks.Generate walk-run interval workouts and automatically sync schedules straight into Garmin Connect.Because garmin_mcp is community-built, users authenticate with Garmin Connect credentials via third-party libraries. Exercise caution before sharing account credentials.In March 2026, Garmin updated API authentication, temporarily breaking garth and python-garminconnect. This highlights the risk of unofficial MCP tools: reliance on unannounced endpoints means service can break whenever Garmin updates system APIs.Side-by-Side Comparison: Strava vs Coros vs Garmin MCPHere is a concise comparison of current MCP integration paths:Strava MCP (Official): Read-Only | Paid Subscriber | Activity, GPS, Power | High OAuth Security | No sleep, HRV, or recovery metrics.Coros MCP (Official Beta): Read-Only | Paid AI Account | Workout Summaries | Easy URL Setup | No lap or second-by-second data.Garmin garmin_mcp (Community): Read & Write | Open Source | 110+ Tools, CTL/ATL/TSB, Workout Builder | Most Feature-Rich | Uncertified, risk of broken APIs and credential storage.How to Get Started with Your Current Fitness WearableIf you use Strava or Coros, setup is effortless: navigate to Connector Settings in Claude.ai (or ChatGPT Developer Mode), paste the official MCP link, and authenticate.For Garmin users eager for AI features, install Taxuspt's garmin-mcp.dxt extension in Claude Desktop. Authenticate once via garmin-mcp-auth to store an OAuth Token, allowing seamless AI chats without re-entering passwords. This provides a powerful interim solution until Garmin releases an official MCP server.

An AI agent may plan extremely well, yet it still cannot update Notion, read GitHub issues, or retrieve reports from Google Drive without the right connection. By combining Hermes Agent with MCP, users can turn a conversation into a practical workflow while clearly controlling which tools and permissions the agent may use. If you are not yet familiar with Hermes memory and its ability to create skills, our guide to what Hermes Agent is provides the necessary foundation. This article focuses on how MCP extends Hermes beyond the terminal so it can work with everyday data and services. What does MCP add to Hermes Agent? MCP is a connection standard between an AI application and a server that provides tools or data. It can be understood as an adapter layer: Hermes remains the agent responsible for understanding the goal and choosing the next step, while each MCP server contributes specific actions such as searching Notion, reading a pull request, creating an issue, or querying files. According to the Hermes Agent MCP documentation, Hermes supports local servers over stdio and remote servers over HTTP. At startup or after a configuration reload, Hermes discovers the tools exposed by each server and registers them in its normal tool system. Users therefore do not need to write a native Hermes tool for every service that already has a suitable MCP server. MCP does not automatically make a workflow safe. A server may expose tools that read, write, create, and delete data. Hermes supports filtering per server, allowing users to enable only the operations they need instead of exposing every capability to the model. How to connect MCP without granting excessive access The standard Hermes installation already includes MCP support. Users can open the picker with hermes mcp, view the catalog with hermes mcp catalog, and test a connection with hermes mcp test. Nous Research reviews entries before they enter the Hermes catalog, but its documentation still recommends reading the manifest, source repository, and installation commands before use. For a server outside the catalog, users can add an HTTP connection or a stdio command to config.yaml. After completing OAuth or configuring the required environment variables, reload MCP and ask Hermes to list the available tools. This simple check reveals servers that failed to connect or tools that were accidentally filtered out. Begin with read access The safest setup is to connect one server, enable read only tools, and test with nonsensitive data. Add create or update permissions only after results are stable. Deletion, sharing changes, and outbound publishing should require human approval. Notion initially needs only search and page reading access. GitHub can be limited to reading repositories, issues, and pull requests. Google Drive access should be limited by folder, account, and required OAuth scope. Three practical workflows with Notion, GitHub, and Google Drive Turn Notion into a knowledge center The official Notion MCP allows an agent to search, read, and update workspace content under the authenticated user's permissions. A useful workflow lets Hermes collect meeting notes, find relevant decisions, and prepare a summary on the project page. Hermes can create a draft first so a user can review it before updating status or assigning work. Notion MCP uses user based OAuth, so it does not fit every unattended process. For scheduled automation, verify how the server maintains authentication and avoid designing a workflow around operations that OAuth cannot support in a headless environment. Coordinate development work through GitHub The GitHub MCP Server is provided and maintained by GitHub, allowing AI tools to work with software development data according to account permissions. Hermes can read new issues, compare them with repository changes, and draft a progress report. It can then prepare issue text or release notes while waiting for an owner to approve the write operation. This workflow works best with clear criteria. For example, Hermes can summarize only pull requests merged during the previous seven days, group them by label, and connect each change to its related issue. A second MCP server can then send the result to Notion as a weekly report. Summarize files and reports from Google Drive With a compatible Google Workspace MCP server, Hermes can find Drive files, read permitted content, and feed data into a reporting process. For example, the agent can locate a sales report in a fixed folder, extract selected metrics, and create a summary for Notion or a GitHub issue. Google collects its official MCP projects in the Google MCP repository, including a path for Google Workspace integration. However, several community Drive servers have different maintenance histories. Check the source, update history, and OAuth scopes of the specific server