TrackLab MCP Ultimate Edition

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools target distinct analytical functions (e.g., calculate_vdot vs predict_race), but some pairs like get_latest_activity vs list_recent_activities and get_fitness_fatigue_form vs get_training_readiness could be confused. Descriptions generally clarify the differences.
Naming Consistency3/5
The majority follow a verb_noun pattern (get_*, calculate_*, analyze_*), but several deviate: provider_status, provider_ping, and training_dashboard are noun-led. This mixed convention is readable but not fully predictable.
Tool Count4/5
With 21 tools, the server is at the higher end of the reasonable range for a comprehensive endurance analytics platform. Each tool has a specific purpose, and the count aligns with the 'Ultimate Edition' scope, though it feels slightly heavy.
Completeness3/5
The domain of training analytics is well covered with load metrics, threshold analysis, and race planning, but there are notable gaps: no tool to fetch a single activity's detailed stream (only analyze_activity_drift references streams), and no explicit activity detail view. This could force workarounds.
Average 3.6/5 across 21 of 21 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
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.json to 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?
With no annotations, the description must disclose behavioral traits, but it only says 'fetches', implying a read operation. It does not describe the return structure (e.g., fields in the 'summary'), whether an empty result is possible, or any limitations. This is minimal disclosure for a tool with no output schema or annotations to supplement.
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 a single sentence with no redundant words or filler. It delivers the core purpose immediately and is appropriately sized for a simple, parameterless tool.
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?
The description is minimally adequate: it tells the agent what the tool does but omits any mention of return values or edge cases (e.g., absence of recent activity). The presence of output schema would have helped, but since it is absent, the description could have been more informative about what the 'summary' contains. However, for such a simple tool, this is barely sufficient.
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 tool has zero parameters, so the description does not need to explain parameter meanings. Per the rubric's baseline for 0 params, a score of 4 is appropriate; the description correctly avoids unnecessary parameter details.
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 identifies the action ('Fetches') and the resource ('most recent workout session and summary'), which distinguishes it from list-oriented siblings like 'list_recent_activities'. However, it does not explicitly name any alternative tools or further differentiate among the many get_* tools, so it falls just short of a 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?
There is no guidance on when to use this tool versus alternatives such as 'list_recent_activities' or 'analyze_activity_drift'. The description provides no context about typical use cases, prerequisites, or situations where another tool would be more appropriate, so it fails to help the agent choose correctly.
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 only says 'Calculates,' which implies a read-only operation, but it does not explicitly confirm side effects, data dependencies, or prerequisites. This is insufficient for a tool with zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately states the tool's purpose and output. There is no redundancy or unnecessary information, making it highly effective.
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 params, no output schema), the description is minimally sufficient but lacks context about what data feeds the calculation (e.g., HRV, sleep) or when to use it. This makes it adequate but with clear gaps.
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 tool has no parameters, making schema coverage 100% by vacuity. The description appropriately omits parameter details, and the baseline for zero-parameter tools is 4.
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 function with a specific verb ('Calculates') and resource ('Training Readiness score and workout recommendation'). However, it does not differentiate this from similar sibling tools like get_fitness_fatigue_form, so it misses the top score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 whether to prefer it over get_fitness_fatigue_form or get_athlete_profile. It simply states what it does without any usage 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 provided, the description carries the full burden of behavioral disclosure. It implies a read-only operation by using the verb 'Lists' and mentions return fields, but it does not clarify what 'recent' means, whether pagination exists, or the effect of the limit parameter. This is a minimal but acceptable level of transparency for a simple list operation.
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 a single sentence with no redundant words. All terms are meaningful and front-loaded, making it highly efficient and easy to parse.
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 tool with one parameter and no output schema, the description is adequate but leaves gaps. It does not define the time window for 'recent,' mention the default behavior of the limit parameter, or describe the response structure. These gaps make it minimally viable but not fully complete.
