Strava MCP Server
The Strava MCP Server connects Claude to the Strava API, enabling direct access to your training data for analysis and insights.
Athlete Profile & Stats (
get_athlete,get_athlete_stats): Retrieve your Strava profile and lifetime/recent statistics (total runs, rides, swims, distances)List Recent Activities (
get_activities): Browse recent activities with pagination support (up to 100 per page)Activities by Date Range (
get_activities_between): Retrieve all activities within a specific date range using ISO 8601 datesActivity Details (
get_activity): Fetch detailed information for a specific activity by IDLap/Split Data: Retrieve lap and split breakdowns for a specific activity
HR & Power Zone Distribution: Get heart rate and power zone data for an activity
Activity Streams (
get_activity_streams): Access time-series data including GPS coordinates, heart rate, power, cadence, altitude, velocity, temperature, and moreStarred Segments (
get_starred_segments): View your starred Strava segmentsSegment Details (
get_segment): Fetch detailed information about a specific segmentSegment Efforts: Access your efforts on a particular segment with optional date filtering
Provides access to Strava training data, including athlete profiles, lifetime and recent statistics, activity lists, detailed activity information, time-series data (GPS, heart rate, power, cadence, altitude), and segment details.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Strava MCP ServerSummarize my training activities from this past week."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Strava MCP Server
An MCP (Model Context Protocol) server that connects Claude to the Strava API, giving Claude direct access to your training data.
Features
Athlete Profile — Get your Strava profile info
Athlete Stats — Lifetime and recent totals (runs, rides, swims)
Activities — List recent activities or filter by date range
Activity Details — Deep dive into any single activity
Activity Streams — Time-series data: GPS, heartrate, power, cadence, altitude
Segments — Starred segments and segment details
Related MCP server: Strava MCP Server
Setup
1. Create a Strava API Application
Go to strava.com/settings/api
Create an application — set the Authorization Callback Domain to
localhostNote your Client ID and Client Secret
2. Install & Authorize
npm install
npm run setupThe setup wizard will:
Ask for your Client ID and Client Secret
Open your browser to authorize with Strava
Automatically catch the callback and exchange tokens
Write your
.envfile
3. Build
npm run buildOpen this URL in your browser (replace CLIENT_ID):
https://www.strava.com/oauth/authorize?client_id=CLIENT_ID&response_type=code&redirect_uri=http://localhost&scope=read_all,activity:read_allAfter authorizing, you'll be redirected to http://localhost?code=AUTHORIZATION_CODE. Copy the code and exchange it:
curl -s -X POST 'https://www.strava.com/oauth/token' \
-F 'client_id=CLIENT_ID' \
-F 'client_secret=CLIENT_SECRET' \
-F 'code=AUTHORIZATION_CODE' \
-F 'grant_type=authorization_code'Save the refresh_token from the response and create a .env file:
STRAVA_CLIENT_ID=your_client_id
STRAVA_CLIENT_SECRET=your_client_secret
STRAVA_REFRESH_TOKEN=your_refresh_token4. Configure Claude Desktop
Option A: Docker (recommended)
Build the Docker image:
docker build -t strava-mcp-server .Edit ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"strava": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"--env-file", "/absolute/path/to/your/.env",
"strava-mcp-server"
]
}
}
}Replace /absolute/path/to/your/.env with the full path to your .env file.
Option B: Node.js (local)
{
"mcpServers": {
"strava": {
"command": "node",
"args": ["/Users/USERNAME/Apps/StravaMCP/dist/index.js"],
"env": {
"STRAVA_CLIENT_ID": "your_client_id",
"STRAVA_CLIENT_SECRET": "your_client_secret",
"STRAVA_REFRESH_TOKEN": "your_refresh_token"
}
}
}
}Restart Claude Desktop. You should see the Strava tools available in the tools menu (hammer icon).
Available Tools
Tool | Description |
| Get your Strava profile |
| Get lifetime and recent statistics |
| List recent activities (paginated) |
| Get all activities within a date range (auto-paginated) |
| Get detailed info for one activity |
| Get lap/split data for an activity |
| Get HR and power zone distribution |
| Get time-series data (GPS, HR, power, etc.) |
| Get your starred segments |
| Get details for a specific segment |
| Get your efforts on a segment (with optional date filter) |
Example Prompts
Once connected, try asking Claude:
"What were my activities this week?"
"Analyze my running performance over the past month"
"Compare my cycling times in January vs February"
"Show me my heartrate data from my last run"
"What are my all-time stats?"
