AI Race Engineer
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation4/5
The tools are largely distinct: session loading, stint data, degradation modeling, and multi-driver comparison. The only potential overlap is between get_degradation and compare_drivers, but the single-vs-multi-driver distinction is clear from descriptions.
Naming Consistency4/5Three tools follow the 'get_' verb-noun pattern, while compare_drivers uses a different verb without 'get'. This is a minor deviation, but the names remain predictable and readable overall.
Tool Count4/5With 4 tools, the server is well-scoped for its tyre-focused purpose. It covers the essential workflow without bloat, though it is on the smaller side, leaving room for additional specialized tools.
Completeness4/5The server covers the full tyre degradation analysis lifecycle: session loading, stint retrieval, degradation fitting, and cross-driver comparison. Minor gaps like raw lap times or session context tools exist, but they are not critical for the stated purpose.
Average 3.5/5 across 4 of 4 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 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?
With no annotations, the description carries the full burden of disclosing behavior. It mentions the output content but does not state that the operation is read-only, describe the return structure, or mention any defaults, errors, 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: one sentence states the tool's purpose, followed by a minimal but useful parameter note. There is no redundant information or wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and incomplete parameter documentation, the description is insufficient for an agent to fully understand the tool's behavior and return values. It covers the core concept but omits important usage context and parameter details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the description only adds meaning for the 'driver' parameter (three-letter code). The required 'year' and 'circuit' parameters and the optional 'session_type' remain undocumented, leaving significant ambiguities.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the resource (driver stints) and the specific data provided (compound, lap range, length). It scopes the tool to one driver, which helps distinguish it from compare_drivers, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus siblings like get_degradation or compare_drivers. The only context is 'for one driver,' which is a scope constraint but not a usage directive or exclusion.
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 to indicate read-only or side-effect behavior, the description carries the full burden of transparency. It fails to mention whether this is a safe read operation, what the output format is (e.g., table, chart), or any assumptions about data availability (e.g., requires race weekend). The description only states the comparison intent without disclosing 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, leading with a clear purpose sentence and then providing a parameter example. It is well-structured and front-loaded. However, it omits documentation for three of four parameters, making it efficient but incomplete; still, what is written earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 parameters, no output schema, and no annotations, so the description must provide substantial context. It only covers one parameter, ignores required inputs like year and circuit, and does not explain the expected output or how this tool relates to sibling functions. This is insufficient for an agent to confidently invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does 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 only documents the 'drivers' parameter with an example format ('Three-letter codes'), but entirely omits 'year', 'circuit', and 'session_type' (which has a default). This leaves most parameters unexplained, providing minimal added meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('comparison') and resource ('stint-by-stint degradation across several drivers'), making it distinct from related tools like get_driver_stints (which likely returns raw stint data) and get_degradation (which may focus on a single driver). This purpose is immediately understandable and differentiates the tool well.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when one needs to compare degradation across multiple drivers, but it does not explicitly state when to prefer this tool over siblings such as get_degradation or get_driver_stints. No exclusions or alternative recommendations are provided, leaving usage context to be inferred.
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 the full burden of behavioral disclosure. It transparently explains that lap times are cleaned (safety car, VSC, in/out, inaccurate laps removed) and fuel-corrected before fitting. It also warns about the R² and trusted flag, including the caveat that a low-R² fit is noise and should not drive decisions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: the opening sentence states the core purpose, the second details the data processing pipeline, and the third explains output interpretation and the trusted flag. Every sentence contributes meaningful information without filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Even though there is no output schema, the description explains key output elements (R², trusted flag) and provides important context about data cleaning and fit interpretation. It is adequate for a moderately complex data retrieval tool, but it lacks parameter-level guidance and any mention of return format or pagination. Given the lack of output schema, the description still covers the essential behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description needs to compensate by explaining parameters. It does not explicitly define any of the four parameters (year, driver, circuit, session_type), though the names are self-explanatory. The description only implicitly ties 'one driver' to the driver parameter, and it fails to clarify session_type's default meaning or values. Thus it adds little semantic value beyond the schema field names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool returns 'Fitted tyre degradation per stint for one driver,' clearly defining the resource and scope. It distinguishes itself from siblings like get_driver_stints and compare_drivers by focusing on fitted degradation rather than raw stint data or comparisons. It lacks an explicit verb like 'calculates,' but the noun phrase is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for per-stint degradation analysis for a single driver, which helps separate it from comparison tools. However, it does not explicitly state when to use this tool versus siblings (e.g., get_driver_stints for raw stints or compare_drivers for multi-driver comparisons). No explicit exclusions or alternative guidance is given, only an implied scope.
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 the burden of behavioral disclosure. It explains the operation (loads), the output (metadata and driver list), and a key data constraint (FastF1 coverage starts in 2018). It does not detail failure modes or exact metadata fields, but it is not misleading and provides useful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise: a single purpose sentence followed by a compact bulleted list of parameter definitions. It is front-loaded with the main function and contains no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple session-information loader, the description covers purpose, all parameters, and output at a high level. Without an output schema, saying 'metadata and driver list' is slightly vague, but it is likely enough for an agent to select and invoke the tool successfully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only parameter names, types, and defaults with 0% description coverage. The description compensates fully by explaining each parameter: year with a minimum value, circuit with examples, and session_type with a list of valid values. This gives the agent everything needed to construct valid arguments.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Load an F1 session and return its metadata and driver list.' This uses a specific verb and resource, and it distinguishes itself from sibling tools like get_driver_stints and get_degradation, which focus on other aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool by explaining it loads session metadata/driver list and supplying parameter constraints (e.g., 2018 or later, valid session types). However, it does not explicitly mention alternative tools or when not to use this tool.
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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