igpsport-mcp

by dengxuhui

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct aspect: training load analysis, activity comparison, lap splits, raw streams, summary metrics, athlete profile, aggregate stats, and activity listing. No functional overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (underscore-separated) with 'get_' for most retrievals and 'analyze_'/'compare_' for specific operations. No mixing of conventions.
Tool Count5/5
8 tools cover the domain of cycling/fitness data retrieval without being excessive. Each tool serves a clear purpose, and the count is within the ideal 3-15 range.
Completeness4/5
The tool set covers core operations: listing, summary, streams, laps, athlete profile, stats, training load, and comparison. A minor gap is the lack of route/segment data, but the core analytics are well represented.
Average 3.2/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 33 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
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}
```
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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?
With no annotations, the description must carry the full burden of behavioral disclosure. It mentions computation of NP from the record stream, implying a read operation, but does not explicitly state safety (read-only), required permissions, side effects, rate limits, or error conditions. The description is insufficient for an agent to understand behavioral expectations.
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 extremely concise at 12 words, with one sentence that front-loads the key output. It could be improved by clarifying the verb, but it is efficient with no wasted words.
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 output schema exists, the description does not need to detail all return fields, but it should explain the computed metric NP and how splits are organized. It feels incomplete for an agent unfamiliar with cycling metrics, but the output schema may compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter ride_id is self-explanatory from its name, but schema coverage is 0% and the description does not elaborate on its format, constraints, or relationship to the returned data. The description adds no meaning beyond the parameter name.
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 'Per-lap splits with per-lap NP computed from the record stream' clearly indicates the tool returns lap-level data with computed normalized power, distinguishing it from siblings like get_activity_streams (raw data) or get_activity_summary (overall data). However, it is phrased as a noun phrase rather than a verb phrase, missing an explicit action word like 'Retrieve'.
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?
No guidance is provided on when to use this tool versus alternatives such as get_activity_streams or get_activity_summary. There is no information about prerequisites, data freshness, or when this tool is appropriate.
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. It does not mention that this is a read-only operation, does not disclose any destructive behavior, auth needs, or side effects. Only the basic aggregation is stated.
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?
Single sentence, no fluff. Efficiently conveys the core purpose. Could be slightly more structured but highly concise.
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?
For a simple tool with two parameters and an output schema, the description covers the main idea. However, missing behavioral context (e.g., read-only) and parameter details make it minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds the allowed period values (week, month, year, all). However, it does not explain the 'end_date' parameter or how values are interpreted. Incomplete parameter documentation.
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 aggregates distance/duration/elevation over specified time periods (week, month, year, all). It distinguishes from sibling tools like get_activity_summary (single activity) and analyze_training_load (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?
No guidance on when to use this tool vs alternatives, no prerequisites or exclusions. It simply describes the aggregation without context.
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 full burden. It mentions 'cached' and 'units fixed', giving some behavioral insight, but fails to disclose other traits like read-only nature, rate limits, or response behavior for missing data. Significant gaps remain.
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 a single concise sentence, front-loading the core action and key features. While efficient, it could include more detail without sacrificing brevity, but it earns a high score for avoiding verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema and 5 parameters, the description does not mention sport_type filtering or explain the caching behavior's impact. It also uses 'rides' instead of 'activities', potentially confusing. The description is incomplete for a list tool with filtering and paging.
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 coverage is 0%, so description must compensate. It explains date range (start_date, end_date) and paging (limit, offset) as optional, but omits the sport_type parameter entirely. This partial coverage provides basic meaning but leaves one parameter undocumented.
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 lists rides (activities) with optional date range and paging. It uses a specific verb 'List' and resource 'rides', which is distinct from sibling tools like get_activity_summary or compare_activities, though the term 'rides' slightly diverges from the tool name 'list_activities'.
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?
No guidance on when to use this tool versus alternatives such as get_activity_summary or compare_activities. The description lacks any 'when to use' or 'when not to use' information, leaving the agent without context for selection.
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, the description carries the full burden. It discloses that the tool returns daily trends and form interpretation, implying read-only behavior. However, it lacks details on authentication requirements, rate limits, or any potential side effects. The behavioral intent is clear but not fully transparent.
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 a single efficient sentence that conveys the core functionality. It is front-loaded with key acronyms and purpose. However, it could be slightly more structured (e.g., listing what the tool returns) without losing conciseness.