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 100% with the 'limit' parameter fully documented in the input schema. The description adds no additional meaning beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 recent activities and specifies the included metrics (pace, HR, distance, training load). It doesn't explicitly differentiate from sibling tool get_latest_activity, but the plural 'activities' implies a batch list rather than a single item, which provides some distinction.
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 given on when to use this tool versus alternatives like get_latest_activity or training_dashboard. The description does not mention scenarios, prerequisites, or exclusions, leaving the agent without clear selection criteria.
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?
Without annotations, the description must disclose behavioral traits. It states the action and output but omits side effects, error behavior, or whether it makes external network calls. No contradictions, but significant gaps.
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 a single concise sentence with no filler words. Every word contributes to the meaning.
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 simple zero-parameter tool, the description explains the core function and output. It could mention failure behavior, but the simplicity lowers the burden.
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 tool has zero parameters, so the description cannot add parameter-level detail. Baseline 4 is appropriate as there is nothing to explain.
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?
Description uses a specific verb 'pings' and names the resource 'provider API endpoint' and output 'response latency'. It clearly states the tool's function, though it doesn't explicitly contrast with sibling tools like provider_status.
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 provider_status. The description gives no context about preferred use cases 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 are provided, so the description carries the full burden of behavioral disclosure. It only mentions confidence scoring, which hints at output behavior, but fails to describe side effects, required data context, or the nature of the analysis. The description is too sparse to convey meaningful 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?
The description is a single, compact sentence that conveys the tool's core purpose and additional detail (confidence scoring) without any fluff. It is front-loaded and efficient, earning a top score for conciseness.
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 being simple in structure, the tool is a multi-faceted analysis with no output schema or annotations. The description fails to mention what data it consumes, what the returned result looks like, or how confidence scoring works. This leaves significant gaps for an agent attempting to invoke the tool correctly.
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 tool has zero parameters, and the schema coverage is 100% (empty schema). With no parameters, the description need not explain parameter semantics. The baseline of 4 for zero-parameter tools applies, and the description adds nothing that contradicts or undermines this.
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 function: it executes a multi-literature threshold analysis, listing specific methodologies (Daniels, Norwegian Double Threshold, Casado, Seiler 3-Zone). This specific verb+resource combination distinguishes it from sibling tools like calculate_vdot or get_hr_zones, which target different physiological metrics.
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 does not mention conditions, prerequisites, or exclusions. While the name implies threshold analysis, there is no explicit context to help an agent choose between this and other analysis tools.
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, the description carries the full burden of explaining behavior, but it only states that it calculates a VDOT rating. It does not disclose what the output represents, any range or units, potential error cases, or assumptions about the race distance. The behavioral transparency is minimal.
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 a single, concise sentence that conveys the essential purpose. It contains no filler or repetition, making it an example of efficient writing. Every word contributes value.
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?
The tool is simple with only two parameters, and the schema covers them well. However, no output schema exists, so the description should at least mention what the tool returns (e.g., a VDOT score). It also lacks context on how to use or interpret the result, leaving the implementation incomplete for an agent.
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 100% with both parameters described (distance in meters, time in seconds). The description adds the context 'race performance' but does not provide any additional meaning or relationships beyond the schema. Baseline 3 is appropriate when the schema already documents parameters clearly.
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 calculates a Jack Daniels VDOT rating from race performance. The verb 'calculates' is specific, the resource 'VDOT rating' is well-defined, and 'from a race performance' scopes the input. This distinguishes it from sibling tools that calculate other metrics like TID or threshold.
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 when a race result is available, but it does not explicitly state when to use this tool versus alternatives such as calculate_tid or predict_race. There is no mention of exclusions or alternative tools, but the 'race performance' context provides some 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, the description must disclose behavioral traits. It reveals the calculation method but omits data dependencies, output format, error handling, or whether the operation is read-only. The tool appears to be a computation, but this is not explicitly stated.
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 a single, front-loaded sentence that delivers the core function without excess words. Every part is meaningful and directly helpful.
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?
The description is too sparse for a complex calculation tool. It does not specify what data the calculation uses, what the output looks like, or potential failure modes. Given no schema, annotations, or output schema, the description should provide more context to be fully actionable.
