Strava MCP Server
The Strava MCP Server acts as a bridge to the Strava API, enabling LLMs to interact with your personal Strava data through natural language prompts.
Key capabilities include:
Profile & Stats: Access athlete profile information and performance metrics
Activities: Fetch recent activities, detailed activity information, lap data, and time-series data streams (heart rate, power, GPS)
Segments: Search, star/unstar, view detailed information, and list your efforts on specific segments
Training: Retrieve configured heart rate and power zones for training insights
Clubs: View clubs you belong to
Routes: Access saved routes with options to export in GPX or TCX formats
This server enables AI-friendly interaction with your Strava data for analysis and visualization.
Uses .ENV files to store and manage authentication credentials and configuration options for the Strava API integration.
Runs as a Node.js application, with the server being launched through the Node runtime in Claude's configuration.
Provides tools for accessing Strava data including recent activities, profile details, athlete stats, activity streams (power, heart rate, cadence), segments management, detailed activity information, route management, and exporting routes in GPX/TCX formats.
Implements the MCP server in TypeScript to provide type-safe interaction with the Strava API.
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 Servershow me my recent Strava activities"
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
Talk to your Strava data using AI.
Connect Claude to your Strava account and ask questions in plain English: "How far did I run this month?", "Analyze my last ride", or "Show me my fastest segments."
What Can You Do With This?
Once connected, just talk to Claude like you're talking to a friend who has access to all your Strava data:
π Track Your Progress
"How many kilometers did I run this month?"
"Compare my running stats from January to December"
"What's my longest ride ever?"
π Analyze Your Workouts
"Break down my last cycling workout - show me power, heart rate, and cadence"
"How did my heart rate zones look during yesterday's run?"
"What was my average pace for each lap in my interval training?"
πΊοΈ Explore Routes & Segments
"What are the most popular cycling segments near Central Park?"
"Show me my starred segments"
"Export my Sunday morning route as a GPX file"
π Get Coaching Insights
"Analyze my training load this week"
"How does my current fitness compare to last month?"
"Give me a summary of my cycling performance this year"
Related MCP server: Strava MCP Server
Quick Start (3 Steps)
Step 1: Add to Claude Desktop
Open your Claude Desktop configuration file:
Mac:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
Add this to the file:
{
"mcpServers": {
"strava": {
"command": "npx",
"args": ["-y", "@r-huijts/strava-mcp-server"]
}
}
}Step 1 (alternative): Add to Claude Code
You can add this MCP server to claude code with the following command:
claude mcp add --transport stdio strava -- npx @r-huijts/strava-mcp-serverYou can confirm successful instalation as follows:
% claude mcp list
Checking MCP server health...
strava: npx @r-huijts/strava-mcp-server - β ConnectedStep 2: Restart Claude Desktop
Close and reopen Claude Desktop to load the new configuration.
Step 3: Connect Your Strava
Just say to Claude:
"Connect my Strava account"
A browser window will open. Enter your Strava API credentials, authorize the app, and you're done!
That's it! Start asking about your activities.
Connecting Your Strava Account
First Time Setup
When you say "Connect my Strava account", here's what happens:
A browser window opens showing a setup page
Enter your Strava API credentials (Client ID and Client Secret)
Click "Continue to Strava" - you'll be redirected to Strava
Authorize the app on Strava's website
See the success message - you can close the browser
Done! Claude confirms you're connected
Your credentials are saved locally at ~/.config/strava-mcp/config.json and persist across sessions.
Getting Your Strava API Credentials
You need to create a free Strava API application (one-time setup):
Go to strava.com/settings/api
Click "Create an App" (or view your existing app)
Fill in the form:
Application Name: Anything you want (e.g., "My Claude Assistant")
Category: Choose any
Website: Can be anything (e.g.,
http://localhost)Authorization Callback Domain: Must be
localhost
Copy your Client ID and Client Secret
That's it! You only need to do this once.
Managing Your Connection
Check status: "Am I connected to Strava?"
Reconnect: "Connect my Strava account" (use
force: trueto reconnect)Disconnect: "Disconnect my Strava account"
Installation Options
Option A: Just Use It (Recommended)
No installation needed! The npx command in the Quick Start automatically downloads and runs the latest version.
Option B: Install Globally
If you prefer to install it once:
npm install -g @r-huijts/strava-mcp-serverThen update your Claude config to use:
{
"mcpServers": {
"strava": {
"command": "strava-mcp-server"
}
}
}Note: Even though the package name is @r-huijts/strava-mcp-server, the executable name remains strava-mcp-server for backward compatibility.
Option C: Build from Source
For developers who want to modify the code:
git clone https://github.com/r-huijts/strava-mcp.git
cd strava-mcp
npm install
npm run buildThen point Claude to your local build:
{
"mcpServers": {
"strava": {
"command": "node",
"args": ["/path/to/strava-mcp/dist/server.js"]
}
}
}Example Conversations
Morning Check-in
You: What did I do for exercise this week?
Claude: Looking at your Strava activities this week, you completed:
Monday: 5.2km run (28:15)
Wednesday: 45km bike ride (1:32:00)
Saturday: 10km run (52:30)
Total: 60.2km across 3 activities. Nice consistency!
Workout Analysis
You: Analyze my last cycling workout in detail
Claude: Here's a breakdown of your ride "Evening Loop" from yesterday:
Overview
Distance: 42.3 km
Time: 1:28:45
Avg Speed: 28.6 km/h
Power Analysis
Average Power: 185W
Normalized Power: 198W
Max Power: 520W
Heart Rate
Average: 142 bpm
Max: 175 bpm
Time in Zone 4+: 23 minutes
Your power-to-heart-rate ratio suggests good aerobic efficiency...
Finding New Routes
You: Find me some challenging cycling climbs near Boulder, Colorado
Claude: Here are popular climbing segments near Boulder:
Flagstaff Road - 5.2km, 6.8% avg grade
Sunshine Canyon - 3.8km, 7.2% avg grade
Left Hand Canyon - 8.1km, 4.5% avg grade
Want me to star any of these so you can find them easily later?
