Build validated football table and World Cup group prediction links with TablePredict.
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Average 4.4/5 across 3 of 3 tools scored.
Each tool maps to a distinct stage of the workflow: list available competitions, fetch canonical team names, and build the prediction link. There is no overlap or ambiguity between the tools.
All tools follow the same pattern: a single tablepredict_ prefix plus a snake_case verb_object name. list, get, and build are consistent, predictable, and easy to distinguish.
Three tools is well-scoped for this server's narrow purpose of generating TablePredict prediction links. Each tool is necessary and the set feels complete rather than padded.
The tool surface covers the full intended workflow: discover competitions, retrieve canonical team/group names, and validate a prediction link. The explicit lack of save/publish/account features appears intentional rather than a gap.
tablepredict_build_prediction_linkBuild a TablePredict linkAValidate a complete prediction and return a source-attributed TablePredict URL prefilled with that ranking. Domestic competitions require order; grouped competitions require every group. This tool does not save, publish, or create an account.
| Name | Required | Description | Default |
|---|---|---|---|
| order | No | Complete top-to-bottom club ranking for a domestic table competition. | |
| groups | No | Every group and its complete ranked team order for a grouped competition. | |
| season | No | Optional season check; must match the current catalog season. | |
| competition | Yes | Competition id, route slug, or display name. |
| Name | Required | Description |
|---|---|---|
| mode | Yes | |
| saved | Yes | |
| season | Yes | |
| source | Yes | |
| competitionId | Yes | |
| predictionUrl | Yes | |
| competitionName | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly explains that the tool validates rather than persists, and that it does not save, publish, or create an account. This complements the annotation readOnlyHint=true and idempotentHint=true, giving an agent a clear mental model of the tool's side effects and boundaries.
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?
Three short sentences convey purpose, key validation rules, and notable non-behaviors with no redundancy. The description is front-loaded with the core action and immediately covers the most impactful constraints.
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 presence of a clear input schema, an output schema, and annotations, the remaining gaps are small. The last sentence clarifies that no account or publishing side effect occurs, and the tool description covers the necessary condition split. It could improve by naming sibling tools to explicitly state when this tool is not needed.
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?
Although the schema already covers all parameter fields with descriptions, the description adds important relationship semantics: order is required for domestic competitions and every group is required for grouped competitions. This provides cross-parameter context beyond standalone schema descriptions.
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?
Description states a specific action: validate a complete prediction and return a source-attributed TablePredict URL prefilled with the ranking. It clearly distinguishes this from sibling tools by focusing on URL generation rather than retrieving teams or listing competitions.
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?
It provides useful conditional usage guidance: domestic competitions require order and grouped competitions require every group. However, it never explicitly discusses when to prefer this tool over the sibling tools or what prerequisite steps like fetching teams or competitions should happen first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tablepredict_get_competition_teamsGet competition rosterAReturn the exact canonical club names for domestic leagues or national-team names for grouped tournaments. Grouped competitions preserve their official group membership. Use an id, route slug, or display name from tablepredict_list_competitions.
| Name | Required | Description | Default |
|---|---|---|---|
| competition | Yes | Competition id, route slug, or display name. |
| Name | Required | Description |
|---|---|---|
| teams | No | |
| groups | No | |
| competition | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and idempotent behavior, so the description's additional context about canonical names and preserved official group membership adds useful behavioral nuance. It explains what kind of data is returned and how grouped tournaments behave.
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 sentences carry both the returned data semantics and usage source with no filler. It is compact, front-loaded, and every clause contributes 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?
With a single required parameter, full schema coverage, an output schema present, and strong annotations for read-only/idempotent behavior, the description is complete enough for an agent to select and invoke the tool confidently. Nothing essential is missing.
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 schema already describes the parameter well ('Competition id, route slug, or display name'), and the description reinforces that by specifying the valid identifier forms plus that they must come from tablepredict_list_competitions. This adds a useful origin constraint beyond the raw schema.
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 states a specific verb-and-resource pairing: 'Return the exact canonical club names for domestic leagues or national-team names for grouped tournaments.' It also differentiates from tablepredict_list_competitions by naming the return content (roster/team names) rather than the competition list.
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?
It explicitly tells the agent to use an 'id, route slug, or display name from tablepredict_list_competitions,' which gives a clear upstream dependency. It does not explicitly contrast with tablepredict_build_prediction_link, but the supported competition identifiers are sufficient for this get-style tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tablepredict_list_competitionsList TablePredict competitionsAList competitions currently configured in TablePredict, including season, prediction mode, roster size, cutoff, and predictor URL. Call this before requesting a roster or building a prediction link. Returns no user data.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| total | Yes | |
| competitions | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already carry readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond the schema, notably that it 'Returns no user data,' which sets expectations about scope and privacy. There is 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?
Two sentences deliver full value: the first states the operation and its content, the second provides usage ordering and a privacy-relevant return note. No filler or repeated title/name information.
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 parameterless, read-only listing tool with an output schema available, the description is complete. It names the returned fields, tells when to call it, and closes any ambiguity about user data. The output schema covers the response structure.
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 zero parameters and the schema shows no properties, so the baseline is 4. The description adds no specific parameter documentation because none is needed; it appropriately focuses on what data the call returns.
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 ('List') with a concrete resource ('competitions currently configured in TablePredict') and enumerates the exact fields returned (season, prediction mode, roster size, cutoff, predictor URL). It also situates the tool relative to sibling actions by saying to call it before requesting a roster or building a prediction link.
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 explicit guidance on when to call the tool: 'Call this before requesting a roster or building a prediction link.' It does not explicitly name the sibling alternatives or state when not to use them, but the sequence rule leaves little ambiguity.
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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