Strava MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific Strava resources: analyze_data_tool for custom analysis, get_activity_details_tool for basic activity info, get_activity_laps_tool for lap data, get_activity_streams_tool for raw metrics, get_athlete_stats_tool for athlete statistics, list_activities_tool for recent activities, and search_activities_tool for filtered searches. There is no overlap or ambiguity between these functions.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case: analyze_data_tool, get_activity_details_tool, get_activity_laps_tool, get_activity_streams_tool, get_athlete_stats_tool, list_activities_tool, and search_activities_tool. This uniformity makes the set predictable and easy to understand.
Tool Count5/5
With 7 tools, this server is well-scoped for a Strava integration, covering core operations like retrieving activities, analyzing data, and accessing athlete stats. Each tool serves a specific, necessary function without bloat, making the count appropriate for the domain.
Completeness4/5
The tool set provides strong coverage for reading and analyzing Strava data, including activities, streams, laps, and athlete stats, with a flexible search tool. A minor gap exists in write operations (e.g., creating or updating activities), but the read-focused surface is largely complete for typical agent workflows.
Average 3.4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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
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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 retrieves data ('Get'), implying a read operation, but doesn't cover critical aspects like authentication requirements, rate limits, error handling, or what 'lap breakdowns' entail (e.g., format, data fields). This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the purpose clearly, and the 'Args' section is brief. However, the 'Args' formatting is slightly redundant since the schema already defines the parameter, but it doesn't add unnecessary length. Overall, it's efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is somewhat complete. However, it lacks usage guidelines and behavioral details, which are important for effective tool invocation. This makes it minimally viable but with clear gaps in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does 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 to retrieve laps for,' which clarifies the parameter's purpose but doesn't provide format details (e.g., integer type, source). With 0% schema description coverage and only 1 parameter, this is adequate but not comprehensive, aligning with the baseline for low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get lap breakdowns for a specific activity.' This includes a specific verb ('Get') and resource ('lap breakdowns'), and it distinguishes the tool from siblings like 'get_activity_details_tool' or 'get_activity_streams_tool' by focusing on laps. However, it doesn't explicitly contrast with siblings, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an activity ID from another tool), exclusions, or comparisons to siblings like 'get_activity_details_tool' or 'get_activity_streams_tool'. This leaves the agent without context for tool selection.
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 mentions 'recent activities' but does not specify what 'recent' means (e.g., time range, ordering), whether it requires authentication beyond the athlete context, or any rate limits or pagination behavior. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the main purpose stated first followed by parameter details. It avoids unnecessary verbosity, though the structure could be slightly improved by integrating the parameter explanation more seamlessly rather than as a separate 'Args:' section.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter) and the presence of an output schema, the description is somewhat complete but has gaps. It covers the basic purpose and parameter, but lacks behavioral details and usage guidelines. The output schema likely handles return values, so the description's focus on input is acceptable, though more context would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining that 'limit' is the 'Number of activities to return' with a default of 5, which clarifies the parameter's purpose beyond the schema's type and default. However, it does not cover constraints like minimum/maximum values or other potential parameters, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('recent activities for the authenticated athlete'), making the purpose specific and understandable. However, it does not explicitly distinguish this tool from sibling tools like 'search_activities_tool' or 'get_activity_details_tool', which limits its differentiation in context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 such as 'search_activities_tool' or 'get_activity_details_tool'. It lacks context on use cases, prerequisites, or exclusions, leaving the agent to infer usage based on tool names alone.