Available Tools
Account & Profile
What you can ask | What it does |
"Connect my Strava account" | Links your Strava to Claude |
"Check my Strava connection" | Shows connection status |
"Disconnect my Strava account" | Unlinks Strava and clears stored tokens |
"Get my Strava profile" | Shows your profile info |
"What shoes do I have?" | Lists your shoes and usage distance |
"What are my training zones?" | Shows HR and power zones |
Activities
What you can ask | What it does |
"Show my recent activities" | Lists your latest workouts |
"Get all my runs from January" | Fetches activities with filters |
"Analyze activity 12345" | Detailed info about one activity |
"Show the laps from my last run" | Lap-by-lap breakdown |
"Get heart rate data from my ride" | Time-series workout data (optimized compact format) |
"Show photos from my hike" | Activity photos |
Stats & Progress
What you can ask | What it does |
"What are my running stats?" | YTD and all-time totals |
"How far have I cycled this year?" | Activity totals by type |
"What's my longest ride?" | Personal records |
Segments
What you can ask | What it does |
"Show my starred segments" | Your favorite segments |
"Find segments near [location]" | Discover popular segments |
"Get details on segment 12345" | Segment info and leaderboard |
"Star this segment" | Save to favorites |
"Show my efforts on [segment]" | Your attempts on a segment |
Routes
What you can ask | What it does |
"List my saved routes" | Your created routes |
"Get details for my [route name]" | Route info |
"Export [route] as GPX" | Download for GPS devices |
"Export [route] as TCX" | Download for GPS devices |
Clubs
What you can ask | What it does |
"What clubs am I in?" | Lists your Strava clubs |
Workouts
What you can ask | What it does |
"Turn this workout into a Zwift file" | Formats a workout plan into a structured file (currently Zwift |
Server
What you can ask | What it does |
"What version is the Strava server?" | Shows the MCP server version and metadata |
Troubleshooting
"Connect my Strava account" doesn't open a browser
Make sure Claude Desktop is running the MCP server (check for errors in Claude)
Try restarting Claude Desktop
Check that port 8111 isn't blocked by a firewall
"Authentication failed" or "Invalid token"
Your token may have expired - say "Connect my Strava account" to reconnect
Make sure your Strava API application is still active at strava.com/settings/api
"Missing credentials" error
You need to complete the OAuth flow - say "Connect my Strava account"
If you're using environment variables, make sure all 4 are set (see Developer section)
Claude doesn't see the Strava tools
Make sure your
claude_desktop_config.jsonis valid JSON (no trailing commas!)Restart Claude Desktop after making config changes
Check Claude's developer console for error messages
JSONRPC.ProtocolTransportError after package name change
If you're getting a JSONRPC error after updating to @r-huijts/strava-mcp-server:
Clear npx cache: Run
rm -rf ~/.npm/_npxin terminalVerify config uses
@r-huijts/strava-mcp-server(not the oldstrava-mcp-server)Restart Claude Desktop completely (quit and reopen)
Test manually: Run
npx -y @r-huijts/strava-mcp-server- you should see "Starting Strava MCP Server v1.2.1..."
For Developers
Environment Variables
Instead of using the browser-based auth, you can set environment variables:
Variable | Description |
| Your Strava Application Client ID |
| Your Strava Application Client Secret |
| OAuth access token |
| OAuth refresh token |
| Directory for GPX/TCX exports |
Token Refresh
The server automatically refreshes expired tokens. New tokens are saved to both process.env and ~/.config/strava-mcp/config.json.
Config Priority
Environment variables (highest)
~/.config/strava-mcp/config.jsonLocal
.envfile
Building & Testing
npm install
npm run build
npm testActivity Streams Optimization
The get-activity-streams tool uses a compact format by default, reducing payload size by ~70-80% while preserving all data:
Compact format (default): Raw arrays with metadata, ~70-80% smaller, ideal for LLM processing
Verbose format: Human-readable objects with formatted values (backward compatible)
Smart chunking: Large activities automatically split into ~50KB chunks
Optional downsampling: Can reduce very large datasets while preserving key features
The compact format includes comprehensive metadata (units, descriptions, statistics) so LLMs can understand the raw numeric data.
API Reference
The server implements the Model Context Protocol (MCP) and exposes 25 tools for Strava API v3. See the source code in src/tools/ for implementation details.
Contributing
Contributions welcome! Please submit a Pull Request.
License
MIT License - see LICENSE file for details.
Questions? Open an issue on GitHub.
Available Tools
24 toolscheck-strava-connectionA
Check if Strava is connected and show the current connection status. Use this when the user asks about their connection status.
| 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. The term 'check' and 'show' imply a non-mutating, read-only operation, which is sufficient for a simple status check. It does not explicitly state that no changes are made, but the nature of the action and the lack of parameters make this transparent enough.
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 two sentences long and front-loaded with the primary purpose. Every word earns its place, with no repetition or fluff, 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?
For a zero-parameter, no-output-schema tool, the description is complete. It clearly states what the tool does and when to use it, and the sibling context shows how it fits into the connection management workflow. There is no missing information that would hinder an agent from invoking it correctly.
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 zero parameters, so the baseline is 4. The empty input schema requires no additional explanation, and the description appropriately does not attempt to document parameters that do not exist.
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 checks and shows Strava connection status, using the specific verb 'check' and resource 'connection status'. This distinguishes it from sibling tools like connect-strava and disconnect-strava, which manage the connection, and data-fetching tools that retrieve stats.
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 explicitly says 'Use this when the user asks about their connection status,' providing clear guidance on when to invoke the tool. It does not explicitly mention when not to use it or compare to alternatives, but the context is strong enough for an agent to differentiate it from connect/disconnect tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
connect-stravaA
Connect your Strava account to enable activity tracking. This will open a browser window for secure authentication. Use this when the user asks to connect, link, or authenticate their Strava account.
| Name | Required | Description | Default |
|---|---|---|---|
| force | No | Force re-authentication even if already connected |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full transparency burden. It discloses that authentication opens a browser window, which is helpful, but it does not explain what happens if the account is already connected, whether the 'force' option is necessary, or any side effects beyond the authentication flow. For a mutation-like action, this is a significant gap.
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 two sentences, front-loaded with the action and followed by the usage condition. Every word serves a purpose, with no filler or redundancy.
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 simple OAuth connect tool with one optional parameter and no output schema, the description covers the core action and when to use it. However, it omits details about what happens after connection, such as success/failure indication or behavior when already authenticated, which is useful context for an 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 schema already fully documents the single 'force' parameter with a clear description ('Force re-authentication even if already connected'). The description adds no further parameter semantics, so the baseline score of 3 applies.
