jk-mcp-epl

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have clearly distinct purposes, but get_roster and get_players both return player information and get_scoreboard vs get_team_schedule could be confused for team-specific date queries. Descriptions adequately differentiate them, but a few boundaries are close.
Naming Consistency5/5
All tools follow a consistent get_<noun> pattern in snake_case. Multi-word nouns are used consistently, making the API predictable and easy to navigate.
Tool Count5/5
12 tools is well-scoped for a Premier League data server, covering team, match, player, news, standings, and advanced analytics without redundancy. Each tool earns its place.
Completeness4/5
The tool set covers core domains well: teams, matches, rosters, players, news, standings, and analytics. Minor gaps exist like individual player stats or detailed match events beyond key events, but these are workable.
Average 4.5/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on August 5, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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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.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context by specifying that returned matches include scheduled, in-progress, and completed ones, with opponent, date, score, and status. However, it does not mention potential pagination limits or error behavior.
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 two sentences plus an Args block. The main action is front-loaded, and every sentence adds meaningful information without fluff. The structure is clean and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, output schema exists), the description is nearly complete. It states the scope (current season), defines the parameter, and lists the return fields. It could mention how to handle other seasons or invalid team IDs, but these are minor gaps for a low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines team_id as a string with 0% description coverage. The description compensates fully by explaining 'ESPN numeric team ID' and providing a concrete example ('18418' for Atlanta United FC). This is exactly the kind of parameter clarity agents need.
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: 'Get all matches for a single Premier League team in the current season.' This specific verb+resource combination distinguishes it from siblings like get_scoreboard (league-wide) and get_match_details (specific match).
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 the tool (when you need a team's schedule) but does not explicitly discuss alternatives or exclusions. Sibling tools exist for other types of match data, but the description alone offers no direct comparison.
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?
Annotations already declare readOnly=True, destructive=False, and idempotent=True, so the safety profile is clear. The description adds behavioral context by explaining the adjustment formula (avg_opponent_ppg / league_average_ppg) and how to interpret values relative to raw PPG. This goes beyond what annotations provide and helps the agent reason about the computation.
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 front-loaded with the main purpose, followed by a compact formula explanation and an arguments line. Every sentence adds value, and the structure is clean. The 'Args' block is redundant with the schema but serves as a useful inline reminder given that the schema lacks descriptions.
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?
The description explains what the tool returns (raw and adjusted PPG) and the reasoning behind the adjustment. With an output schema present, return values do not need to be spelled out. It does not cover error conditions or rate limits, but for a simple read-only tool this is adequate. The description is complete enough for an agent to invoke it 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 input schema only defines team_id as a string with no description (0% coverage). The description compensates by stating 'team_id: ESPN numeric team ID', which clarifies the expected format and meaning. This is sufficient for the single parameter, though it could also mention that the team must exist in ESPN systems.
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+resource ('Get a team's raw points-per-game alongside an opponent-quality-adjusted PPG') and clearly distinguishes this from sibling tools like get_standings or get_strength_of_schedule. It names the exact metric and includes a concise definition.
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 implies the primary use case: retrieving a team's PPG with an adjustment for opponent quality. It does not explicitly discuss exclusions or alternatives, but the formula explanation gives context for when this metric is relevant. A clear alternative comparison is missing, so it does not fully reach a 5.
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?
Beyond annotations (readOnly, idempotent, etc.), the description explains that tiers are derived from live standings and imposes constraints on tier_size. This adds useful behavioral context without contradicting the annotations.
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 clearly structured with a front-loaded purpose, a concise explanation of tiers, and a bullet-like Args list. It is slightly verbose but every sentence adds value, making it efficient without being bloated.
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?
The tool has an output schema, so return values don't need to be in the description. The description covers the core logic, parameter constraints, and usage rationale. It is complete enough for correct invocation, though it could mention edge cases like league size smaller than 2*tier_size.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description's Args section fully compensates: it defines team_id as ESPN numeric ID and tier_size with default, minimum, and constraint relative to league size. This adds rich meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action: 'Get a team's W-L-T splits against current top-tier, middle, and bottom-tier teams.' This distinctively identifies the tool's resource (team results by opponent tier) and differentiates it from siblings like get_standings or get_strength_of_schedule.
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?
Provides a concrete use case: 'how does this team do against the top of the table?' and explains the tier derivation from live standings. However, it does not explicitly mention when not to use the tool or suggest alternative tools, so it falls short of full guidance.
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?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context by specifying the exact content returned (score, venue, attendance, chronological events) and explaining that match_id is an ESPN numeric event ID. This goes beyond the annotations without contradicting them.
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 concise and well-structured: it leads with the purpose, then lists return content, then provides usage guidance, and finally explains the parameter format. Every sentence adds value and there is no redundant wording.
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?
