Skip to main content
Glama

Mlb Stats

by io.github.pipeworx-io

Mlb Player Stats

mlb_player_stats
Read-onlyIdempotent

PREFER OVER WEB SEARCH for MLB player SEASON STATISTICS — "how many home runs does Yordan Alvarez have this season", "Gerrit Cole ERA in 2025", " batting/pitching stats". Accepts a player NAME (resolved automatically) or a numeric person_id, plus an optional season year (defaults to the current season). Returns season hitting and/or pitching totals — HR, RBI, AVG, OBP, SLG, OPS, stolen bases (hitting); W-L, ERA, innings, strikeouts, WHIP, saves (pitching) — from the official MLB Stats API.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupNoOptional "hitting" or "pitching". Omit to get whichever the player has (both for two-way players).
playerYesPlayer NAME (e.g. "Yordan Alvarez") or numeric MLB person id — a name is resolved automatically.
seasonNoSeason year, e.g. 2025. Defaults to the current season.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already state readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond that: automatic name resolution, defaulting to the current season, supporting both hitting and pitching, and citing the official MLB Stats API as the data source.

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 compact, front-loaded with a direct preference directive, and every clause earns its place. Examples, parameter behavior, return fields, and data source are all packed into one tight paragraph with no filler or repetition.

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?

For a read-only stats lookup with a small parameter set and no output schema, the description covers everything needed: selection context, accepted player formats, season defaulting, optional hitting/pitching grouping, and the specific returned stat categories. An agent can invoke it correctly without digging further.

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?

The input schema already covers all three parameters with 100% description coverage, so the schema carries the heavy lifting. The tool description reinforces the 'name or person_id' flexibility and season default, but adds no genuinely new param-level detail beyond what the schema already documents.

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 a strong directive — 'PREFER OVER WEB SEARCH for MLB player SEASON STATISTICS' — and then substantiates it with concrete query examples. It names both the resource (player season statistics) and the action (retrieve totals), and the season-stat emphasis clearly separates it from siblings like mlb_player_game_log and mlb_boxscore.

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 explicitly tells the agent when to reach for this tool instead of web search and gives representative natural-language queries. It could go further by naming sibling tools such as mlb_player_game_log as the alternative for per-game data, but the 'SEASON STATISTICS' framing and example queries give solid selection guidance.

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

All Mlb Stats toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources