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cricket_differential_picks

Read-onlyIdempotent

Identify low-ownership cricket players with positive projected upside for fantasy picks. Uses estimated ownership based on credit weight to find differential selections.

Instructions

Suggest low-ownership picks with positive projected upside.

Ownership is estimated — proxied by credit weight (lower-credit players tend to have lower ownership), not real ownership data. Flagged estimated: true in the response.

Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name. Required if match_id is absent. team_b: Second team code/name. Required if match_id is absent. venue: Venue key/name. Required if match_id is absent. ownership_threshold: percent ownership cap; affects estimated label.

Returns: data.picks: list of {name, role, team, credits, projected_points, estimated_ownership_pct}. meta.source: model:captain_score (filtered). meta.estimated: true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
venueNoVenue key/name. Required if match_id is absent.
team_aNoFirst team code/name. Required if match_id is absent.
team_bNoSecond team code/name. Required if match_id is absent.
match_idNoCricAPI match identifier; resolves team_a/team_b/venue automatically.
ownership_thresholdNopercent ownership cap; affects estimated label.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
metaNo
errorNo
Behavior5/5

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

Annotations already mark readOnlyHint true, but the description adds critical behavioral context: ownership is estimated via credit weight, not real data, and is flagged as 'estimated: true' in the response. This goes beyond what annotations provide.

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 well-structured with a summary line, estimation note, and parameter docs. It is concise and front-loaded, though it could be slightly tighter by removing duplicate parameter descriptions.

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 complexity (5 parameters, estimation nuance, output schema), the description covers purpose, usage, behavioral details, parameters, and return structure comprehensively. It is fully adequate for an agent to use correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description's Args section mostly repeats schema descriptions with minor additional context (e.g., ownership_threshold affecting estimated label). No significant added meaning.

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 'Suggest low-ownership picks with positive projected upside,' which is a clear verb+resource combination. It is distinct from sibling tools like cricket_captain_recommendation and cricket_find_value_bets.

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 states the tool is for differential picks and clarifies the ownership estimation methodology. It does not explicitly list when not to use it or directly compare to siblings, but the context is clear enough for an agent to decide.

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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