Slabline

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds useful return-content context—current graded value, raw/loose value, and across-grades total—though it does not mention not-found/error behavior.

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?

It is a single front-loaded sentence: fetch one card by id, then what it returns. There is no wasted text and the key selector appears immediately.

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 simple one-parameter read tool with rich annotations, the description supplies the main missing context: what values the fetch returns, since there is no output schema. It stops short of describing error behavior or full response structure.

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%, and the schema already says the id is a card id/slug from search. The description repeats that meaning without adding new parameter semantics, so the baseline 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 gives a specific verb (fetch), resource (one Slabline card), key selector (by id), and the returned value categories. It distinguishes fetching from searching by saying it fetches one card using an id returned by search, though it does not explicitly differentiate from the sibling value_card.

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 clearly implies the tool is used after search, when you already have a card id/slug. It does not provide explicit when-not-to-use guidance or contrast with value_card, so it falls short of a 5.

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, and openWorldHint=false, so the safety profile is covered. The description adds that results carry ids for follow-up fetch calls and that results are limited to graded cards, but says nothing about result counts, paging, or empty-result behavior.

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, no filler, with the core action front-loaded and the follow-up step ('id to pass to fetch') appended where it is immediately 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 one-parameter read-only search with no output schema, the description covers what it searches, what it returns, and how to continue the workflow. The notable omission is sibling disambiguation against search_cards, which is outside the basic call mechanics but matters for correct tool selection.

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 coverage is 100% and includes an example query, so the baseline is 3. The description still adds value by enumerating the searchable dimensions (player, set, year) and indicating they can be combined, which the schema's single example does not make explicit.

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 ('Search Slabline for graded trading cards') and narrows scope to sports & TCG by player, set, or year. However it never distinguishes itself from the sibling tool named 'search_cards', which appears to cover nearly identical ground, so an agent cannot tell from this text alone which search to pick.

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 second sentence gives a useful workflow cue ('each with an id to pass to fetch'), which implicitly says when to chain into the fetch tool. But there is no when-not guidance and no mention of the near-duplicate sibling search_cards, which is the most important routing decision an agent faces here.

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 readOnly/idempotent/safe and closed-world, so the safety profile is covered. The description adds real context beyond that: corpus size (1.5M+), grading companies covered, the 10-result cap, and the highest-value-first ordering, which tells the agent how to interpret and trust 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, zero filler, front-loaded with verb+resource and followed immediately by return shape and ordering. Every clause carries information an agent would otherwise have to guess.

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 no output schema, the description usefully discloses result count, ordering, and that each match carries a Slabline value — enough to decide whether the result is worth consuming. It stops short of naming other returned fields, but the essential return contract is present for a two-parameter lookup tool.

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 parameters are already documented, and the description's 'up to 10 matches' merely restates the limit bound. It adds a little framing about what the query string should target, but nothing beyond the schema's own examples.

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 ('Search Slabline's catalogue of graded cards') plus the searchable axes (player, set, year), which is clear. It does not distinguish itself from the sibling 'search' or 'value_card', so an agent must infer which entry point applies.

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 when this tool applies by naming the supported query dimensions (player, set, year), but never states when to prefer it over 'search' or 'value_card', nor any exclusions or prerequisites. Usage is inferable rather than specified.

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 read-only, idempotent, closed-world safety, so the description's job is to add what annotations can't — and it does: valuations are Slabline-derived from comparable sales, individual sale rows are not returned, and a free-text query resolves to the top match. It does not say what happens when neither parameter is supplied or when no match is 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 sentences, front-loaded with the return payload, then the invocation mechanics and the non-obvious caveat about underlying sale rows. Nothing is repeated from the schema or title.

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 no output schema, the description does its job by naming the three value components and clarifying that raw sale rows are not included. The remaining gap is failure-mode behavior — no result, ambiguous query, both parameters given — which matters for a zero-required-parameter tool.

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 coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: the slug is authoritative and comes from search_cards, and query resolution uses the top match rather than requiring exactness. What it omits is the interaction when both are passed, or when neither is.

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 card's current Slabline value") and enumerates exactly what comes back: graded value, raw/loose value, across-grades total. This is clearly distinguishable from the sibling search_cards, which returns matches rather than a valuation.

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?

Gives concrete routing context: take a slug from search_cards, or fall back to free-text if no slug exists. It does not state any when-not-to-use condition (e.g., that bulk valuation requires repeated calls or that it should not be used for discovery), so it stops short of explicit exclusions.

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