TablePredict

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing the non-mutating safety profile. The description adds value by explicitly stating it 'does not save, publish, or create an account,' which reinforces the read-only nature. It doesn't add details about validation errors, URL format, or rate limits beyond what's given, so not exceeding a 3.

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 tight sentences, zero filler. Each sentence earns its place: what it does, the structural requirements, and the explicit non-actions. Highly front-loaded and efficient.

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?

The tool has an output schema and rich annotations (readOnly, idempotent, non-destructive), and the description covers the critical conditional logic that distinguishes domestic vs grouped competitions. Four parameters at 100% schema coverage, plus prerequisites for each ranking type, make this largely complete. Slight gap: no mention of what happens with invalid input (validation failure behavior), but the schema's required fields plus description cover most needs.

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?

Schema coverage is 100%, so the description isn't required to explain each parameter. The description adds the conditional logic that order applies to domestic competitions and groups to grouped competitions, which supplements the schema's per-parameter descriptions usefully. However, season's semantics ('must match the current catalog season') are already partly in the schema, so no major additional value beyond baseline.

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 function: 'Validate a complete prediction and return a source-attributed TablePredict URL prefilled with that ranking.' This is a specific verb+resource combination that distinguishes it from siblings (get teams, list competitions). However, 'prefilled with that ranking' is slightly abstract and doesn't fully clarify what 'prediction' means in this context, keeping it from a 5.

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 context on requirements ('Domestic competitions require order; grouped competitions require every group') and explicitly states what the tool does NOT do ('does not save, publish, or create an account'). However, it doesn't name specific alternative tools or give when-not-to-use guidance relative to siblings, leaving the usage guidance somewhat implicit.

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, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable context about grouped competitions preserving official group membership, which is behavioral information beyond what annotations reveal.

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 tight sentences, zero filler. Each sentence earns its place: what it returns, the grouping nuance, and valid input sources.

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?

Well-covered given the annotations and output schema presence. The tool is a straightforward lookup with one parameter; the description covers purpose, input sources, and a behavioral nuance. Lacks explicit details on return format, but the output schema likely handles that.

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?

Schema coverage is 100% with the single 'competition' parameter well-described (id, route slug, or display name). The description adds value by clarifying the accepted input types align with what list_competitions returns, reinforcing which identifiers work.

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?

Clear verb+resource+outcome: 'Return exact canonical team names' specifically for building a TablePredict link. It explains what makes this tool distinct (canonical names, official group membership preservation), which separates it from the sibling list_competitions tool.

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?

Explicitly states its purpose is to obtain teams for building links and references the sibling tool tablepredict_list_competitions as the source of valid inputs. It doesn't explicitly say when NOT to use it, but the linkage to list_competitions provides good contextual guidance.

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, idempotentHint=true, destructiveHint=false, and openWorldHint=false. The description adds the useful note 'Returns no user data,' giving privacy context beyond the structured fields. This adds genuine value without contradicting 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?

A tight two-sentence description. Every sentence earns its place: the first enumerates output fields and the second provides sequencing guidance plus a privacy note. Zero waste.

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, strong annotations covering the safety profile, and an output schema present (so return value format is documented), the description is complete. The sequencing note and 'Returns no user data' add sufficient behavioral context.

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 schema coverage is 100% (vacuously). With 0 params, baseline is 4. The description lists the exact data fields returned, which describes the output rather than params but adds meaningful context about the payload.

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?

Clear verb+resource: 'List competitions'. Enumerates the specific fields returned (season, prediction mode, team count, cutoff, predictor URL), which is specific and distinguishes meaningfully. However, it doesn't explicitly differentiate from siblings, though the resource is distinct enough.

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?

Gives explicit when-to-call guidance: 'Call this before requesting teams or building a prediction link.' This orients the agent to the sequencing relationship with sibling tools. Lacks explicit when-not-to-use or alternative considerations, but the ordering directive is clear context.

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