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: 'Connect your Strava account to enable activity tracking.' This specifies the verb and resource, and it naturally distinguishes from sibling tools like 'disconnect-strava' and 'check-strava-connection'.
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?
Explicit usage guidance is provided: 'Use this when the user asks to connect, link, or authenticate their Strava account.' This gives clear context for when to invoke the tool, though it does not discuss exclusions or alternatives, which is acceptable given the tool's distinct purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
disconnect-stravaA
Disconnect your Strava account and remove stored credentials. Use this when the user wants to logout, disconnect, or remove their Strava connection.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It explicitly discloses the key side effect: 'remove stored credentials.' This goes beyond the name and informs the agent of the security-relevant action. It doesn't detail irreversibility or effects on other services, but for a zero-parameter tool this is adequate and notably transparent.
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?
Two sentences, each earning its place: the first states the action, the second states when to use it. Front-loaded and free of filler. Excellent structure.
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 simple action tool with no parameters and no output schema, the description is complete: it covers what, why, when, and the side effect. Sibling tools help disambiguate, and the description is self-sufficient. No gaps remain.
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 zero parameters, so the baseline is 4. The description adds no parameter-specific information because none is needed. It appropriately focuses on the action and usage context, which is all that matters here.
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 uses a specific verb ('Disconnect') and identifies the resource ('your Strava account') plus additional detail ('remove stored credentials'). It clearly distinguishes this tool from siblings like connect-strava and check-strava-connection, which serve different purposes.
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?
Provides explicit guidance: 'Use this when the user wants to logout, disconnect, or remove their Strava connection.' This clearly states the intended use case. However, it lacks explicit 'when not to use' or alternative tool references, so it falls slightly short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explore-segmentsC
Searches for popular segments within a given geographical area.
| Name | Required | Description | Default |
|---|---|---|---|
| bounds | Yes | The geographical area to search, specified as a comma-separated string: south_west_lat,south_west_lng,north_east_lat,north_east_lng | |
| activityType | No | Filter segments by activity type (optional: 'running' or 'riding'). | |
| minCat | No | Filter by minimum climb category (optional, 0-5). Requires riding activityType. | |
| maxCat | No | Filter by maximum climb category (optional, 0-5). Requires riding activityType. |
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 searching for 'popular segments' but doesn't specify what makes a segment 'popular' (e.g., based on usage, ratings, or other metrics), nor does it describe the return format, pagination, or any rate limits or authentication requirements. This leaves significant gaps in understanding the tool's behavior.
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 that efficiently conveys the core functionality without unnecessary words. It is front-loaded with the main action and resource, making it easy to understand at a glance.
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 complexity of a search tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., how popularity is determined, result format), usage context, and differentiation from siblings. This makes it inadequate for an AI agent to fully understand when and how to invoke the tool 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 schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond implying a geographical search, which is already covered by the 'bounds' parameter description. Thus, it meets the baseline score of 3 without adding 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 tool's purpose with a specific verb ('searches') and resource ('popular segments'), and specifies the geographical scope. However, it doesn't differentiate from sibling tools like 'list-starred-segments' or 'get-segment', which might also retrieve segments but with different criteria or scope.
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 'list-starred-segments' or 'get-segment', nor does it mention prerequisites such as requiring a connected Strava account. It only states what the tool does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
export-route-gpxA
Exports a specific Strava route in GPX format and saves it to a pre-configured local directory.
| Name | Required | Description | Default |
|---|---|---|---|
| routeId | Yes | The ID of the Strava route to export. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It does disclose a key side effect: 'saves it to a pre-configured local directory,' which is important for an agent to know. However, it does not mention authentication requirements, error handling, file naming, or whether the operation overwrites existing files. This is adequate but not rich behavioral detail.
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 sentence that is concise, front-loaded with the action, and contains no redundant information. Every phrase earns its place: specifies the export format, the resource, and the destination.
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 one required parameter and no output schema, the description covers the essential context: what it exports, in what format, and where it saves. It lacks information about return values (e.g., success message) and potential failure modes, but given the simple nature of the tool and the rich schema, the description is sufficiently complete. A score of 4 reflects that it could add a note about output or prerequisites but is otherwise adequate.
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 covers 100% of parameter semantics: routeId is described as 'The ID of the Strava route to export.' The description adds no additional parameter-level detail beyond the schema, but it reinforces the meaning by referring to 'a specific Strava route.' Per calibration, with schema coverage at 100%, a 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 function: 'Exports a specific Strava route in GPX format and saves it to a pre-configured local directory.' It specifies the verb (exports), resource (specific Strava route), output format (GPX), and side effect (saving to a local directory). This distinguishes it from sibling tools like export-route-tcx (different format) and get-route (retrieval without file output).
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 when to use this tool (when a GPX export of a route is needed) but does not explicitly provide usage exclusions or name alternatives. Since sibling tools exist (e.g., export-route-tcx for TCX format), the description could have stated 'use this for GPX, export-route-tcx for TCX' to improve guidance. The context is clear but lacks explicit alternative comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
export-route-tcxA
Exports a specific Strava route in TCX format and saves it to a pre-configured local directory.
| Name | Required | Description | Default |
|---|---|---|---|
| routeId | Yes | The ID of the Strava route to export. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses a key behavioral traitβsaving to a pre-configured local directoryβwhich implies a mutating side effect. However, it omits other useful behavioral context such as overwrite behavior, authentication requirements, or error conditions.
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, tightly worded sentence that conveys the core purpose, format, and destination with no redundancy or unnecessary detail.
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 simple one-parameter tool, the description covers the essential elements: what is exported, the format, and the destination. It lacks minor details like return value or prerequisites, but given the absence of annotations and output schema, it is reasonably complete.
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 already provides 100% coverage for the single parameter routeId with a clear description. The tool description adds no additional meaning beyond what the schema states, 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 action ('Exports... in TCX format'), the specific resource ('a specific Strava route'), and a unique side effect ('saves it to a pre-configured local directory'). It distinguishes the tool from the sibling export-route-gpx by format (TCX vs GPX).
