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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and non-destructive, so the safety profile is covered. The description does add one genuinely useful behavioral fact: it computes metrics the source does not publish (FIP, K-BB%, LOB%, ISO), implying derived/calculated output rather than raw retrieval. It says nothing about caching, data freshness, or league-specific caveats.

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?

A single front-loaded sentence with zero waste. The differentiating capability (computed metrics) is placed prominently rather than buried.

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?

With an output schema present and full parameter documentation, the description does not need to explain return values or defaults. It adequately frames the tool's core behavior, though it omits that league/season/qualification scope is configurable, which the schema silently handles.

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%, so the schema already documents all 11 parameters including sort_by enums, defaults, and qualification rules. The description adds no syntax or format detail beyond what the schema provides, so the baseline 3 applies.

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?

States a specific verb (rank), resource (players), and mechanism (any computed metric, including derived stats the source doesn't publish). This clearly separates it from siblings like baseball_player_stats or mlb_standings, though it never names an alternative to route against.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no mention of alternatives such as baseball_player_stats for a single player. Usage is only inferable from the verb 'Rank'.

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

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

Annotations already establish the safe, read-only, idempotent profile, so the description is free to add value elsewhere: it discloses the output shape (gaps, plain-language signals, verdict, caveats) and the caveat categories (sample size, contact quality, defense). The 'leads not conclusions' framing usefully sets expectations about interpretation. It stops short of noting data-freshness or coverage limits.

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?

Front-loaded opening question, one sentence on method, one on returns, one on caveats — every sentence earns its place with no filler.

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?

With a full schema, complete parameter coverage, and an output schema, the description is nearly complete; it even summarizes return values despite the output schema existing. Only minor gaps remain (no explicit alternative-tool routing), keeping it just below the top.

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%, with player/team/league/season/format all documented in the schema itself, so the schema already carries parameter meaning. The description adds no format or naming syntax beyond what the schema provides, making the baseline 3 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?

It states a specific analytical verb and resource ('Is a pitcher's ERA lucky or unlucky? Compares ERA to FIP, then BABIP and LOB%') and clearly sets itself apart from generic siblings like baseball_player_stats by being a verdict/interpretation tool rather than a raw-stat lookup.

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 intent ('is ERA lucky or unlucky') implies when to reach for it, and the closing line 'Signals are leads to investigate, not conclusions' frames how to treat output. However, it never states when to prefer this over baseball_player_stats or an explicit exclusion, relying on inference.

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

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower, and the description adds real context: it details the computed metrics per group and discloses that FIP relies on a league constant derived from that season's league totals. It stops short of noting edge cases such as ambiguous or missing players.

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?

Front-loads the core purpose in one sentence, then lists the returned metrics per group. The metric enumeration is dense but serves the caller; it could be trimmed slightly since an output schema exists, but nothing is padding.

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 6-parameter lookup tool with full schema coverage, annotations, and an output schema, the description covers purpose, routing, and result shape completely. An agent has everything needed to call it correctly without opening the schema.

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%, so the baseline is a 3, but the description adds meaning by tying the 'group' parameter to two distinct output shapes (hitting vs pitching metrics) and by explaining the derivation of FIP. That is genuine value beyond the enum labels in the schema.

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?

States a specific verb and resource ('Get one player's regular-season line plus computed sabermetric rates') and enumerates the exact metrics returned for each group, so an agent knows precisely what the tool produces. It also distinguishes itself from a named sibling (baseball_pitcher_luck_check), so it is not confused with the other baseball tools.

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?

Explicitly routes one case to an alternative: 'Use baseball_pitcher_luck_check for an ERA-vs-FIP read.' That is clear context for a specific scenario, but there is no broader when-to-use statement versus siblings like baseball_leaderboard or mlb_search_players, and no explicit exclusions.

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

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is fully covered structurally. The description adds the substance of what is surfaced (row counts, column problems, expected names), but an output schema already exists to carry return details, so the marginal behavioral value is modest.

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?

A single compact sentence that front-loads the action and enumerates the reported facets without filler. Every clause earns its place.

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 diagnostic whose return shape is defined by an output schema, the description covers what the agent needs to know. Only the trigger conditions for calling it are absent, a minor gap given the tool's simplicity.

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 there is nothing for the description to disambiguate; baseline 4 applies. The 100% schema coverage and empty properties object leave no semantic 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?

States a specific verb (show) and a precisely scoped resource (KBO CSV file load status), enumerating the facets reported: loaded files, row counts, column problems, expected names. This is clearly distinguishable from the sibling tools, which deal with player stats, leaderboards, and standings, not data-file diagnostics.

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 a diagnostic use case but never states when to reach for this tool versus alternatives or what condition triggers it (e.g., before running a stats query to verify data is present). Usage is inferable from the content but not spelled out.

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

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

Annotations already declare the safety profile (read-only, idempotent, non-destructive, open-world). The description adds useful behavioral context beyond that: it explains that the tool returns ids and that those ids are meant to be passed to other tools, plus it flags ambiguity handling.

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 zero waste. The core action is front-loaded, and the workflow cue is placed second without padding.

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?

With an output schema present and annotations covering safety, the description does not need to explain return values. It sufficiently covers what the tool does and how its output integrates with sibling tools, leaving no essential gap for correct invocation.

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%, so both the name and limit parameters are fully documented in the schema. The description adds no additional parameter semantics beyond what the schema already provides, which is the baseline expectation.

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 states a specific verb and resource: find MLB player ids by name. This implicitly distinguishes it from sibling stats/standings tools by emphasizing that the output is an id, but it does not explicitly name an alternative or contrast scope.

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 gives clear workflow guidance: use the returned id with other tools when a name is ambiguous. This implies the tool should be used as a resolver before other baseball tools, though it does not state when not to use it or name a specific alternative.

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

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and openWorld, so the safety profile is covered. The description adds useful content context (what metrics are computed) but says nothing about data freshness, season availability, or any rate/API constraints, so it adds only modest value beyond the structured fields.

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?

A single front-loaded sentence naming the resource and its key outputs, with zero filler. Nothing redundant with the title or schema.

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?

An output schema exists, so return values need not be explained, and the schema fully documents all three parameters. The description covers scope and computed metrics; only minor omissions (e.g., whether standings are live or cached) keep it from a 5.

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%, with 'format', 'season' and 'exponent' fully documented including defaults and ranges, so the baseline of 3 applies. The description alludes to Pythagorean expected wins but adds no new syntax or usage meaning for the exponent parameter beyond what the schema states.

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?

States a specific verb+resource ('Regular-season standings by division') and enumerates the derived metrics returned (run differential, Pythagorean expected wins, luck), so the agent knows exactly what it gets. It does not explicitly differentiate itself from siblings like baseball_leaderboard, which could partially overlap, so it stops short of a 5.

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?

Usage is only implied: an agent can infer this is the tool for division standings, but there is no explicit when-to-use, when-not-to-use, or named alternative (e.g., vs baseball_leaderboard). Minimum viable guidance, no exclusions.

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