get_predicted_lineupsUse this for predicted Premier League starting XIs and team news for the next FPL gameweek (who starts, who is injured or rotated), from FPL Copilot's expected minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| teams | No | Clubs, e.g. ['ARS', 'Chelsea'] (default: all 20) |
Changes observed during successful MCP inspections.
Input schema / properties / teams / anyOfPrevious value: -[
- {
- "items": {
- "maxLength": 30,
- "minLength": 1,
- "type": "string"
- },
- "maxItems": 20,
- "type": "array"
- },
- {
- "type": "null"
- }
-]New value: +[
+ {
+ "items": {
+ "maxLength": 30,
+ "minLength": 1,
+ "type": "string"
+ },
+ "maxItems": 40,
+ "type": "array"
+ },
+ {
+ "type": "null"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful context beyond that: the data source (FPL Copilot's expected minutes) and the temporal limitation (next gameweek only), which an agent needs to judge staleness.
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?
One dense sentence with no filler; the use case is front-loaded before the supporting detail about content and source.
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 single-optional-param read tool with full annotation coverage, the definition tells the agent when to call it and what data underlies the result. The lack of an output schema means the response shape is slightly unspecified, but the description's content summary largely compensates.
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 description coverage is 100%: the single 'teams' parameter already documents the example format and the default of all 20 clubs. The description adds nothing about the filter parameter, so the baseline 3 applies.
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?
Names a specific resource (predicted Premier League starting XIs and team news) and scope (next FPL gameweek), with parenthetical detail on what it actually reveals — starters, injuries, rotation. No sibling tool covers lineups, so no differentiation is needed and an agent can route to it unambiguously.
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?
Opens with an explicit 'Use this for...' trigger naming the exact scenario (predicted XIs/team news for the next gameweek). It stops short of naming alternatives or when-not-to-use conditions among the many player/team siblings, so it's clear context without exclusions.
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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