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 for exporting a route in TCX format, but it does not explicitly mention when to use this over export-route-gpx or other route tools. No exclusions or alternative comparisons are provided, leaving usage guidance only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-activity-detailsC
Fetches detailed information about a specific activity using its ID.
| Name | Required | Description | Default |
|---|---|---|---|
| activityId | Yes | The unique identifier of the activity to fetch details for. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it 'fetches detailed information' without disclosing behavioral traits. It doesn't mention whether this is a read-only operation, what permissions are needed, rate limits, error conditions, or what format/details are returned. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 gets straight to the point with zero wasted words. It's appropriately sized for a simple lookup tool and front-loads the core functionality without unnecessary elaboration.
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 insufficiently complete. It doesn't explain what 'detailed information' includes, the response format, or any behavioral aspects. For a tool in a complex domain (Strava activities) with many sibling tools, more context is needed to understand its specific role 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 'activityId' well-documented in the schema. The description adds no additional parameter semantics beyond implying the ID is used to fetch details. Since the schema does the heavy lifting, the baseline score of 3 is appropriate even though the description doesn't enhance parameter understanding.
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 'fetches' and resource 'detailed information about a specific activity', making the purpose understandable. It distinguishes from siblings like 'get-all-activities' by specifying 'specific activity using its ID', but doesn't explicitly contrast with similar tools like 'get-activity-laps' or 'get-activity-streams' that also fetch activity-related 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 doesn't mention when this tool is appropriate compared to siblings like 'get-all-activities' for listing activities or 'get-activity-streams' for different types of activity data. There's no context about prerequisites, timing, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-activity-lapsA
Retrieves detailed lap data for a specific Strava activity.
Use Cases:
Get complete lap data including timestamps, speeds, and metrics
Access raw values for detailed analysis or visualization
Extract specific lap metrics for comparison or tracking
Parameters:
id (required): The unique identifier of the Strava activity.
Output Format: Returns both a human-readable summary and complete JSON data for each lap, including:
A text summary with formatted metrics
Raw lap data containing all fields from the Strava API:
Unique lap ID and indices
Timestamps (start_date, start_date_local)
Distance and timing metrics
Speed metrics (average and max)
Performance metrics (heart rate, cadence, power if available)
Elevation data
Resource state information
Activity and athlete references
Notes:
Requires activity:read scope for public/followers activities, activity:read_all for private activities
Returns complete data as received from Strava API without omissions
All numeric values are preserved in their original precision
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The identifier of the activity to fetch laps for. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing important behavioral traits: authentication requirements ('Requires activity:read scope...'), data completeness ('Returns complete data... without omissions'), and precision handling ('All numeric values are preserved...'). It doesn't mention rate limits or error conditions, keeping it from a perfect score.
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?
Excellent structure with clear sections (Description, Use Cases, Parameters, Output Format, Notes). Every sentence earns its place by adding specific value - no redundant information. The description is appropriately sized and front-loaded with the core purpose.
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 single-parameter read operation with no output schema, the description provides exceptional completeness. It covers authentication requirements, data scope, output format details (both human-readable and JSON), and specific data fields returned. This gives the agent sufficient context to use the tool 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 already documents the single 'id' parameter adequately. The description adds minimal value beyond the schema by specifying it's for 'a specific Strava activity' and listing it in the Parameters section, but doesn't provide additional syntax or format details.
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 verb ('Retrieves') and resource ('detailed lap data for a specific Strava activity'). It distinguishes from siblings like 'get-activity-details' by focusing exclusively on lap data rather than general activity information.
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 'Use Cases' section provides clear context for when to use this tool (detailed lap analysis, visualization, comparison). However, it doesn't explicitly state when NOT to use it or name specific alternatives among sibling tools, though the focus on lap data implies differentiation from general activity tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-activity-photosA
Retrieves photos associated with a specific Strava activity.
Use Cases:
Fetch all photos uploaded to an activity
Get photo URLs for display or download
Access photo metadata including location and timestamps
Parameters:
id (required): The unique identifier of the Strava activity.
size (optional): Size of photos to return in pixels (e.g., 100, 600, 2048). If not specified, returns all available sizes.
Output Format: Returns both a human-readable summary and complete JSON data for each photo, including:
A text summary with photo count and URLs
Raw photo data containing all fields from the Strava API:
Photo ID and unique identifier
URLs for different sizes
Source (1 = Strava, 2 = Instagram)
Timestamps (uploaded_at, created_at)
Location coordinates if available
Caption if provided
Notes:
Requires activity:read scope for public/followers activities, activity:read_all for private activities
Photos may come from Strava uploads or linked Instagram posts
Returns empty array if activity has no photos
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The identifier of the activity to fetch photos for. | |
| size | No | Optional photo size in pixels (e.g., 100, 600, 2048). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses scope requirements (activity:read vs activity:read_all), the possibility of Instagram-sourced photos, and the empty-array behavior. It also explains the size parameter's default behavior, providing thorough transparency.
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 well-structured with clear sections. It could be slightly tighter in the output-format section, but every part serves a purpose and is easy to scan.
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 two-parameter tool with no annotations and no output schema, the description is exceptionally complete. It covers purpose, parameters, output structure, auth scopes, and edge cases, leaving no critical gaps.
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 already covers both parameters, but the description adds value with size examples and the behavior when size is omitted (returns all sizes). This enhances understanding beyond the schema's bare definition.
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 opens with a specific verb and resource: 'Retrieves photos associated with a specific Strava activity.' This clearly distinguishes it from sibling tools like get-activity-details or get-activity-streams, which handle other 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?
Use cases explicitly state when to use the tool: fetching photos, getting URLs, and accessing metadata. While it doesn't name alternative tools, the context makes the appropriate scenario clear. A slight improvement would be explicitly contrasting with other activity-data tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-activity-streamsA
Retrieves detailed time-series data streams from a Strava activity. Perfect for analyzing workout metrics, visualizing routes, or performing detailed activity analysis.
Key Features:
Multiple Data Types: Access various metrics like heart rate, power, speed, GPS coordinates, etc.
