gamesFetch AFL fixture and results from Squiggle, filtered by year, round, and/or completion status; returns scores, venues, dates, and match IDs.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| round | No | ||
| complete | No | Filter to completed games. |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "round": 1,
+ "year": 2024
+ },
+ {
+ "complete": true,
+ "round": 5,
+ "year": 2024
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "items": {
+ "properties": {
+ "away_score": {
+ "description": "Away team score",
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "away_team": {
+ "description": "Away team name",
+ "type": "string"
+ },
+ "complete": {
+ "description": "Whether game is completed",
+ "type": "boolean"
+ },
+ "date": {
+ "description": "Game date and time",
+ "type": "string"
+ },
+ "home_score": {
+ "description": "Home team score",
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "home_team": {
+ "description": "Home team name",
+ "type": "string"
+ },
+ "id": {
+ "description": "Game ID",
+ "type": "number"
+ },
+ "round": {
+ "description": "Round number",
+ "type": "number"
+ },
+ "year": {
+ "description": "Season year",
+ "type": "number"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive behavior. The description adds useful context beyond annotations by naming the data source (Squiggle) and the return fields (scores, venues, dates, match IDs), enriching understanding of what the tool provides.
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?
The description is a single, well-structured sentence that front-loads the action and includes all essential information without redundancy. Every word contributes to understanding the tool's function.
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 simple filtered query tool with an output schema and comprehensive annotations, the description is complete. It covers purpose, filters, return fields, and source, leaving no critical gaps. The output schema handles return format details.
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 only 33% (only 'complete' is described). The description mentions all three filters (year, round, completion status) but adds little detail beyond the property names. It confirms their role as filters but does not compensate for the lack of schema descriptions for 'year' and 'round'.
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 tool's action ('Fetch AFL fixture and results from Squiggle') and scope, including filters and return fields. This distinguishes it from sibling tools like ladder, standings, teams, and tips, which serve different data needs.
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 implies when to use the tool (when needing AFL fixtures/results by year, round, completion status) and specifies key filters. However, it does not explicitly mention alternatives or exclusions, though the sibling list provides context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools occupy overlapping boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicate query entry points (beta is currently identical), while ladder/standings, ai_visibility_check/scan_competitor_ai_presence, and polymarket_edges/polymarket_arbitrage also blur together. An agent would struggle to reliably pick the right tool without reading very long descriptions.
The vast majority of names are snake_case and many follow a readable verb_noun shape, such as resolve_entity, validate_claim, and list_subscriptions. However, the Squiggle/AFL tools are bare nouns (games, ladder, sources, standings, teams, tips), and the polymarket_* / pipeworx_* prefixes do not use one consistent verb style, so it is not a fully uniform convention.
37 tools is in the too-many band, and the sprawl is compounded by mixing unrelated domains under one server: AFL stats, a huge Pipeworx data-routing layer, prediction-market analytics, AI visibility checks, npm dependency review, and llms.txt generation. The set feels like several merged servers rather than one well-scoped MCP.
The query/research surface is broad and covers many subdomains, and the subscription lifecycle is reasonably complete with subscribe/list/unsubscribe/recent_alerts. However, pipeworx:// citation URIs are prominently returned but no tool fetches a cited record directly, the AFL side lacks player-level data, and subscriptions cannot be updated.