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?
There are zero input parameters, so the baseline is 4. The description adds value by explaining the acronyms, providing context for the calculation without needing to describe individual 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 states the tool calculates a specific model (Banister EWMA PMC) and explicitly defines the metrics (CTL Fitness, ATL Fatigue, TSB Form), making it clear and distinct from sibling tools like calculate_vdot or calculate_tid.
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, nor are any prerequisites or expected contexts mentioned. The agent must infer usage solely from the name and brief description.
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, the description must fully explain behavior. It says 'Calculates' but does not disclose that it likely relies on stored athlete profile data (since there are no parameters), nor does it describe the output format or any side effects. The source of 'LTHR & Max HR' is ambiguous, which could mislead the agent.
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 a single, front-loaded sentence with no filler. It efficiently conveys the core purpose and inputs. Every word contributes value.
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?
For a tool with no output schema, no annotations, and zero parameters, the description is insufficiently complete. It does not explain where LTHR and Max HR come from, what the 'breakdown' looks like (e.g., bpm ranges), or how to interpret the result. This lack of context could hinder correct invocation and result handling.
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?
There are zero parameters, and the schema has no descriptions, so the baseline is 4. The description adds meaning by specifying that the calculation is based on LTHR and Max HR, which clarifies the implicit inputs even though they are not formal 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 a specific action ('Calculates') and resource ('athlete 5-zone HR breakdown') with explicit inputs ('based on LTHR & Max HR'). This distinguishes it from sibling tools like get_pace_zones, which focus on pace rather than heart rate.
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 about when to use this tool versus alternatives such as get_pace_zones or calculate_vdot. There is no mention of prerequisites, conditions, or exclusions, leaving the agent to infer usage context on its own.
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 states the tool generates a workout but does not disclose whether this modifies existing data, requires permissions, or what the output format is. For a generation tool, this leaves significant ambiguity about side effects and return value.
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 a single sentence that is front-loaded with the verb and resource, and includes the key distinguishing types. There is no redundant or unnecessary information.
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?
The tool is simple with only two parameters, but the description lacks details about the output structure or what 'structured step workout' means exactly. Since there is no output schema to compensate, this leaves some ambiguity, though the core purpose is clear.
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 100% for both parameters, so the baseline is 3. The description adds no extra meaning beyond the enum values already in the schema; it merely restates them, and does not explain the repetitions parameter beyond the schema's default.
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?
Description clearly states the verb 'Generates' and resource 'structured step workout', and specifies the two types (Threshold Cruise Intervals or VO2max Intervals). This distinguishes it from sibling tools like generate_race_plan and analyze_threshold.
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?
Usage is implied: the tool should be used when generating a structured step workout of these types. However, there is no explicit guidance on when not to use it or which alternative tools might be more 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 must disclose behavioral traits on its own. It only states that the tool 'Exports' a report, but does not clarify whether the report is returned, saved as a file, or sent somewhere. It also leaves the term 'PMC summary' undefined and does not mention any dependencies on recent activity data or 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?
The description is a single sentence of 9 words, directly stating the verb and object with no redundant or filler content. It is appropriately sized for a simple tool with no parameters and no output schema.
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 (zero parameters, no output schema, no annotations), the description is minimal but leaves key details unclear, such as what 'PMC' stands for and how the export is delivered (returned, file, etc.). For a zero-parameter tool, a bit more context about the report's content and delivery would make it fully complete.
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 zero parameters, so the description is not required to add parameter-level detail. The baseline for zero-parameter tools is 4, and the description confirms that no parameters are needed by simply stating the action and content of the report.
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 uses the specific verb 'Exports' and identifies the resource as 'weekly performance & PMC summary report' with the output format 'GitHub Markdown.' This clearly distinguishes the tool from the sibling analysis and retrieval tools, which focus on getting, analyzing, or calculating data rather than exporting a report.
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?
There is no guidance on when to use this tool versus the many siblings, nor any mention of alternatives, prerequisites, or exclusions. The description simply states the function without providing context for choosing it over other tools.