Flexible Resolution: Choose data density from low (~100 points) to high (~10000 points)
Smart Pagination: Get data in manageable chunks optimized for LLM context limits
Rich Statistics: Includes min/max/avg for numeric streams
Dual Format Support: Compact (LLM-optimized) or verbose (human-readable)
Intelligent Downsampling: Automatically reduce large datasets while preserving key features
Format Options:
compact (default): Raw arrays, minified JSON, ~70-80% smaller payloads, ideal for LLM processing
verbose: Human-readable objects with formatted values, backward compatible with legacy format
Common Use Cases:
Analyzing workout intensity through heart rate zones
Calculating power metrics for cycling activities
Visualizing route data using GPS coordinates
Analyzing pace and elevation changes
Detailed segment analysis
Output Format:
Metadata: Activity overview, available streams, data points, units, format info
Statistics: Summary stats for each stream type (max/min/avg where applicable)
Data: Time-series data in compact arrays or verbose objects (based on format parameter)
Notes:
Requires activity:read scope
Not all streams are available for all activities
Older activities might have limited data
Large activities are automatically chunked to ~50KB per message
Use max_points parameter to downsample very large activities intelligently
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The Strava activity identifier to fetch streams for. This can be obtained from activity URLs or the get-activities tool. | |
| types | No | Array of stream types to fetch. Available types: - time: Time in seconds from start - distance: Distance in meters from start - latlng: Array of [latitude, longitude] pairs - altitude: Elevation in meters - velocity_smooth: Smoothed speed in meters/second - heartrate: Heart rate in beats per minute - cadence: Cadence in revolutions per minute - watts: Power output in watts - temp: Temperature in Celsius - moving: Boolean indicating if moving - grade_smooth: Road grade as percentage | |
| resolution | No | Optional data resolution. Affects number of data points returned: - low: ~100 points - medium: ~1000 points - high: ~10000 points Default varies based on activity length. | |
| series_type | No | Optional base series type for the streams: - time: Data points are indexed by time (seconds from start) - distance: Data points are indexed by distance (meters from start) Useful for comparing different activities or analyzing specific segments. | distance |
| page | No | Optional page number for paginated results. Use with points_per_page to retrieve specific data ranges. Example: page=2 with points_per_page=100 gets points 101-200. | |
| points_per_page | No | Optional number of data points per page. Special values: - Positive number: Returns that many points per page - -1: Returns ALL data points split into multiple messages (~1000 points each) Use -1 when you need the complete activity data for analysis. | |
| format | No | Output format: - compact: Raw arrays, minified JSON (~70-80% smaller, LLM-friendly) - verbose: Human-readable objects with formatted values (backward compatible) | compact |
| max_points | No | Maximum number of data points to return. If activity exceeds this, data will be intelligently downsampled while preserving peaks and valleys. Useful for very large activities. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and excels by disclosing key behavioral traits: it requires 'activity:read scope', notes 'not all streams are available for all activities', warns 'older activities might have limited data', explains 'large activities are automatically chunked to ~50KB per message', and describes intelligent downsampling for large datasets. This covers permissions, data availability, limitations, and performance considerations thoroughly.
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 well-structured with sections like 'Key Features', 'Format Options', 'Common Use Cases', 'Output Format', and 'Notes', making it easy to scan. However, it is lengthy with multiple bullet points and detailed explanations, which, while informative, could be more concise. Every sentence adds value, but some redundancy exists (e.g., repeating format details).
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 complexity of 8 parameters, no annotations, and no output schema, the description is highly complete. It covers purpose, usage, behavioral traits, parameter semantics, output format details, and limitations. The 'Output Format' section compensates for the lack of output schema by describing metadata, statistics, and data structure, making it sufficient for an agent to understand what to expect.
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%, so the baseline is 3. The description adds significant value by explaining parameter implications beyond the schema: it details how 'resolution' affects data points (~100 to ~10000), describes 'smart pagination' for 'page' and 'points_per_page', explains 'intelligent downsampling' for 'max_points', and elaborates on 'format' options (compact vs verbose) with payload size impacts. This enhances understanding but doesn't fully cover all 8 parameters in depth.
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 'retrieves detailed time-series data streams from a Strava activity' with specific verbs ('retrieves', 'analyzing', 'visualizing') and resources ('Strava activity', 'workout metrics', 'routes'). It distinguishes from siblings like get-activity-details (which likely provides summary info) and get-activity-laps (which focuses on lap segments) by emphasizing time-series data streams for analysis.
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 explicitly states when to use this tool: 'Perfect for analyzing workout metrics, visualizing routes, or performing detailed activity analysis' and lists common use cases like analyzing heart rate zones, calculating power metrics, and visualizing GPS coordinates. It distinguishes from siblings by focusing on time-series data streams rather than summary details, photos, or segments.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-all-activitiesA
Fetches complete activity history with optional filtering by date range and activity type. Supports pagination to retrieve all activities.
| Name | Required | Description | Default |
|---|---|---|---|
| startDate | No | ISO date string for activities after this date (e.g., '2024-01-01') | |
| endDate | No | ISO date string for activities before this date (e.g., '2024-12-31') | |
| activityTypes | No | Array of activity types to filter (e.g., ['Run', 'Ride']) | |
| sportTypes | No | Array of sport types for granular filtering (e.g., ['MountainBikeRide', 'TrailRun']) | |
| maxActivities | No | Maximum activities to return after filtering (default: 500) | |
| maxApiCalls | No | Maximum API calls to prevent quota exhaustion (default: 10 = ~2000 activities) | |
| perPage | No | Activities per API call (default: 200, max: 200) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses pagination support and filtering capabilities, which is helpful. However, it doesn't mention authentication requirements, rate limits, error conditions, or what 'complete activity history' entails (e.g., all-time vs. limited period). The behavioral context is partially covered but incomplete.
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 efficiently structured in two sentences: the first states core functionality, the second adds important behavioral detail about pagination. Every word earns its place with zero redundancy or fluff.
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 7-parameter tool with no annotations and no output schema, the description provides basic functional context but lacks details about authentication, error handling, return format, or performance characteristics. It's minimally adequate given the schema handles parameter documentation, but more behavioral context would be helpful.
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 all 7 parameters. The description adds minimal value beyond the schema by mentioning 'optional filtering by date range and activity type' and 'pagination', but doesn't provide additional semantic context about parameter interactions or usage patterns.
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 'fetches complete activity history' with filtering capabilities, providing a specific verb ('fetches') and resource ('activity history'). It distinguishes from sibling tools like 'get-recent-activities' by emphasizing 'complete' history, though it doesn't explicitly name alternatives.
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 for retrieving comprehensive activity data with filtering, but doesn't explicitly state when to use this versus alternatives like 'get-recent-activities' or 'get-activity-details'. No guidance on prerequisites, exclusions, or specific scenarios is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-athlete-profileA
Fetches the profile information for the authenticated athlete, including their unique numeric ID needed for other tools like get-athlete-stats.
| 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 correctly uses 'Fetches' to imply a read-only operation and notes that it returns an ID, but it does not disclose details such as authentication scope, rate limits, or the exact set of profile fields returned. This is adequate for a simple no-parameter read tool but falls short of rich transparency.