Given the single parameter, rich annotations, and the presence of an output schema, the description is complete enough. It covers the tool's function, required input format, and how to obtain the match ID. There are no significant gaps for an agent 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 input schema only defines match_id as a string with no description. The description compensates by clarifying that it is an ESPN numeric event ID, providing an example ('401853883'). This gives the agent actionable guidance for supplying the parameter correctly.
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 'Get detailed information for a single Premier League match' and enumerates the specific return fields (score, venue, attendance, key events). This distinguishes it from sibling tools like get_scoreboard or get_team_schedule, which provide broader match lists, by focusing on a single match's details.
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 gives clear context that this tool is for single-match details and instructs the agent to obtain the match_id from get_scoreboard or get_team_schedule. While it does not explicitly state when not to use it, the prerequisite and scope provide sufficient guidance for tool selection.
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?
Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds useful context by specifying the return fields and clarifying that it returns the active roster only. It does not cover edge cases like invalid team IDs, but that is acceptable for a simple read 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 compact and front-loaded: purpose in the first sentence, return fields in the second, prerequisite in the third, and a single-parameter Args block. There is no superfluous content.
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 one-parameter read tool with an output schema and strong annotations, the description covers purpose, return contents, parameter semantics, and prerequisite. It is complete enough for an agent to select and 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?
Schema description coverage is 0%, so the description compensates with an Args section explaining team_id as an ESPN numeric team ID with an example. However, the example ('18418' for Atlanta United FC) conflicts with the stated Premier League context, making it slightly misleading.
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 and resource: 'Get the active roster for an Premier League team.' It then enumerates the returned fields (jersey number, name, position, citizenship, age), clearly distinguishing it from sibling team-related tools like get_team or get_team_schedule.
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?
It gives a clear prerequisite: use the team ID returned by get_teams. However, it does not explicitly state when not to use this tool or compare it to alternatives such as get_players, so it stops short of a 5.
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?
Annotations already flag read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context: the inclusive date range semantics, the dependency of end_date on date, and the default behavior without arguments. It does not contradict annotations and enriches the understanding of the tool's behavior.
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 compact and well-structured. The first sentence states the core purpose, followed by concise paragraphs explaining behavior and an Args list. Each sentence carries meaningful information without redundancy. The Args section repeats some prose but in a scannable format, which is acceptable.
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 two-parameter design, strong annotations, and presence of an output schema, the description covers the essential invocation semantics thoroughly. It explains all argument modes and the date format. It does not discuss return structure, but that is complemented by the output schema. A small gap is the lack of explicit cross-referencing to sibling tools, hence a 4 rather than 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides param names and types but no descriptions (0% schema coverage). The description fully compensates by explaining the format (YYYYMMDD), optionality, the relationship between date and end_date, and the resulting behavior for each combination. This is essential information for correct invocation.
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 clear, specific statement: 'Get Premier League match scores and status for a date or date range.' It uses an action verb, identifies the resource (match scores/status), and specifies the scope. The subsequent sentences clarify behavior for different argument combinations, distinguishing it from sibling tools like get_teams or get_standings.
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 provides clear context on how to invoke the tool with no arguments, with date only, and with both date and end_date. It does not explicitly name alternatives or say when not to use this tool, but the detailed argument behavior serves as practical guidance. A brief mention of get_match_details for single-match queries would have elevated it to 5.
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?
The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable context about the return shape (eight-team table, ordered by points, with specific statistics) and notes 'current', indicating dynamic data that aligns with openWorldHint. No contradictions 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the primary action and then detailing the return format without redundancy. Every word contributes to clarity, making it appropriately concise.
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?
Given the simple tool (no params, rich annotations, and an output schema), the description covers the purpose and output specifics, making it complete for an agent to invoke correctly. The output schema would handle return structure details, so the description need not repeat them.
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%. The description does not add parameter-specific details (as none exist), but it enriches the overall understanding of the tool's output, which is sufficient given the baseline for no-parameter tools.
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 'Get the current Premier League standings' with a specific verb and resource, and distinguishes itself from sibling tools like get_teams and get_scoreboard by focusing on standings data. It also specifies the eight-team table format, adding clarity.
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 provides clear context for when to use this tool: to retrieve current standings. It does not explicitly mention alternatives or exclusions, but the specificity makes its intended use obvious. A slight improvement would be naming contrasting tools, but it's not necessary here.
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?
Annotations already cover safety (read-only, non-destructive). The description adds value by revealing the return contents (headline, date, summary, ESPN link) and mentions the 'recent' filter, which aligns with openWorldHint. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the purpose, and every sentence serves a purpose. The Args block is cleanly structured and directly tied to the schema.
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 tool with one optional parameter and an output schema, the description is complete. It provides enough detail for an agent to select and invoke the tool correctly, without unnecessary extras.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite the schema having zero description coverage, the tool description explicitly documents the 'limit' parameter with its default value and meaning, fully compensating for the schema's lack of detail.
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: 'Get recent Premier League news articles.' It uses a specific verb and resource, and no sibling tool covers news, so it is fully distinguished.