Development
# Run in dev mode (no build step)
npm run dev
# Build for production
npm run build
npm startLicense
MIT
Available Tools
8 toolsget_activitiesC
Get a list of recent Strava activities
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of activities to return (1-100) | |
| page | No | Page number for pagination |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it retrieves 'recent' activities but doesn't define what 'recent' means (e.g., time frame, ordering). It also omits details like authentication needs, rate limits, pagination behavior beyond the schema, or what the return format looks like.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'recent' entails, the structure of returned activities, or how pagination works in practice. For a tool with two parameters and no structured output documentation, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters (limit and page) with defaults, constraints, and purposes. The description adds no additional parameter semantics beyond what's in the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('list of recent Strava activities'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_activities_between' or 'get_activity', which might retrieve activities with different scopes or individual activities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_activities_between' (which likely filters by date range) or 'get_activity' (which retrieves a single activity). There's no mention of prerequisites, context, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_activities_betweenC
Get Strava activities within a specific date range
| Name | Required | Description | Default |
|---|---|---|---|
| after | Yes | Start date (ISO 8601, e.g. 2025-01-01) | |
| before | Yes | End date (ISO 8601, e.g. 2025-12-31) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action but lacks behavioral details like authentication requirements, rate limits, pagination, return format, or error handling. For a read operation with no annotations, this is insufficient disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It is front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It does not cover behavioral aspects like response format, pagination, or error cases, which are critical for a tool with two required parameters and no structured output guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear parameter descriptions in the schema. The description adds minimal value by implying date-range filtering but does not provide additional semantics beyond what the schema already documents, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('Strava activities') with scope ('within a specific date range'), making the purpose unambiguous. However, it does not explicitly differentiate from siblings like 'get_activities' (which likely lacks date filtering) or 'get_activity' (singular), so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or compare to siblings such as 'get_activities' (possibly unfiltered) or 'get_activity' (for a single activity), leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_activityC
Get detailed information for a specific Strava activity by ID
| Name | Required | Description | Default |
|---|---|---|---|
| activity_id | Yes | The Strava activity ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a 'get' operation but doesn't mention whether it requires authentication, has rate limits, returns paginated results, or what format the detailed information takes. This leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple retrieval tool and gets straight to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'detailed information' includes, whether authentication is required, or how errors are handled. Given the lack of structured metadata, the description should provide more context about the tool's behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'activity_id' clearly documented in the schema. The description adds no additional parameter information beyond what's already in the structured schema, so it meets the baseline expectation but doesn't provide extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get detailed information') and target resource ('specific Strava activity by ID'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its sibling 'get_activities' (which likely returns multiple activities), leaving some ambiguity about when to use one versus the other.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_activities' or 'get_activity_streams'. It doesn't mention prerequisites, context for use, or any exclusion criteria. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_activity_streamsC
Get detailed time-series data (streams) for an activity: GPS coordinates, heartrate, power, cadence, altitude, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| activity_id | Yes | The Strava activity ID | |
| stream_types | No | Types of streams to retrieve |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieving data but does not cover aspects like rate limits, authentication requirements, error conditions, or the format of the returned streams. This leaves significant gaps for an agent to understand how to use the tool effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and provides relevant examples without unnecessary details. Every word earns its place, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain what the returned data looks like (e.g., format, structure), potential limitations, or error handling. For a tool that retrieves detailed time-series data, more context is needed to guide an agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the parameters. The description adds minimal value by implying that 'stream_types' includes examples like GPS coordinates and heartrate, but does not provide additional semantics beyond what the schema already specifies (e.g., default values or enum details). Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving detailed time-series data (streams) for an activity, with specific examples like GPS coordinates, heartrate, power, cadence, and altitude. It uses a specific verb ('Get') and resource ('activity'), but does not explicitly differentiate from sibling tools like 'get_activity' or 'get_activities', which might retrieve different types of activity data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'get_activity' (which might retrieve summary data) or specify contexts where detailed streams are needed over other data types. Usage is implied by the description but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_athleteB
Get the profile of the authenticated Strava athlete
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'authenticated' which hints at permission needs, but lacks details on rate limits, error handling, or response format. This is inadequate for a tool that likely involves API calls and user data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero wasted words, front-loading the essential action and resource. It's appropriately sized for a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema), the description is minimally complete but lacks depth. Without annotations or output schema, it should ideally explain more about the returned profile data or authentication context to fully guide the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a high baseline score as it avoids redundancy.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('profile of the authenticated Strava athlete'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_athlete_stats', which might provide overlapping athlete information, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance by implying usage for retrieving the authenticated athlete's profile, but it offers no explicit when-to-use advice, alternatives (e.g., vs. 'get_athlete_stats'), or exclusions. This leaves the agent with little context for tool selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_athlete_statsB