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 sentence that is front-loaded with the action and resource, immediately states the output's key value, and includes a concrete example of downstream usage. Every word contributes, with no redundancy or filler.
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 simple read-only tool with no parameters and no output schema, the description is reasonably complete: it names the resource, identifies the primary output (numeric ID), and provides a usage link to other tools. However, it does not enumerate the full profile fields or specify any error conditions, so it stops short of a 5.
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 zero parameters, so the input schema is empty and no parameter documentation is required. The description adds meaningful context about the output (the numeric ID), which is more than the schema provides. Baseline for zero-parameter tools is 4, and this description meets that bar.
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 function with a specific verb ('Fetches') and resource ('profile information for the authenticated athlete'). It also explicitly distinguishes the tool by highlighting the unique numeric ID that other tools (e.g., get-athlete-stats) depend on, which sets it apart from sibling tools.
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 clear usage context by stating the profile ID is needed for other tools like get-athlete-stats, implying this should be called first to obtain that ID. It does not explicitly mention when not to use it or list alternatives, but the guidance is practical and unambiguous for its intended role.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-athlete-statsA
Fetches the activity statistics (recent, YTD, all-time) for a specific athlete using their ID. Requires the athleteId obtained from the get-athlete-profile tool.
| Name | Required | Description | Default |
|---|---|---|---|
| athleteId | Yes | The unique identifier of the athlete to fetch stats for. Obtain this ID first by calling the get-athlete-profile tool. |
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 mentions the prerequisite (athleteId requirement) which is useful context, but doesn't disclose other behavioral traits like rate limits, authentication needs, error conditions, or what the output format looks like (since no output schema exists).
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?
Two sentences with zero waste. The first sentence states purpose and scope, the second provides critical prerequisite information. Every word earns its place and the description is appropriately sized for a single-parameter tool.
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 read-only tool with 100% schema coverage but no annotations and no output schema, the description provides adequate purpose and usage guidance. However, it lacks information about return values (what the stats actually contain) and other behavioral context that would be helpful given the absence of structured output documentation.
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 already documents the single parameter. The description adds value by explaining where to obtain the athleteId ('from the get-athlete-profile tool'), which provides practical guidance beyond the schema's technical specification.
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 specific action ('fetches') and resource ('activity statistics for a specific athlete'), specifying the types of statistics (recent, YTD, all-time). It distinguishes from siblings like 'get-athlete-profile' by focusing on stats rather than profile 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?
Explicitly states when to use this tool ('for a specific athlete using their ID') and provides a prerequisite ('Requires the athleteId obtained from the get-athlete-profile tool'), clearly differentiating it from alternatives that don't require this ID or fetch different data types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-athlete-zonesA
Retrieves the authenticated athlete's configured heart rate and power zones.
Output includes both a formatted summary and the raw JSON data.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Although no annotations exist, the description adds useful context by specifying that output includes both a formatted summary and raw JSON, and it implies authentication scope with 'authenticated athlete.' However, it does not explicitly confirm read-only semantics or discuss any caveats.
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?
Two concise sentences deliver purpose and output details with no irrelevant content. Front-loaded and efficient.
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 parameterless read operation, the description covers what it does and what it returns. There is no output schema to elaborate, and the complexity is low, so this is sufficient.
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?
There are zero parameters, so the description does not need to explain any inputs. The baseline of 4 applies, and the description adds no param-specific details.
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 identifies the action (retrieves) and the resource (the athlete's configured heart rate and power zones). This distinguishes it from sibling tools like get-athlete-stats or get-athlete-profile, making its purpose unambiguous.
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 explicit guidance is provided for when to use this tool instead of alternatives. The usage is implied by the clear purpose, but there are no exclusions or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-recent-activitiesA
Fetches the most recent activities for the authenticated athlete.
| Name | Required | Description | Default |
|---|---|---|---|
| perPage | No | Number of activities to retrieve (default: 30) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly indicates a read operation ('Fetches'), which implies non-destructive behavior, but it does not disclose pagination behavior, rate limits, or what 'most recent' means in terms of time range. The verb provides basic transparency but not rich details.
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 sentence with no filler or redundancy. It is front-loaded with the verb and resource, 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?
The tool is simple with one optional parameter and no output schema, so the description is largely adequate. However, it does not clarify how this differs from the sibling tool get-all-activities, which could lead to incorrect selection. Additionally, no behavioral edge cases are mentioned, leaving some context missing.
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 covers the sole parameter (perPage) with a description and default value, yielding 100% schema coverage. The tool description does not mention the parameter, but the schema already explains it, so the baseline of 3 applies.
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 uses a specific verb 'Fetches' and a clear resource: 'most recent activities' for the authenticated athlete. The scope ('most recent') helps distinguish it from siblings like get-all-activities and get-activity-details.
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 such as get-all-activities or get-activity-details. The description simply states what it does without giving context on when it is the preferred choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-routeA
Fetches detailed information about a specific route using its ID.
| Name | Required | Description | Default |
|---|---|---|---|
| routeId | Yes | The unique identifier of the route to fetch. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry behavioral information. 'Fetches' implies read-only, but the description does not mention authentication needs, error handling, or the exact nature of 'detailed information'. It adds minimal value beyond the obvious.
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, tightly written sentence that front-loads the verb and resource. Every word earns its place, with no fluff or redundancy.
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 simple single-parameter fetch, the description covers the core purpose. However, without an output schema, 'detailed information' is vague about what exactly is returned. It also omits prerequisites or failure scenarios, leaving some gaps for an 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 already provides a complete description of the only parameter (routeId) with pattern and explanation. The description's 'using its ID' simply restates the schema, adding no extra meaning. With 100% schema coverage, the baseline of 3 applies.
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 uses 'fetches' with a specific resource ('a specific route') and method ('using its ID'), clearly distinguishing it from listing, searching, or exporting sibling tools. It is specific and unambiguous.
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 like find-nearby-routes or list-athlete-routes. There are no exclusions, alternatives, or context for choosing this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-segmentC
Fetches detailed information about a specific segment using its ID.
| Name | Required | Description | Default |
|---|---|---|---|
| segmentId | Yes | The unique identifier of the segment to fetch. |
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 it 'fetches detailed information' but doesn't specify what 'detailed' includes, whether it's a read-only operation, if it requires authentication, or any rate limits. This leaves significant gaps for an agent to understand the tool's behavior.