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 that an output schema exists (though not visible), the description is adequate for a tool that calculates training load metrics. It mentions the trend and interpretation over N days. However, it does not explain the output structure or how the tool relates to the athlete's data, which might be needed for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the parameters. It references 'over the last N days,' which loosely describes the 'days' parameter, but 'end_date' is not mentioned at all. The description fails to add meaning beyond the schema structure.
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: analyzing CTL/ATL/TSB daily trends and providing current form interpretation over N days. It distinguishes from sibling tools like list_activities or get_activity_summary by focusing on training load metrics. However, it assumes knowledge of the acronyms CTL/ATL/TSB, which might not be immediately clear to all agents.
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?
No explicit when-to-use or when-not-to-use guidance is provided. The description implies use for training load trend analysis, but does not compare with sibling tools or specify contexts where this tool is preferred or inappropriate.
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 provided, so the description carries full burden. It mentions 'to keep tokens sane' hinting at token usage, but does not disclose other behavioral traits like rate limits, auth needs, or side effects. It does not explicitly state the tool is read-only.
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?
Two sentences, front-loaded with core purpose, no wasted words. Ideal length for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters and an output schema, the description is too sparse. It leaves many questions about available channels, offset behavior, and response format, which is not fully compensated by the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, yet the description only adds meaning for two of five parameters (channels default, resolution default). It does not explain start_offset_s, end_offset_s, or ride_id beyond the 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 tool returns time-series channels as compact bare arrays, with defaults for channels and resolution. This distinguishes it from sibling tools that provide summaries or analyses.
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?
No guidance on when to use this tool vs. siblings like get_activity_summary or get_activity_laps. The description implies use for raw data but lacks explicit context.
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, the description partially covers behavior by mentioning output format (delta%, narrative hint). However, it doesn't disclose whether the tool is read-only, auth requirements, or any side effects.
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?
Single sentence, front-loaded with purpose, no redundant information. Perfectly concise.
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 output schema exists, the description doesn't need to detail return values entirely, but it lacks context on ride count limits, metric definitions, and error handling. Adequate but leaves ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description adds minimal meaning. It mentions 'rides' and 'metrics' but does not explain ride_ids format or valid metric values. The default null for metrics is implied but not clarified.
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 compares 2-5 rides across metrics with specific outputs (per-metric delta% and narrative hint). It distinguishes from siblings like analyze_training_load or get_activity_summary by focusing on multi-ride comparison.
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?
No guidance on when to use this tool versus alternatives like get_activity_summary for single rides or analyze_training_load for training load. The description does not specify prerequisites or exclusions.
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 provided. Description implies read-only access but does not explicitly state safety (non-destructive), authentication needs, or rate limits. Minimal behavioral disclosure beyond the basic return type.
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?
Single concise sentence with no redundancy. Front-loads key information (what it returns) and is immediately useful.
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?
Has output schema to define return values. Description mentions specific parameters (FTP, LTHR) and zones, which is sufficient for a no-argument getter. Could hint that data is per athlete and from config, but not necessary.
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?
No parameters exist, so schema coverage is 100%. Description adds no parameter info, which is acceptable as there are none. Baseline for zero parameters is 4.
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 returns athlete training parameters (FTP, LTHR) and zone bounds from config. It uses specific terms and distinguishes from sibling tools like get_athlete_stats which probably returns aggregate stats, not config parameters.
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?
No guidance on when to use this tool versus siblings like analyze_training_load or get_athlete_stats. The description does not mention contexts, prerequisites, or alternatives.
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, the description carries full burden. It indicates the metrics are 'derived', suggesting computation, but does not mention rate limits, data freshness, or error handling. The brief description is sufficient for a simple read-only tool but lacks explicit behavioral traits.
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?
A single sentence that is front-loaded with key information ('Derived metrics for one ride') and lists specific metrics. No unnecessary words or redundancy.
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 simplicity (one required parameter, no nested objects) and the presence of an output schema, the description provides sufficient context about the returned metrics. However, it lacks guidance on edge cases like missing ride or access errors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not add any detail about the 'ride_id' parameter (e.g., format, required uniqueness). The description only implies it identifies a ride, leaving the agent with minimal guidance beyond the schema's basic type.
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 explicitly states 'Derived metrics for one ride' and lists specific metrics (NP/IF/TSS/work, HR & power zone time), making it clear what the tool does and distinguishing it from siblings like get_activity_laps or get_activity_streams which provide raw data.
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 for obtaining summary metrics for a single ride but does not explicitly specify when to use this tool over alternatives (e.g., compare_activities or analyze_training_load) nor provide exclusions.
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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