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jalateras

weather

by jalateras
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Python
Local

Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation2/5

    The two weather tools (get_alerts, get_forecast) are distinct, but the inclusion of run_shell_command is completely unrelated, making the toolset confusing. An agent might struggle to decide when to use the shell command versus the weather tools, and the server's purpose is unclear.

    Naming Consistency3/5

    The naming pattern is consistent (verb_noun) across all tools, but run_shell_command disrupts the domain-specific naming expected for a weather server. The pattern itself is fine, but the mismatch in domain reduces consistency.

    Tool Count2/5

    Three tools is reasonable for a simple weather server, but the presence of run_shell_command makes the count inappropriate because it does not fit the server's purpose. The toolset should focus solely on weather operations.

    Completeness2/5

    The weather tools cover alerts and forecasts, but miss common operations like current conditions or historical data. Moreover, the shell command is out of place and does not contribute to weather functionality, leaving a gap in the expected weather domain coverage.

  • Average 3.1/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries full burden. It mentions returning 'command output or error message' but omits critical security implications, side effects, permissions, or sandboxing. For a potentially dangerous tool, this is insufficient.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is very concise with three short sentences. However, it sacrifices critical details for brevity. It is front-loaded with purpose but misses essential usage information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity and potential risks of executing shell commands, the description is grossly incomplete. It lacks information on return format, error handling, security, timeouts, and permissions. No output schema or annotations exist to 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 'command' is described as 'Shell command to execute,' which adds almost no value beyond the schema property name. With 0% schema description coverage, the description should provide more detail (e.g., format, restrictions).

    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 runs a shell command and returns output. It uses a specific verb+resource structure and easily distinguishes from sibling tools like get_alerts and get_forecast.

    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. It does not provide any context for when executing a shell command is appropriate or mention any 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?

    With no annotations, the description should disclose behavioral traits. It only mentions returning a forecast or error message, but omits details like data source, update frequency, or rate limits. The inherent read-only nature is reasonable, but more context would help.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is short but includes an Args section that is redundant with the input schema. It could be more concise by omitting the Args section, as the schema already defines parameters. Still, it is not overly verbose.

    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 (2 params, no output schema), the description covers the basics: what it does, input coordinates, and output type. However, it lacks completeness on error conditions, coordinate limitations, or any output formatting hints beyond 'formatted forecast'.

    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 repeats parameter names with minimal description (e.g., 'Latitude coordinate'). It lacks details like valid ranges (-90 to 90), decimal degree format, or that longitude should be between -180 and 180.

    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 fetches a weather forecast for given coordinates. The verb 'get' and resource 'weather forecast' are specific, and it distinguishes from siblings like 'get_alerts' and 'run_shell_command'.

    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 over alternatives (e.g., 'get_alerts' for warnings). The description lacks context for decision-making, such as noting that coordinates should be valid or that this tool is for general forecasts.

    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 returns 'Formatted alerts or error message' but does not disclose behavioral traits such as read-only nature, rate limits, authentication needs, or error conditions. Minimal transparency.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise with a clear structure: purpose line, args section, returns section. It is front-loaded and each sentence serves a purpose, though it omits some useful context.

    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 one-parameter tool with no output schema or annotations, the description is adequate. It explains the input format and basic return, but lacks details on what 'active' means, possible multiple alerts, formatting specifics, and error scenarios.

    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 schema only defines 'state' as a string with a title. The description adds crucial detail: 'Two-letter state code (e.g., 'CA', 'NY')', clarifying format and providing examples. Since schema description coverage is 0%, the description compensates effectively.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb 'Get', the resource 'active weather alerts', and the scope 'for a state'. It effectively distinguishes from siblings: 'get_forecast' is for forecasts, 'run_shell_command' is unrelated.

    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 retrieving weather alerts by state but does not explicitly state when to use it versus alternatives or provide exclusion criteria. No when-not or prerequisite information is given.

    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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