Strava MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: get_activity retrieves details of a specific activity, get_activity_segments focuses on segments within an activity, and get_user_activities lists the user's activities. The descriptions clearly differentiate these functions, making tool selection unambiguous for an agent.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern with snake_case (get_activity, get_activity_segments, get_user_activities). The naming is predictable and readable, using 'get' as the verb throughout, which aligns well with the read-only nature of these tools.
Tool Count2/5With only 3 tools, this server feels under-scoped for a Strava integration, which typically involves activities, segments, athletes, and more. While the tools cover some read operations, the count is too low to support comprehensive agent workflows, such as creating or updating activities, which are common in fitness tracking domains.
Completeness2/5The tool surface is severely incomplete for a Strava server, as it only includes read operations (get) with no support for create, update, or delete actions. There are significant gaps, such as missing tools for managing segments, athletes, or uploading activities, which will likely cause agent failures when attempting full interactions with the Strava API.
Average 3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get details' but doesn't clarify if this is a read-only operation, what permissions might be required, error handling, or response format beyond 'The activity details.' 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a clear purpose statement followed by Args and Returns sections, making it easy to parse. It's concise with no wasted words, though the Args section could be more integrated into the main text for better flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (2 parameters, no output schema, no annotations), the description is incomplete. It lacks details on error cases, authentication needs, rate limits, or what 'activity details' include, which are crucial for proper tool invocation. Without annotations or output schema, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal semantics beyond the input schema. It explains that 'activity_id' is 'The ID of the activity' and 'include_all_efforts' is 'Whether to include all segment efforts,' which clarifies purpose but doesn't provide format details or examples. With 0% schema description coverage, this partially compensates but remains basic, aligning with the baseline expectation when schema coverage is low.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get details of a specific activity.' This is a specific verb ('Get') and resource ('activity'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_activity_segments' or 'get_user_activities', which likely retrieve related but different data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 sibling tools like 'get_activity_segments' (which might retrieve parts of an activity) or 'get_user_activities' (which might list multiple activities), leaving the agent to infer usage context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] the segments' and returns a 'List of segment efforts for the activity', which implies a read-only operation, but it doesn't disclose any behavioral traits such as authentication needs, rate limits, error handling, or what 'segment efforts' entail. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the main purpose stated first ('Get the segments of a specific activity.'). The Args and Returns sections are structured but could be more integrated. It avoids unnecessary fluff, but the separation into sections might slightly reduce flow. Overall, it's efficient with little waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a read operation with one parameter) and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'segment efforts' are, how the list is structured, or any behavioral aspects like pagination or errors. For a tool with no structured data support, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal meaning beyond the input schema. It mentions 'activity_id: The ID of the activity', which is already clear from the schema's title 'Activity Id' and type 'integer'. With 0% schema description coverage, the description doesn't compensate by providing additional context, such as where to find the activity ID or format requirements. However, since there's only one parameter, the baseline is higher, but it still lacks enrichment.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the segments of a specific activity.' It uses a specific verb ('Get') and resource ('segments of a specific activity'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish this from sibling tools like 'get_activity' or 'get_user_activities' in terms of what specific data it returns versus those tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 sibling tools like 'get_activity' or 'get_user_activities', nor does it specify prerequisites, exclusions, or contextual cues for choosing this tool over others. The only implied usage is when you need segments for a specific activity, but this is basic and lacks comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
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 that it returns a 'List of activities' and includes pagination parameters, which hints at a read-only operation. However, it lacks details on authentication requirements, rate limits, error handling, or what constitutes an 'activity' (e.g., format, fields). This leaves significant gaps for an agent to understand the tool's behavior fully.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with a clear purpose statement, followed by organized sections for 'Args' and 'Returns'. Each sentence adds value without redundancy, making it easy to parse and understand quickly. There's no wasted verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and parameters but lacks details on authentication, error cases, sibling differentiation, and the structure of returned activities. Without an output schema, the agent must infer the return format from the vague 'List of activities'. This is adequate but has clear gaps for a tool with multiple parameters and siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, but the description compensates by listing all four parameters ('before', 'after', 'page', 'per_page') with brief explanations in the 'Args' section. It clarifies that 'before' and 'after' are epoch timestamps for filtering, and 'page' and 'per_page' handle pagination. This adds meaningful context beyond the bare schema, though it doesn't detail defaults or constraints like valid ranges.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the authenticated user's activities.' It specifies the verb ('Get') and resource ('authenticated user's activities'), making it clear what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_activity' or 'get_activity_segments', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 its siblings ('get_activity' and 'get_activity_segments'). It doesn't mention any prerequisites, exclusions, or alternative scenarios. The only implied usage is for retrieving the user's activities, but this is too vague for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/yorrickjansen/strava-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server