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 function 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 'detailed information' includes, potential error cases, or authentication requirements. For a tool in a context with many siblings and no structured output, more context is needed for effective use.
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 the parameter 'segmentId' fully documented in the schema. The description adds no additional semantic context beyond implying it's used to fetch a segment, which aligns with the schema. This meets the baseline for high schema 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 ('fetches') and resource ('detailed information about a specific segment'), making the purpose understandable. However, it doesn't distinguish this tool from similar siblings like 'get-segment-effort' or 'list-segment-efforts', which reduces its differentiation value.
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-segment-effort' or 'list-segment-efforts'. It mentions using a segment ID but doesn't specify prerequisites, such as needing an authenticated connection or when this is the appropriate fetch method.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-segment-effortC
Fetches detailed information about a specific segment effort using its ID.
| Name | Required | Description | Default |
|---|---|---|---|
| effortId | Yes | The unique identifier of the segment effort to fetch. |
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 'fetches' information, implying a read-only operation, but doesn't disclose behavioral traits such as authentication requirements, rate limits, error handling, or what 'detailed information' entails (e.g., fields returned, format). This leaves gaps for safe and effective use.
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 ('fetches detailed information') without unnecessary words. Every part earns its place, 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 doesn't explain what 'detailed information' includes (e.g., fields like time, distance, athlete details), which is critical for a tool with one parameter and no structured output documentation. This leaves significant gaps for an agent to understand the tool's behavior and results.
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 the parameter 'effortId' well-documented in the schema. The description adds minimal value beyond the schema by mentioning 'using its ID', which aligns with but doesn't expand on the schema's description. Baseline 3 is appropriate as the schema does the heavy lifting.
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 ('fetches detailed information') and resource ('about a specific segment effort using its ID'), which distinguishes it from siblings like 'list-segment-efforts' (plural listing) and 'get-segment' (segment vs. segment effort). However, it doesn't specify what 'detailed information' includes, leaving some ambiguity.
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 explicit guidance on when to use this tool versus alternatives is provided. It doesn't mention prerequisites (e.g., needing an effort ID from 'list-segment-efforts'), contrast with 'get-segment' (which fetches segment metadata), or specify use cases like retrieving performance data for analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-server-versionA
Returns the Strava MCP server version and related metadata.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It says 'Returns,' implying a safe read operation, but does not explicitly state non-mutating behavior, authentication needs, or side effects. For a simple version check this is adequate yet not highly transparent.
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?
A single sentence with a clear verb and object, no filler. It is appropriately sized for a trivial utility tool.
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 zero parameters, no output schema, and a simple informational purpose, the description fully covers the necessary context. There is no missing information that would hinder correct invocation or interpretation.
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 zero parameters, so no parameter details are needed. The 0-parameter baseline of 4 applies, since there is nothing for the description to add beyond what the schema already conveys.
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 uses the specific verb 'Returns' and clearly identifies the resource (Strava MCP server version and related metadata). This distinguishes it from sibling tools that focus on athlete data, activities, and segments.
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 use when you need server version info, but it does not explicitly state when to use this tool vs alternatives, nor does it mention any exclusions. Given no sibling provides this function, the lack of contrast is minor, but the guidance is still not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list-athlete-clubsA
Lists the clubs the authenticated athlete is a member of.
| 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 must carry the burden of behavioral disclosure. It conveys that the operation is a read (List) and scoped to the authenticated athlete, but it does not mention pagination, required OAuth scopes, rate limits, or response format. This is basic transparency but not comprehensive.
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?
A single sentence front-loaded with the verb 'Lists', followed by the resource and scope. No wasted words, clear and directly 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 zero-parameter, read-only list tool without an output schema, the description provides the essential information: what is listed and for whom. It could mention pagination or return type, but the simplicity of the tool makes the description adequately complete.
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 zero parameters and an empty input schema, so baseline for parameter semantics is 4. The description adds no parameter detail because there are none to explain.
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 (Lists), the resource (clubs), and the scope (the authenticated athlete's memberships). It is specific and distinguishes itself from sibling tools since no other club-related tool exists.
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 does not explicitly state when to use this tool over alternatives or provide exclusions, but the usage is implied: it is the tool for retrieving the authenticated athlete's club memberships. No alternative club tool exists, so ambiguity is low, but explicit guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list-athlete-routesA
Lists the routes created by the authenticated athlete, with pagination.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number for pagination | |
| perPage | No | Number of routes per page (max 50) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the read-only listing nature and pagination, but does not describe return format, ordering, or specific authentication needs beyond the phrase 'authenticated athlete'. This is acceptable for a low-risk list operation, but some behavioral details are missing.
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 one concise sentence, front-loaded with the core purpose and including the key pagination detail. No wasted words or redundancy.
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 simple list tool with two optional parameters and no output schema, the description provides sufficient context: it states what is listed, ownership, and pagination. It does not specify the response shape, but that is less critical for a standard list endpoint.
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%: both 'page' and 'perPage' have descriptions with defaults, ranges, and meanings. The tool description adds no additional parameter context beyond the schema, so the baseline 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 'lists' and the resource 'routes', with scope 'created by the authenticated athlete'. This distinguishes it from siblings like 'get-route' (likely a single route) and 'list-athlete-clubs' (a different resource), making the purpose unmistakable.
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 for retrieving the authenticated athlete's routes, and the pagination mention indicates how to handle large result sets. It does not explicitly name alternatives or exclusion conditions, but the context is clear enough for a simple listing tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list-segment-effortsA
Lists the authenticated athlete's efforts on a specific segment, optionally filtering by date.
| Name | Required | Description | Default |
|---|---|---|---|
| segmentId | Yes | The ID of the segment for which to list efforts. | |
| startDateLocal | No | Filter efforts starting after this ISO 8601 date-time (optional). | |
| endDateLocal | No | Filter efforts ending before this ISO 8601 date-time (optional). | |
| perPage | No | Number of efforts to return per page (default: 30, max: 200). |
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 'Lists' and 'filtering by date', but does not describe pagination behavior (implied by 'perPage' in schema but not explained), authentication requirements, rate limits, or what the output looks like. For a tool with no annotations, this leaves significant behavioral gaps.