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 context is clear: this is the only news-related tool among siblings, so usage is unambiguous. However, there is no explicit mention of when not to use it or alternatives, slightly lowering the score.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read operation. The description adds meaningful behavioral context by specifying that the calculation uses 'opponents already faced' in 'completed matches' and returns 'average current points-per-game' plus a 'per-opponent breakdown,' which is not evident from annotations alone.
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 concise and well-structured: the first line states the core purpose, the second explains the return value, the third gives a usage scenario, and the final line documents the argument. Every sentence adds value, and it avoids extraneous detail or repetition of the schema.
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?
The description provides sufficient context for a one-parameter read-only tool: it explains the computation logic, the return format (average + breakdown), and an example use case. Since the output schema exists, it need not detail return fields, but it does enough to fully orient an agent selecting this tool among siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only the parameter name and type (team_id as string), with 0% schema description coverage. The description fully compensates by explaining what team_id means ('ESPN numeric team ID') and giving a concrete example ('e.g. "18418" for Atlanta United FC'), making the parameter's format and usage clear.
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 starts with a specific verb+resource: 'Get a team's strength of schedule' and clearly defines the computation basis: 'based on opponents already faced.' It distinguishes itself from sibling tools like get_team_schedule by focusing on strength of schedule rather than listing fixtures, and clarifies the output: average points-per-game plus a per-opponent breakdown.
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 provides context for when to use the tool: 'Useful early in the season for "who has played the tougher schedule so far?" questions.' It does not explicitly name alternatives or exclusion conditions, but the framing implies this tool is for schedule-difficulty analysis rather than raw schedule or match detail retrieval.
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?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful context: that it returns full team information and that the ID originates from get_teams, which is a cross-tool dependency. No contradictions with annotations exist.
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 concise and well-structured: a clear one-sentence purpose, a one-sentence return summary, and a compact Args block. Every sentence adds value with no redundancy or fluff.
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 single-parameter read-only tool with an output schema available, the description is complete. It states the purpose, input source, and key return fields. Sibling tool names provide sufficient surrounding context, and the description covers all necessary operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description fully compensates by explaining that team_id is an ESPN numeric team ID and providing a concrete example. It also instructs how to obtain the ID (from get_teams), making the single parameter's meaning and source completely clear.
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 purpose: getting details for a specific Premier League team. It lists the returned fields (display name, abbreviation, location) and explicitly distinguishes itself from the sibling get_teams by requiring a numeric team ID, making the scope and resource unambiguous.
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 provides clear context by instructing to use the numeric ID returned by get_teams, implying the complementary relationship between the two tools. It does not explicitly list when-not-to-use scenarios, but the purpose and pointer to get_teams offer sufficient practical guidance.
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by specifying the return format (numbered list with ID, full name, abbreviation, home city) and the 'active' filter. It does not contradict annotations 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two sentences, front-loaded with the core purpose, and every sentence contributes meaning. It is an excellent example of minimal yet complete writing.
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 zero-parameter, read-only list tool with an output schema, the description fully covers what the tool returns and how it fits with sibling tools. The output format is explicitly described, making it complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so the description bears no burden for explaining parameters. The schema coverage is trivially 100% and thus baseline for zero parameters is 4, which 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 tool's purpose: 'Get all active Premier League teams.' It uses a specific verb and resource, and the focus on active teams distinguishes it from sibling tools like get_standings or get_team.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use this tool and what to do next: 'Use the ID or abbreviation with get_team to retrieve detailed information about a specific team.' This clearly points to the alternative for more detail.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds critical behavioral details beyond that: season_year is ignored, there is a default to the most recent completed season with a caveat about transfer windows, and limit has a max of 500. These insights are not derivable from annotations or schema and help the agent anticipate response behavior. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: it opens with the core purpose, then enriches with data quality details, adds a season-caveat note, and ends with a clear Args section. Every sentence contributes meaningful information without redundancy or fluff.
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?
Given the tool's simplicity (two optional params, no nested objects) and the presence of an output schema, the description covers all necessary context: purpose, data source, key behavioral caveats, and parameter semantics. It is complete for an agent to select and invoke the tool correctly without further clarification.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates. It explicitly states that season_year is ignored and reserved for future use, and that limit has a default of 50 and a maximum of 500, explaining the parameter semantics that the schema (types and defaults) does not convey.
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 'Get Premier League player registrations from the Pulselive/Opta feed,' specifying the verb, resource, and data source. It differentiates itself from sibling tools like get_roster by emphasizing the richer data (Opta player id, detailed positional labels, loan flag, etc.), 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides useful context: it defaults to the most recent completed season, warns that the current season may be incomplete until the transfer window closes, and notes that season_year is currently ignored. However, it does not explicitly contrast with alternatives like get_roster beyond a general comparison, nor does it state when not to use this tool in favor of another.
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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