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 the output nature but does not state whether the calculation relies solely on the two inputs or uses athlete profile data, nor does it describe expected units or any side effects. Some behavioral context is present, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. Every word contributes to conveying the tool's function, making it optimally 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 two-parameter generator, the description is adequate but incomplete. It does not clarify the boundary between 'fuel strategy' and the sibling calculate_race_nutrition tool, nor describe the output format. Given the overlapping siblings, more context would help the agent select correctly.
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 100%, with both distanceKm and targetTimeSeconds described in the schema. The description adds only a hint that pacing is per-kilometer, which slightly enhances distanceKm's meaning, but does not substantially extend the schema. Baseline 3 is appropriate.
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 uses a specific verb ('Generates') and clearly identifies the output: a negative split kilometer pacing chart and fuel strategy. This distinguishes it from sibling tools like build_workout or calculate_race_nutrition, which focus on different deliverable aspects.
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?
There is no explicit guidance on when to use this tool versus alternatives. It does not mention race preparation scenarios, prerequisites, or differentiate from calculate_race_nutrition which likely overlaps on the fuel strategy component. The agent must infer usage from the description 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?
With no annotations, the description carries the full burden of behavioral disclosure. It says 'calculates' but does not mention what input data is used (e.g., threshold pace), whether it reads from a profile, or what the output structure looks like. No safety or side-effect information is provided.
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 a single, front-loaded sentence with every word contributing meaning. It is concise, well-structured, and free of fluff.
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 absence of an output schema and annotations, the description provides only the high-level purpose. It does not explain what the pace zones are based on or what the returned data looks like, but for a simple no-parameter tool it is minimally adequate.
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 tool has zero parameters, so there is no schema to document. The description appropriately focuses on the computation itself, and the baseline for zero-parameter tools is 4, which is appropriate here.
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 calculates athlete pace zones, using a specific verb and resource. The Z1 Easy to Z5 Repetition detail adds specificity and distinguishes it from sibling tools like get_hr_zones and calculate_vdot.
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 guidance is given on when to use this tool versus alternatives such as get_hr_zones or calculate_vdot. The description simply states the function, leaving the agent to infer usage from the name and 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 carries the full burden. The verb 'Checks' implies a read-only operation and the listed outputs give some transparency, but it does not mention failure modes (e.g., what happens if no active provider exists), permissions, or any other behavioral edge cases.
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 a single, efficient sentence that front-loads the purpose and includes all key details without any redundancy or filler.
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 simple, parameterless status tool, the description adequately conveys what the tool returns: provider name, capabilities, and health status. However, it does not clarify what 'health status' entails or how it relates to the sibling provider_ping tool, leaving slight ambiguity.
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 tool has zero parameters, so the baseline is 4 as per the rubric. No parameter descriptions are needed, and the description correctly focuses on the tool's output rather than inputs.
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 uses a specific verb ('Checks') and a clear resource ('active endurance provider'), and it enumerates the returned details (name, capabilities, health status). It is distinct enough from sibling tools like provider_ping, though it does not explicitly compare itself to them.
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 guidance is given about when to use this tool versus alternatives such as provider_ping. The usage context is implied by the wording, but there are no stated conditions, exclusions, or alternative tool references.
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 bears full responsibility for behavioral disclosure. It only lists the data fields returned and does not explicitly state read-only behavior (though 'fetches' implies it), authentication needs, error handling, or caching. This is minimal transparency for a getter.
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 a single, front-loaded sentence that lists the data categories compactly. Every word contributes to defining the tool, with no redundancy or filler.
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 parameter-less fetch tool with no output schema, the description lists the key data categories (profile, LTHR, LT Pace, VDOT, body metrics), giving a reasonable expectation of the return. However, it does not specify units, formatting, or whether some metrics might be absent, keeping it from a perfect score.
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 tool has zero parameters, confirmed by the empty input schema. The description correctly does not invent parameters, so the baseline score of 4 applies.