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 ('Lists the authenticated athlete's efforts on a specific segment') and adds optional filtering information. Every word earns its place with zero waste, 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 4 parameters with 100% schema coverage but no annotations and no output schema, the description is adequate for a read-only list tool but incomplete. It covers the purpose and basic filtering, but lacks details on authentication, pagination behavior, error handling, or return format, which are important for a tool with no structured output schema.
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 already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying date filtering, but does not provide additional semantics or usage context for parameters. Baseline 3 is appropriate when the schema does the heavy lifting.
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 specific action ('Lists'), the resource ('the authenticated athlete's efforts on a specific segment'), and includes optional filtering by date. It distinguishes this tool from siblings like 'get-segment-effort' (singular) and 'get-all-activities' (broader scope), making the purpose precise and differentiated.
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 for listing efforts on a segment with optional date filtering, but does not explicitly state when to use this tool versus alternatives like 'get-all-activities' or 'get-segment-effort'. It provides some context (filtering by date) but lacks guidance on exclusions or specific scenarios where this tool is preferred over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list-starred-segmentsA
Lists the segments starred by the authenticated 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 full burden. It indicates a read-only listing operation ('Lists') and ties data to the authenticated athlete, but does not disclose details about pagination, response format, or authorization requirements beyond the phrase 'authenticated athlete'.
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?
One concise sentence with no extraneous words; front-loaded and easily scanned.
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 simple, no-parameter listing tool, the description adequately conveys the tool's main purpose. However, since there is no output schema, a bit more detail about the return value (e.g., array of segment summaries) could enhance completeness, but it's not critical.
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 zero parameters and 100% coverage, so the baseline is 4. The description need not explain parameters, and it doesn't add conflicting information.
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 uses the specific verb 'Lists' and clearly identifies the resource ('segments') and scope ('starred by the authenticated athlete'), making its purpose unambiguous and distinct from sibling tools like 'get-segment' or 'explore-segments'.
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 for viewing the authenticated athlete's starred segments but provides no explicit guidance on when to use this tool over alternatives or any exclusion criteria. The intended use case is clear from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
star-segmentA
Stars or unstars a specific segment for the authenticated athlete.
| Name | Required | Description | Default |
|---|---|---|---|
| segmentId | Yes | The unique identifier of the segment to star or unstar. | |
| starred | Yes | Set to true to star the segment, false to unstar it. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full responsibility for behavioral disclosure. It reveals the mutation (star/unstar) but does not disclose potential side effects, idempotency, required auth scopes, or what happens if the segment is already starred/unstarred. The mention of 'authenticated athlete' hints at authorization but lacks detail.
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 concise sentence: 'Stars or unstars a specific segment for the authenticated athlete.' It front-loads the action and contains no superfluous words, making it highly efficient.
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 simple 2-parameter boolean action, the description covers the core purpose. However, since there is no output schema and no annotations, the agent receives no information about return values, errors, or behavioral nuances. It is minimally complete but leaves gaps around expected outcomes and edge cases.
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 both segmentId and starred having clear descriptions. The tool description adds no extra meaning beyond what the schema already provides, so the baseline of 3 is appropriate. The schema sufficiently explains each parameter's purpose.
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 action: 'Stars or unstars a specific segment'. It specifies the resource (a specific segment) and the actor (the authenticated athlete). This distinguishes it from siblings like list-starred-segments, which lists segments rather than modifying their star status.
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 implicitly conveys usage: use this to change the starred status of a segment. However, it does not explicitly mention alternatives or when not to use, such as using list-starred-segments to view stars or get-segment for details. No exclusions are stated, so it is minimally sufficient but lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
5 tool updates
v1.0.1- Added
check-strava-connection - Added
connect-strava - Added
disconnect-strava - Changed
get-activity-streams2 fields changed- added
Input schema / properties / formatAdded value: +{ + "default": "compact", + "description": "Output format:\n- compact: Raw arrays, minified JSON (~70-80% smaller, LLM-friendly)\n- verbose: Human-readable objects with formatted values (backward compatible)", + "enum": [ + "compact", + "verbose" + ], + "type": "string" +} - added
Input schema / properties / max_pointsAdded value: +{ + "description": "Maximum number of data points to return. If activity exceeds this, data will be intelligently downsampled while preserving peaks and valleys. Useful for very large activities.", + "type": "number" +}
- Added
get-server-version
6 tool updates
v1.0.0- Added
get-activity-photos - Added
get-all-activities - Changed
get-athlete-profile1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
- Changed
get-athlete-zones1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
- Changed
list-athlete-clubs1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
- Changed
list-starred-segments1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
18 tool updates
- First observed
explore-segments - First observed
export-route-gpx - First observed
export-route-tcx - First observed
get-activity-details - First observed
get-activity-laps - First observed
get-activity-streams - First observed
get-athlete-profile - First observed
get-athlete-stats - First observed
get-athlete-zones - First observed
get-recent-activities - First observed
get-route - First observed
get-segment - First observed
get-segment-effort - First observed
list-athlete-clubs - First observed
list-athlete-routes - First observed
list-segment-efforts - First observed
list-starred-segments - First observed
star-segment
TDQS
Scored across 24 tools
Most tools have distinct purposes targeting specific Strava resources like activities, segments, routes, or athlete data, with clear boundaries. However, some overlap exists between get-all-activities and get-recent-activities, which could cause confusion about which to use for general activity retrieval, though descriptions help differentiate them by scope.
Tool names follow a consistent verb-noun pattern with hyphens (e.g., get-activity-details, list-athlete-clubs), making them predictable and readable. Minor deviations include check-strava-connection and export-route-gpx, which slightly break the pattern but maintain overall coherence.
With 24 tools, the count is borderline high for a Strava integration, potentially overwhelming for agents. While it covers many aspects of the Strava API, some tools like get-server-version or check-strava-connection might be considered non-essential, contributing to a slightly bloated set.
The tool set provides comprehensive coverage of the Strava domain, including athlete management, activities, segments, routes, and data export. It supports full CRUD-like operations (e.g., connect/disconnect, star/unstar, get/list) and handles key workflows like activity analysis and segment tracking without obvious gaps.
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Related MCP Connectors
Strava MCP tools for AI: athletes, activities, segments, clubs, routes. Powered by HAPI MCP server.
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 β activities, sleep, HRV, training, workouts. Free, open source.
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