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 retrieves detailed information, implying a read-only operation, but doesn't cover aspects like authentication needs, rate limits, error handling, or what 'detailed information' entails beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with a clear purpose statement followed by parameter details in a separate 'Args' section. It avoids unnecessary words, though the parameter explanation could be slightly more informative without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the core purpose and parameter role, though it lacks behavioral context and usage guidelines, which are minor gaps in this simple scenario.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal semantics beyond the input schema, which has 0% description coverage. It specifies that 'activity_id' is for retrieving a specific activity, but doesn't explain format, constraints, or examples. With only one parameter, this is adequate but not comprehensive, aligning with the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 ('Get') and resource ('detailed information for a specific activity'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'get_activity_laps_tool' or 'get_activity_streams_tool', which might provide overlapping or related activity details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like 'list_activities_tool' for browsing activities or 'get_activity_laps_tool' for lap-specific data, 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that it 'Returns a formatted string with recent and all-time stats,' which gives some insight into output format, but it doesn't cover critical aspects like authentication requirements, rate limits, error handling, or whether it's a read-only operation. For a tool with zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured, consisting of just two sentences that efficiently convey the core functionality and output. Every word earns its place, with no redundant information, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, output schema exists), the description is adequate but has clear gaps. It explains the output format ('formatted string with recent and all-time stats'), which complements the output schema, but lacks details on authentication, error cases, or usage context. With no annotations and sibling tools present, it should do more to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, meaning there are no parameters to document. The description doesn't need to add parameter semantics beyond what the schema provides. It appropriately focuses on the tool's purpose and output, earning a baseline score of 4 for this context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get statistics for the authenticated athlete.' It specifies the verb ('Get') and resource ('statistics for the authenticated athlete'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate this from sibling tools like 'get_activity_details_tool' or 'analyze_data_tool', which might also involve athlete data analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 any prerequisites, context for use, or comparisons to sibling tools such as 'list_activities_tool' or 'search_activities_tool'. This lack of usage guidelines leaves the agent to infer appropriate scenarios based on tool names alone.
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 full burden. It describes what data is retrieved but doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, or what happens when invalid parameters are provided. The description is functional but lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement followed by detailed parameter explanations. Every sentence adds value, though it could be slightly more front-loaded by integrating parameter details more seamlessly. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with no schema descriptions and an output schema present, the description does an excellent job explaining parameter semantics. It doesn't need to cover return values due to the output schema. However, it lacks behavioral context like error handling or performance characteristics, which would be helpful despite the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all three parameters: activity_id (ID of the activity), types (list of stream types with specific options and default behavior), and resolution (data point resolution options with point counts and default). It adds crucial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'raw stream data (GPS, HR, power, etc.) for a specific activity.' It distinguishes from siblings by focusing on stream data retrieval rather than activity details, laps, stats, or listing/searching activities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying it's for retrieving stream data for a specific activity, but it doesn't explicitly state when to use this tool versus alternatives like get_activity_details_tool or analyze_data_tool. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses that name/description search is client-side (implying performance considerations), recommends a reasonable limit, specifies case-insensitive partial matching for queries, and provides default values and examples. This goes beyond basic functionality to include implementation details and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a brief overview followed by detailed parameter explanations. Every sentence earns its place by providing necessary information. It could be slightly more front-loaded with a clearer distinction from siblings, but overall it's efficient and organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 7 parameters, 0% schema coverage, no annotations, but an output schema, the description is quite complete: it covers all parameters, behavioral constraints, and usage notes. The presence of an output schema means return values don't need explanation. It adequately addresses the complexity, though could benefit from more explicit sibling differentiation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the description fully compensates by explaining all 7 parameters in detail: it clarifies that 'query' uses case-insensitive partial matching, provides examples for 'activity_type', specifies ISO 8601 format for date parameters, explains units for distance parameters, and states the default for 'limit'. This adds essential meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches activities with optional filters, providing a specific verb ('search') and resource ('activities'). It distinguishes from siblings like 'list_activities_tool' by emphasizing search capabilities, though it doesn't explicitly contrast them. The purpose is unambiguous but lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance through the note about client-side search and recommendation for a reasonable limit, suggesting when to use caution. However, it doesn't explicitly state when to use this tool versus alternatives like 'list_activities_tool' or other siblings, nor does it provide clear exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 'safely using Monty', which hints at security or sandboxing, but does not detail specific behavioral traits such as execution limits, error handling, or output format. It adds some context but leaves gaps in transparency for a code execution tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by detailed parameter explanations. Every sentence earns its place by adding essential information without redundancy, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (code execution with data analysis), no annotations, and no output schema, the description is incomplete. It covers parameters well but lacks details on behavioral aspects like safety mechanisms, execution environment, or return values. For a tool with this level of complexity, more context is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'code' is Python code with an example and that 'data' is the data to analyze, specifying it can be a JSON object or string. This fully compensates for the schema's lack of documentation, providing clear semantics for both parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Execute Python code to analyze Strava data') and identifies the resource ('Strava data'). It distinguishes from sibling tools like 'get_activity_details_tool' or 'list_activities_tool' by focusing on custom analysis rather than data retrieval, making its role explicit and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying that it analyzes 'Strava data safely using Monty' and includes an example, but it does not explicitly state when to use this tool versus alternatives like 'get_athlete_stats_tool' or 'search_activities_tool'. It implies usage for custom analysis but lacks explicit exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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