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 uses the verb 'Fetches' and specifies the resource ('athlete profile') plus distinct data categories (LTHR, LT Pace, VDOT, body metrics). This clearly differentiates it from sibling tools like get_hr_zones or get_pace_zones, which focus on narrower subsets, making its purpose unmistakable.
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 offers no guidance on when to use this tool versus alternatives like get_hr_zones, get_pace_zones, or calculate_vdot. There is no mention of exclusions, prerequisites, or preferred scenarios, leaving the agent to guess.
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 present, so the description carries the full burden of behavioral disclosure. It only lists the computed metrics and does not mention data requirements, assumptions, return format, or side effects. For a calculation tool this is a notable gap, though not misleading.
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 a single clear sentence, front-loaded with the key metric names and the action. Every word earns its place, and there is no redundancy or unnecessary detail.
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 no-parameter calculator with no output schema, the description names the three metrics but omits details like output format, interpretation, or underlying data needed. It is enough to select the tool but not fully self-contained for understanding results.
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 tool has zero parameters, so the schema is empty and schema coverage is 100%. Per rubric, 0 params gives a baseline of 4; the description does not need to explain parameter details because there are none.
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 uses a specific verb 'Calculates' and names the exact metrics (Acute:Chronic Workload Ratio, Monotony, Strain), distinguishing this from sibling tools like calculate_tid or calculate_vdot. It clearly states what the tool does.
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 the tool is used when ACWR, Monotony, or Strain are needed, but it does not explicitly state when to use this tool versus alternatives like calculate_tid or calculate_vdot. No exclusionary guidance is provided, so it is adequate but not explicit.
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?
No annotations are provided, so the description carries full burden. It specifies the computed metrics and units, but does not disclose assumptions (e.g., reliance on athlete profile data), return format, or edge-case behavior. It is a read-only calculation, but that is not stated.
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 concise sentence that front-loads the primary function and lists key outputs. 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?
For a simple calculation with one parameter and no output schema, the description provides the essential computed values. However, it omits context about required underlying data (e.g., athlete profile) and behavioral assumptions, which could be important for an agent.
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 sole parameter targetRaceHours is fully described in the schema with 'Target race duration in hours'. The description does not add additional semantics about the parameter beyond the calculation outcome.
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 uses a specific verb 'Calculates' and names the exact resources: Carb Loading (g/kg), fluid (mL/hr), sodium, and gel schedule. It clearly distinguishes this from sibling tools that calculate other metrics like vdot or acwr.
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 use for race nutrition planning but provides no explicit guidance on when to use it over alternatives or any excluding conditions. It is self-evident but not explicit.
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, the description carries the full burden of behavioral disclosure. It lists the metrics covered but does not state that the tool is read-only, indicate data sources, or describe any side effects. For a dashboard tool, the lack of explicit behavioral context is a notable gap.
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?
One concise sentence, front-loaded with the key concept and metrics. No wasted words or redundant details.
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?
The description lists the metrics summarized but does not explain what the output or summary looks like, especially given the absence of an output schema. For a simple tool with no parameters, this is adequate but could be more explicit about the return format or scope.
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 tool has zero parameters, which gives a baseline of 4. The description does not add parameter-specific information, but none is needed since the input schema is empty and fully covered.
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 it is a 'Unified Status Dashboard' that summarizes specific metrics (PMC, TID, ACWR, Readiness, Provider Status). This distinguishes it from sibling tools that calculate individual metrics, making its purpose unmistakable.
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 when an aggregated overview of multiple training metrics is needed, but it does not explicitly state when to choose this over individual metric tools. No exclusions or alternatives are mentioned, but the unified nature provides some contextual guidance.
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?
No annotations are provided, so the description carries the burden. It clearly indicates a read/analysis operation, but doesn't disclose return format, prerequisites (e.g., HR data), or error behavior. It's not misleading, but could be more transparent about what the analysis entails beyond the metric name.
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 a single sentence, front-loaded with the verb and key metric, and contains no extraneous information. Perfectly concise.
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?
