mlb_teamsList all MLB teams from the official MLB Stats API. Returns each team with id, name, abbreviation, location, league, division, and home venue.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful behavioral detail by stating the data comes from the official MLB Stats API and that the result is a list of team objects with specific fields. No contradictions with annotations were found.
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 short sentences with no filler. The primary action and scope are front-loaded, and the field list is compact and useful.
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 zero-parameter read-only list tool, the description is largely complete, including return fields. The only minor gap is ambiguity around whether 'all MLB teams' means current active teams or includes historical franchises, which an agent may need to disambiguate.
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?
The tool takes zero parameters, so the description has no parameter burden to carry. The schema fully covers parameters (100% coverage with none present), and the description adds value by naming the output fields rather than parameter details.
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 uses a specific verb ('List') with a clear resource ('all MLB teams') and names the data source ('official MLB Stats API'). It also enumerates the returned fields, so an agent can distinguish it from sibling tools like mlb_roster or mlb_schedule without opening schemas.
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 makes the use case clear: when an agent needs a complete list of MLB teams with identifiers and basic metadata, this is the tool. It does not explicitly name alternatives or state when not to use it, but the direct task framing provides enough context for correct selection.
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.