get_entity_metricsEvery verified figure Muffed holds for one NFL player or one NFL team (by name or abbreviation, e.g. "Bijan Robinson" or "DEN"), with the source's rank where one exists. Names are resolved before lookup; an unrecognised name returns the closest matches rather than a guess.
| Name | Required | Description | Default |
|---|---|---|---|
| entity | Yes | ||
| metric | No | ||
| season | No | ||
| entity_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (read-only, idempotent, non-destructive), the description adds concrete behavioral details: names are resolved before lookup, unrecognized names return closest matches, and source rank is included where available. This is rich, useful context.
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, front-loaded with the core purpose, examples, and error behavior. No wasted words.
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 4-parameter tool with no output schema, the description covers entity resolution and error handling well, but omits semantics for 'metric' and 'season', making it partially incomplete.
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 has 0% description coverage. The description only clarifies the 'entity' parameter with examples, but does not explain 'metric', 'season', or 'entity_kind'. The agent is left to guess their meaning, which is a significant gap.
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 returns every verified figure for one NFL player or team, with examples, and distinguishes it from siblings by focusing on a single entity's verified stats.
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?
It implies use when needing a player/team's verified figures and explains name resolution and fallback behavior. However, it does not explicitly mention alternative tools or when not to use it, so not a 5.
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.
Each tool targets a distinct resource or action: entity metrics, metric definitions, leaderboards, comparisons, backfield info, editorial insights, claim verification, and five separate Sleeper league tools each with a clear purpose. No two tools could be easily confused.
All tool names use snake_case and follow a predictable verb-noun pattern (get_, list_, query_, compare_, verify_) with the sleeper_* prefix consistently applied to fantasy-league tools. The naming convention is uniform throughout.
13 tools is within the ideal 3-15 range and matches the server's dual purpose of verified NFL stats and fantasy context. Every tool serves a clear function without redundancy or bloat.
The tool surface comprehensively covers the stated domain: metric retrieval, definitions, listings, leaders, comparisons, backfield context, insights, claim verification, and fantasy league operations. No obvious dead ends or missing core operations.