instead of installing one based only on its name. Combine multiple MCP servers into a controlled workflow A complete workflow can begin in GitHub, use Drive as a data source, and finish in Notion. Hermes reads an issue labeled for reporting, finds the corresponding spreadsheet in Drive, produces a summary, and updates the project page. Each stage uses a different MCP tool group, while Hermes plans the sequence and passes results between stages. Do not enable parallel execution merely because a server supports it. Hermes documentation allows servers to declare parallel tool support but warns that operations reading and writing shared state can conflict. Independent read operations may run together, while Notion updates, issue creation, and file changes should remain sequential. Important: An MCP server is software that can run commands and receive credentials. Install only trusted servers, keep tokens out of prompts, filter dangerous tools, and require approval for deleting, sharing, or publishing data. How should you start the first workflow? Do not connect Notion, GitHub, and Google Drive on the same day and immediately assign a critical process. Choose one input, one output, and one completion criterion that is easy to verify. A first workflow could read closed GitHub issues and create a draft report in Notion without deletion or publishing permissions. After several stable runs, you can turn the procedure into a reusable Hermes skill and add a schedule. The real value of MCP is not the number of connected servers. It is the ability to complete a recurring workflow with a small permission surface, verifiable results, and a clear data path.

Automating Excel and Google Sheets reporting is no longer exclusive to software engineers. With the rapid evolution of AI models like GPT, office workers can now create custom workflow automation tools using simple instructions with Codex, freeing up hours of repetitive daily tasks. Why Excel Formulas and VBA Are No Longer Enough For weekly recurring reports or automated integrations with email, Slack, and messaging apps, traditional methods like nested Excel formulas or recording VBA (Visual Basic for Applications) macros require specific technical skills and break easily whenever a single column in the source file changes. This is the exact gap that OpenAI Codex fills: you describe precisely what needs to be done in natural language, and Codex generates complete Python or Google Apps Script code ready to run in seconds. Codex Is Not a Single Product A common misconception is how Codex is accessed: it can be used via CLI in the terminal, IDE extensions in VS Code, Codex Web on the cloud at https://chatgpt.com/codex/cloud for developers and coding enthusiasts, as well as desktop applications for both macOS and Windows. For office workers unfamiliar with command-line tools, the easiest way to start is downloading the Codex desktop app. It lets you manage multiple agents simultaneously right from a visual interface without opening a terminal or configuring API keys—just log in with your existing ChatGPT account. What an Example Prompt Looks Like You only need to provide Codex with a detailed prompt like: "Write a Python script that reads the Excel file 'sales_raw.xlsx', filters orders with status 'Completed', calculates total revenue by branch, and exports the result to 'revenue_report.xlsx' with dark blue header styling." Codex will instantly generate standard, high-quality code, and you simply run the script to get your finalized report. Automating Local Excel Reports with Python and Codex For Excel files stored locally, combining Codex with two popular Python libraries—pandas and openpyxl—delivers outstanding processing speed. Pandas handles hundreds of thousands of data rows in seconds, while openpyxl manages cell formatting, header colors, and formula insertion as illustrated in the example above. Automating Google Sheets in the Cloud When your team collaborates on Google Sheets instead of offline Excel files, the Python library gspread or Google Apps Script is the ideal choice. Codex can write code that connects directly to the Google Sheets API via a Service Account (JSON credential file) to read and write data continuously without opening a browser. A Sample Workflow Automatically pull new form submissions from Google Forms into the spreadsheet Automatically categorize customer feedback by priority Automatically send daily summary emails to leadership at 17:00 This entire pipeline runs in the background with zero manual intervention once configured. 4-Step Implementation Process for Non-Coders Step 1 - Standardize input data: Ensure the Excel or Google Sheets file has clean, clear headers without arbitrary merged cells. Step 2 - Write clear prompts for Codex: Explicitly mention file names, column names, filtering/calculation steps, and desired output formats. Step 3 - Test and paste errors for AI auto-debugging: If the script encounters an error, copy the full error traceback and paste it back into Codex for automatic correction. Step 4 - Schedule automatic runs: Use Windows Task Scheduler (Windows) or Cron jobs (macOS/Linux) to run the script automatically on a set schedule. Risks and Considerations Before Handing Reports Over to AI Codex is powerful but not completely free; however, simple operations with Excel or Google Sheets consume very little quota, so light users can comfortably rely on the free tier. If heavier workloads are required, consider Plus or Pro plans—avoid the Go tier as Codex capabilities there offer little advantage over the free plan. Never paste system passwords, financial records, or real customer data into public AI chat interfaces. When asking Codex to generate code, always substitute sensitive details with dummy data of the same structure. For critical validations such as monetary amounts or tax formulas, do not trust AI-generated code blindly on the first run. Always cross-check the output during the first 1-2 executions to ensure the logic perfectly matches your actual business requirements. Automating reports with Codex does not make you a programmer, nor should it. The true value lies in understanding your own data and business processes well enough to describe them clearly to AI—Codex handles the coding. If your company has weekly recurring reports, start today by picking the simplest one, writing a prompt following the sample above, and running your first automated test.