Get lifetime and recent statistics for the authenticated athlete (total runs, rides, swims, distances, etc.)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves statistics, implying a read-only operation, but doesn't clarify authentication requirements, rate limits, data freshness, or response format. For a tool with zero annotation coverage, this is a significant gap in transparency, though it doesn't contradict any annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get lifetime and recent statistics') and includes helpful examples ('total runs, rides, swims, distances, etc.'). There is zero waste, and every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple read operation with no parameters), the lack of annotations and output schema means the description should do more to be complete. It adequately states what the tool does but fails to cover behavioral aspects like authentication, response format, or limitations. This makes it minimally viable but with clear gaps in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so the schema already fully documents the lack of inputs. The description adds no parameter information, which is appropriate here. A baseline score of 4 is given since no parameters exist, and the description doesn't need to compensate for any gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get') and resources ('lifetime and recent statistics for the authenticated athlete'), including examples of what statistics are retrieved ('total runs, rides, swims, distances, etc.'). It distinguishes itself from siblings like 'get_activities' or 'get_athlete' by focusing on aggregated statistics rather than individual activities or athlete profile data. However, it doesn't explicitly name alternatives, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'for the authenticated athlete,' suggesting this tool is for retrieving personal statistics rather than general data. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_activities' (for detailed activity lists) or 'get_athlete' (for profile info), and provides no exclusions or prerequisites. This leaves usage somewhat open to interpretation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_segmentB
Get detailed information about a specific Strava segment
| Name | Required | Description | Default |
|---|---|---|---|
| segment_id | Yes | The Strava segment ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves 'detailed information' but doesn't specify what that includes (e.g., metadata, statistics), whether it's a read-only operation, or any rate limits or authentication needs. This leaves significant gaps for a tool that likely interacts with an external API.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence that efficiently conveys the core purpose without any fluff or redundancy. It is front-loaded and appropriately sized for the tool's simplicity, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no nested objects) and high schema coverage, the description is minimally complete. However, with no annotations or output schema, it lacks details on behavioral traits and return values, which could hinder an agent's understanding of what 'detailed information' entails or how to handle the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'segment_id' clearly documented as 'The Strava segment ID'. The description adds no additional semantic context beyond this, such as format examples or constraints, so it meets the baseline for adequate but unenhanced parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('detailed information about a specific Strava segment'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'get_starred_segments' or 'get_activity_streams', which might also retrieve segment-related data, so it misses the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing a valid segment ID, or compare it to siblings like 'get_starred_segments' for broader segment queries, leaving the agent with minimal context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_starred_segmentsB
Get the authenticated athlete's starred segments
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states it retrieves starred segments but lacks behavioral details: it doesn't specify if this is a read-only operation, how results are returned (e.g., list format, pagination), authentication requirements beyond 'authenticated athlete', or any rate limits. The description is minimal and misses key operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded with the core action and resource, making it easy to parse quickly, which is ideal for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple tool with 0 parameters, the description is incomplete. It lacks details on return values (e.g., format, structure), authentication specifics, or behavioral traits like error handling. For a tool that likely returns data, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds no parameter details, which is appropriate here. A baseline of 4 is applied as it doesn't need to compensate for any gaps, but it doesn't exceed expectations by providing extra context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('starred segments'), with specificity about whose segments ('authenticated athlete's'). However, it doesn't explicitly differentiate from sibling tools like 'get_segment' or 'get_activities', which might also involve segments, leaving room for ambiguity in sibling context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication status), compare to siblings like 'get_segment' for non-starred segments, or indicate scenarios where this is preferred, leaving the agent to infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose targeting specific Strava resources: activities (list, range, detail, streams), athlete (profile, stats), and segments (detail, starred). No overlap exists in functionality, making tool selection unambiguous for an agent.
All tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive suffixes (e.g., get_activities, get_activity_streams). This predictable naming convention enhances readability and agent usability throughout the set.
With 8 tools, this server is well-scoped for the Strava domain, covering core resources like activities, athlete data, and segments. Each tool earns its place by addressing distinct aspects of the API without being overwhelming or insufficient.
The tool set provides strong read-only coverage for activities, athlete, and segments, but lacks write operations (e.g., create/update activities, star segments) or broader functionality like clubs or routes. This is a notable gap that may limit agent workflows requiring full CRUD capabilities.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Connect Claude to your Intervals.icu watch data for fitness, workout review, and plan writing.
Garmin data in Claude & ChatGPT via the Garmin Health API. OAuth sign-in, no password sharing.
Pace is a remote MCP server that exposes wearable and fitness data to Claude via the Model Context Protocol. It connects to Garmin, Oura, Whoop, Polar, Fitbit and 20+ devices and provides 15 tools for querying sleep, activity, recovery, and training data. Hosted on Google Cloud Run, OAuth 2.1 authentication, Streamable HTTP transport. Instructions: First you need to create an account at: https://pacetraining.co and connect your wearables. After that you can connect the remote Server via Custom Connector in Claude and OAuth 2.1 Flow startet.
Garmin data in Claude: 135 tools — activities, sleep, HRV, training, workouts. Free, open source.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceEnables Claude to access and analyze your Strava activities through OAuth authentication. Supports retrieving activity lists and detailed workout data for fitness tracking and analysis.2
- AlicenseAqualityDmaintenanceConnects Claude to your Strava account for analyzing training, predicting race times, and generating periodized training plans via natural language.1361ISC
- AlicenseNot gradedqualityCmaintenanceConnects Claude to Strava data for natural language queries about rides, stats, and activities.25MIT
- AlicenseAqualityDmaintenanceConnects Claude to your Strava account so you can query your activities, stats, routes, and segments using natural language.27221MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/KerkhofMe/StravaMCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server