With only one parameter and no output schema, the description provides enough to select the tool appropriately. It explains the input (specific activity stream) and the purpose (% aerobic drift). A slightly more complete description might mention the output format or data requirements, but for a narrow analysis tool this is adequate.
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 schema covers activityId with a minimal 'Activity ID' description (100% coverage). The tool description adds that it's the activity stream, which gives slight context, but doesn't add meaningful detail beyond the schema. Baseline 3 is appropriate.
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 specific action ('Analyzes cardiac decoupling') and the metric ('% aerobic drift'), scoped to 'a specific activity stream'. This distinguishes it from sibling analysis tools like analyze_threshold and get_hr_zones.
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 when to use it (when you need drift for a specific activity) but doesn't explicitly contrast it with alternatives or state exclusions. There's no 'when not to use' guidance, which is a minor gap given the number of sibling analysis tools.
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?
No annotations are provided, so the description carries the full burden. It correctly states the calculation and the three models, but does not disclose prerequisites like activity data availability, return format, or behavior when data is insufficient. Some behavioral context is present but limited.
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 the core purpose and immediately distinguishes the tool. There is no redundant information or unnecessary elaboration.
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 simple one-parameter schema and clear tool name, the description is mostly sufficient for an agent to select the tool. However, the lack of an output schema and any mention of return values means the agent must infer what the result looks like, leaving a small gap in 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?
The input schema fully documents the single 'days' parameter with its type, default, and description. The tool description does not add extra parameter-level context, but since schema coverage is 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb ('Calculates') and clearly identifies the resource ('3-zone Training Intensity Distribution'), distinguishing it from sibling tools like calculate_vdot by focusing on intensity distribution categories. The mention of Polarized 80/20 vs Pyramidal vs Threshold adds useful conceptual scope.
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 clearly implies when to use the tool: when a training intensity distribution calculation is needed. However, it does not explicitly state when not to use it or name alternatives such as calculate_vdot or analyze_threshold, so it lacks exclusions.
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?
No annotations are provided, so the description carries the full burden. It adds 'validity checking,' which suggests input validation behavior beyond a simple calculation, but it does not detail what validity means, what constraints apply, or how invalid inputs are handled.
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, front-loaded with the action and resource, and every phrase contributes meaning. No filler or repetition.
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 simple three-parameter computation with full schema coverage, the description provides the essential information: what it does and the modeling approach. It would benefit from stating the return value or caveats, but it is not inadequate.
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 input schema already covers 100% of the parameters with clear descriptions and examples. The description's mention of Riegel & Daniels models and validity checking is useful context but adds no parameter-specific semantics 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 opens with a specific verb ('Predicts') and a clear resource ('race finish time'), and distinguishes this tool from siblings by naming the Riegel & Daniels models. This leaves no ambiguity about the tool's core function.
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 clearly implies the use case: predicting finish times from a known race performance. However, it does not explicitly name alternatives like calculate_vdot or generate_race_plan, nor state when not to use it, so it lacks explicit exclusions.
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?
No annotations are provided, so the description must carry the safety/behavior burden. 'Lists all... registered in TrackLab' clearly indicates a read-only, non-destructive operation. It also discloses the scope and included references, which adds value beyond the bare tool name.
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 a single, well-structured sentence that states the action, resource, included references, and scope. Every word earns its place; there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, zero-parameter list tool, the description is complete: it tells the agent exactly what will be returned and what content areas are covered. No output schema exists, but the description sufficiently describes the return payload's nature.
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 tool has zero parameters, so schema coverage is trivially 100%. No parameter descriptions are needed; the baseline of 4 applies since the description fully explains what the tool returns without requiring parameter context.
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 uses a specific verb ('Lists') and a clear resource ('scientific paper & textbook references'), while enumerating the included sources (Daniels, Seiler, etc.). This clearly distinguishes it from sibling tools focused on training metrics and activities.
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?
No direct alternatives are mentioned, but the description makes it clear this is the tool for all registered literature references. The context signals (zero parameters, unique resource type) eliminate ambiguity about when to use it relative to siblings.
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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