Just two weeks after Kimi K3 launched, OpenAI cut GPT-5.6 Luna API pricing by as much as 80%. This is not proof that OpenAI reacted directly to Kimi K3, but it is a clear sign that the 2026 AI race is shifting from "who is smarter" to "who delivers comparable performance for less". OpenAI cuts API prices sharply, with Luna down 80% Beginning July 30, OpenAI adjusted API pricing across the GPT-5.6 lineup. GPT-5.6 Luna, the fastest and lowest-priced tier of the three, fell by 80% to $0.20 per million input tokens and $1.20 per million output tokens. Terra, the balanced tier for everyday work, fell by 20% to $2/$12 per million input/output tokens. GPT-5.6 Sol, the family flagship, keeps the same price but adds Fast mode in place of Priority Processing. OpenAI says Fast mode is up to 2.5 times faster than Standard at twice the price, with no change in model intelligence. The new pricing is also reflected in credit usage for ChatGPT Work and Codex. Terra and Luna users on those plans consume fewer credits for the same workload, although subscription prices do not change. See the details in OpenAI's official pricing announcement. Is OpenAI under pressure from Kimi K3? OpenAI does not mention Kimi K3 in its announcement, so the price cuts cannot be attributed to that model alone. Still, the timing of the two events makes the market-pressure argument worth examining. On July 16, Moonshot AI launched Kimi K3, an open-weight model with a one-million-token context window. Within days, Kimi K3 drew attention from developers for competitive API pricing and performance that exceeded expectations for an open-weight release. Where does Kimi K3 approach GPT-5.6 Sol? On several benchmarks, Kimi K3 comes close to the max version of GPT-5.6 Sol. The overall gap remains, but it is far smaller than many expected from an open-weight model. Kimi K3 also leads on several specific measurements, including FrontierSWE, BrowseComp, and Frontend Code Arena, while its API is priced at $3/$15 per million input/output tokens, below Sol. Sol still leads on many aggregate benchmarks and its Ultra multi-agent mode scored 91.9% on Terminal-Bench 2.1. Even so, an open-weight model approaching OpenAI's closed flagship at a lower price creates real competitive pressure, especially for enterprises and developers sensitive to long-term operating costs. Compare current models in the 4AIVN rankings. Linking the price cuts to Kimi K3 is an interpretation based on timing and market context, not an official confirmation from OpenAI. AI pricing across the industry is changing Kimi K3 is not the only source of pressure. DeepSeek continues to pursue a low-price strategy: DeepSeek V4 Flash is listed at $0.14/$0.28 per million tokens, while DeepSeek V4 Pro is listed at $0.435/$0.87; both have one-million-token context windows. Even after an 80% cut, GPT-5.6 Luna at $0.20/$1.20 remains more expensive than DeepSeek V4 Flash on output tokens, although the gap has narrowed substantially. OpenAI's change therefore fits a broader trend rather than a one-off response to Kimi K3. Chinese labs are pushing prices lower while maintaining competitive performance, forcing US companies to optimize pricing strategies faster than before. How do users benefit? For ChatGPT users who do not use the API, there is little direct impact because subscription prices are unchanged. For teams building applications, chatbots, or automated agents on GPT-5.6, however, the difference is meaningful. Tools such as Hermes Agent, which lets users choose GPT-5.6 Sol, Terra, or Luna as the base model, can reduce operating costs when each tier is matched to the right task. Luna suits repetitive, high-volume work that does not need complex reasoning.Terra is the balanced choice for everyday work and general-purpose assistants.Sol remains the better choice when accuracy and deeper reasoning are the priority. 4AIVN's view The capability gap among leading models is narrowing faster than the price gap. When an open-weight model such as Kimi K3 can approach a top closed model, major companies must choose between protecting margins and retaining enterprise customers. OpenAI's move suggests it is prioritizing stronger performance per dollar, at least for Luna and Terra. If you operate an application or agent on the GPT-5.6 API, this is a good time to redistribute work across Sol, Terra, and Luna rather than using one model for every task. The price difference between the three tiers is now large enough that matching the model to the task is a genuine cost-optimization decision, not merely a technical preference.