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Mlb Stats

by io.github.pipeworx-io

Mlb Standings

mlb_standings
Read-onlyIdempotent

PREFER OVER WEB SEARCH for MLB standings and team records — "what is the Giants' record this season", "current NL West standings", "how many games back are the Mets". MLB division standings (regular season) from the official MLB Stats API. Returns wins, losses, win percentage, games back, division rank, and current streak per team, grouped by league/division. Defaults to the CURRENT season; pass season only for a past year (e.g. "2024").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seasonNoSeason year, e.g. "2024". Omit for the current season — only pass this for a past year.
league_idNoComma-separated league IDs (103=AL, 104=NL). Default "103,104" (both).

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is known. The description adds useful behavioral context: it defaults to the current season, only accepts a past season in `season`, returns grouped league/division standings, and lists the computed metrics. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with no filler. The strongest guidance ('PREFER OVER WEB SEARCH') is front-loaded, followed by examples, data scope, output fields, and default behavior. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description lists the returned fields (wins, losses, win percentage, games back, division rank, streak) and explains grouping. Combined with the fully documented parameters, this is sufficient for an agent to invoke the tool correctly for standings queries.

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%, with both `season` and `league_id` already fully documented including defaults and examples. The description reinforces that `season` should be omitted for the current year, but it does not add substantial meaning beyond the schema, so the baseline 3 is appropriate.

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 is highly specific: it identifies the resource as 'MLB division standings (regular season)' from the official MLB Stats API, and gives concrete example queries like 'current NL West standings' and 'how many games back are the Mets'. This clearly differentiates the tool from sibling mlb_boxscore, mlb_schedule, or mlb_player tools by naming the exact data domain.

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?

It explicitly says to prefer this tool over web search for standings and team records, providing example user intents. It does not enumerate exclusions or explicitly contrast with sibling MLB tools, but the usage context is unmistakable and actionable.

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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TDQS

A3.7/5.0
Disambiguation2/5

Severe overlap between entry points: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the prediction-market cluster (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk) has fuzzy boundaries that would confuse an agent picking one. Entity lookups (entity_profile, compare_entities, recent_changes, validate_claim) also overlap on company data. The stakes are raised by the server being named 'Mlb Stats' while most tools are unrelated general-data tools, compounding misselection risk.

Naming Consistency2/5

There is internal consistency within families — the 7 MLB tools share a clean get_ prefix, and the Poly tools share a polymarket_ prefix — but the overall set mixes bare verbs (remember, recall, forget), prefixed families (ask_pipeworx_*), and descriptive compounds (ai_visibility_check, generate_llms_txt) with no unifying convention. The 'Mlb Stats' server name bears no relation to the dominant ask_pipeworx/deep_research naming, which further breaks pattern expectations.

Tool Count2/5

38 tools is well past the heavy threshold, and more importantly the bulk of them (SEC filings, FRED economics, Polymarket arbitrage, npm scanning, AI visibility, subscriptions) have nothing to do with the server's stated MLB purpose. Only 7 of 38 tools are actually baseball-related, so the count is both too high for the labeled scope and misallocated.

Completeness3/5

The 7 MLB tools cover teams, rosters, schedule/scores, standings, player profiles, season stats, and batter-vs-pitcher history — a solid read-only core. However, obvious gaps remain: no per-game box scores, no league leaders, no team offensive/pitching stats, no live game detail beyond final scores. The Pipeworx catch-all router technically fills data gaps but leaves the MLB-specific surface incomplete.

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