mlb_rosterActive roster for an MLB team from the official MLB Stats API. Returns each player with id, name, jersey number, and position abbreviation.
| Name | Required | Description | Default |
|---|---|---|---|
| team_id | Yes | MLB team id (e.g. 147 for the Yankees). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds useful context by noting the official MLB Stats API source and the 'active' roster scope, but it does not disclose additional behavioral details such as data freshness, rate limits, or how open-world changes might affect results.
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?
Two sentences with no filler or repetition. The core purpose is front-loaded, and the return value summary is concise without repeating schema details.
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-parameter, read-only tool with full schema coverage and no output schema, the description provides the necessary return-field context. It does not mention how to obtain team IDs or reference sibling tools, but with the schema example provided this is a minor gap rather than a completeness failure.
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 only parameter, team_id, is already documented with type and an example. The description does not add meaning beyond the schema, so the baseline score of 3 is appropriate.
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 identifies the resource ('Active roster for an MLB team') and the specific operation ('Returns each player with id, name, jersey number, and position abbreviation'). It is distinct from sibling tools like mlb_schedule, mlb_standings, and mlb_teams.
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 this should be used when the agent needs an active MLB team roster, and the return fields clarify what is available. However, it does not explicitly distinguish when to choose this over related sibling tools such as mlb_teams or mlb_player, nor does it state any exclusions or alternatives.
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.
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.
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.
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.
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.