sports-mcp-server

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

Annotations already declare readOnlyHint=true and openWorldHint=true, establishing the safety profile. The description adds meaningful detail about the return content (final scores, teams, game times, standings summaries), which goes beyond the annotations. No contradictions.

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

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

The description is two sentences, front-loaded with the primary function, and contains no filler or repetition. Every word adds value.

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

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

Given this is a simple read-only aggregation tool with no parameters or output schema, the description fully covers its purpose, output contents, and intended use cases. It is complete and self-contained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the schema is trivially complete. The description does not need to explain parameters; the baseline for 0-parameter tools is 4.

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

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

The description clearly states what the tool does: retrieves combined scores from NBA, NFL, and NHL in a single call. It also differentiates from sibling tools (which are league-specific) by explicitly covering all three leagues.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear use case: comprehensive sports news monitoring or multi-sport fantasy management. It does not explicitly mention when NOT to use it or name alternatives, but the context of a single aggregated call implies that for single-league needs, the specific sibling tools would be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, and the description's 'Retrieve' aligns with these. It adds the 'today's' time scoping constraint, which is useful behavioral context, but does not disclose other traits like data freshness limits or timezone handling. No contradiction with annotations.

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

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

The description is three sentences with a clear structure: purpose, return contents, and use cases. Every sentence earns its place, and there is no redundant fluff. It is front-loaded with the primary action.

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

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

Given the tool has no parameters and no output schema, the description adequately covers the return values (team names, scores, game time, win-loss records, player stats). This is sufficient for a simple read-only retrieval tool with good annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so the schema fully covers this aspect. The description does not need to explain parameters; it adds value by detailing the return fields, which is more relevant for the agent's output expectations.

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

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

The description clearly states the tool retrieves 'today's NBA basketball game scores, schedules, and results' with the verb 'Retrieve' and a specific resource. It also lists the return content (team names, final scores, etc.), making it distinct from sibling NFL/NHL tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear use cases ('sports betting research, fantasy basketball, or staying updated on daily NBA action'), providing context for when to use it. However, it does not explicitly contrast with sibling tools like get_nfl_scores or get_all_scores, so it lacks direct exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations include readOnlyHint=true, and the description reinforces this with 'Fetch' and lists detailed return content (matchups, scores, start times, standings, player stats). This adds context beyond annotations by describing what data is returned. No mention of limitations, but baseline is well covered.

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

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

The description is two sentences, front-loaded with the action and resource. Every clause adds relevant information: what is fetched, what is returned, and intended use cases. No redundancy.

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

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

For a simple read-only tool with no parameters and no output schema, the description is quite complete. It covers input (none needed), behavior (fetch current data), output (specific data elements), and use cases. Minor gaps like timezone or update frequency are not critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, so schema coverage is 100%. Per the rubric, a baseline of 4 is appropriate. The description adds value by explaining what will be returned, compensating for the lack of parameter details.

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

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

The description uses a specific verb 'Fetch' and clearly identifies the resource: NFL football game scores, schedules, and results. It distinguishes itself from sibling tools by focusing explicitly on NFL, unlike NBA and NHL siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear usage context: 'Use for fantasy football, sports analysis, or following NFL season progress.' While it doesn't explicitly mention when not to use or name alternatives, the NFL-specific scope implies separation from NBA/NHL tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds value by specifying the date scope ('today's') and listing the types of data returned, which goes beyond the structured annotations.

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

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

Two concise sentences, front-loaded with the primary function. No redundant words; every sentence adds value by stating the purpose, output scope, and use cases.

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

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

For a zero-parameter read-only tool, this description covers the purpose, output details, and use cases. The lack of an output schema makes the description's list of return values important, and it is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters, the schema provides complete coverage. The description adds meaningful context about the output (team names, scores, standings, stats), which helps the agent understand what data will be returned even without parameters.

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

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

The description clearly states 'Get today's NHL hockey game scores, schedules, and match results' with a specific verb and resource. It explicitly names NHL, distinguishing it from sibling tools for NBA, NFL, and all scores.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear use cases ('hockey fan updates, fantasy league management, sports betting research') that indicate when to use the tool. It does not explicitly mention alternatives, but the tool name and sibling list make the scope evident.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.