Canvas64

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds value by enumerating exactly what the read yields (owner, step, prices, royalty, purchase link), but says nothing about unowned/unclaimed cells or invalid-address 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?

One front-loaded sentence: the resource and address format come first, then the returned fields. The field list is long but each item earns its place by describing the response, since no output schema exists.

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 carries the return-value burden and does so by naming every field returned. The remaining gap is edge-case behavior (unowned cell, out-of-range address) for what is otherwise a simple read.

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 the single parameter is self-documenting, so the baseline is high regardless. The description still contributes by restating the address form as an explicit A1–H8 board range, reinforcing the schema's regex in human terms.

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 (read) and resource (one cell by address, range A1–H8) plus the exact payload it returns. It's clearly distinguishable from get_rules and get_wall, though it never says how it differs from the sibling `quote`, which plausibly also surfaces claim/buyout pricing.

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 when-to-use / when-not-to-use guidance and no mention of alternatives. An agent must infer that this is the per-cell lookup and guess whether `quote` is the right tool for pricing questions, which is a real ambiguity given the description advertises prices.

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 covered. The description adds one genuinely useful behavioral detail — the result is a short set of 'quotable sentences' rather than a full specification — but says nothing about caching, completeness or whether the rules are static.

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 sentence that front-loads the resource and then lists the covered topics; every clause maps to a distinct piece of content the agent might be seeking, 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?

For a zero-parameter, read-only reference tool with no output schema, the description is nearly sufficient: it tells the agent what topics are inside. Only the shape of the return (short prose snippets, not structured data) is left partly implicit.

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. No misleading parameter hints are present.

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 names the resource ('the rules of Canvas64') and enumerates exactly what content it covers — price formula, payout, royalty, contract/network, cell services, audit status, docs — which lets an agent distinguish it from get_cell, get_wall and quote. The verb is only implied by the tool name, keeping it just 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 place to look for pricing/royalty/contract rules rather than per-object data, but there is no explicit when-to-use statement, no exclusion, and no pointer to siblings like quote for pricing calculations.

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, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds real value by disclosing the returned payload (owner identity and wallet, step, cost fields, links) and the fixed grid layout, which the agent could not learn from annotations.

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, both dense and front-loaded: the first defines scope and return shape, the second gives use cases. No filler and nothing repeated from the schema or annotations.

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 carries the burden of explaining return values and does so field by field. It is nearly complete for a fixed 64-cell read; the only omission is the filter parameter's role, which the schema covers.

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 the single 'filter' enum (all/free/taken) is fully documented in the schema, so the schema does the heavy lifting. The description never mentions the filter or how it interacts with the 'all 64 cells' claim, adding nothing beyond what the schema already provides; 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 resource (all 64 cells of the Canvas64 wall, A1 to H8) and enumerates exactly what each cell record contains: owner name and wallet, step, purchase purpose, current buyout cost, and owner links. The 'all 64 cells' framing implicitly but clearly distinguishes it from the sibling get_cell, which reads a single cell.

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 names four use cases: find free cells, the cheapest cell, the priciest cells, or a creator on the wall. That's clear positive guidance. It does not state exclusions or point at get_cell for single-cell lookups, so the routing is inferred rather than 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 establish readOnly/idempotent/non-destructive behavior, so the description correctly spends its budget elsewhere: it discloses the pricing formula (1.65x per step from $30, no cap) and the fact that the optional `until` produces an accumulated royalty figure. That formula and the 'no cap' caveat are genuine behavioral context an agent could not get from structured fields. It stops short of describing the return shape (e.g. currency units or object 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?

Two sentences, front-loaded with the payload contents and then the pricing rule. The long enumeration in the first sentence is dense but each clause carries distinct information about a returned value; nothing is padded or redundant.

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?

There is no output schema, so the description carries the burden of explaining return values, and it does so by listing the four figures returned. The main remaining gap is the concrete shape of the response (field names, currency, whether the optional royalty figure is omitted or zero when `until` is absent).

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. The description goes slightly beyond the schema by framing `step` as 'one step of any cell' in a rising price ladder and by clarifying that `until` sums the royalty the owner has earned up to that later step, which tells the agent what the optional parameter actually produces rather than just its bounds.

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 names a specific resource (the Canvas64 price model for one step of a cell) and enumerates exactly what the quote contains: price, buyout payout, royalty rate, and optional cumulative royalty. It is clearly a read-only pricing calculator, though it never explicitly contrasts itself with siblings like get_cell or get_rules, so the differentiation is inferred rather than stated.

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 it should call this to learn what a step costs or what an owner would receive. There is no explicit 'use this when X, use get_cell instead when Y' guidance, and no mention of prerequisites such as needing a valid cell or valid step range (though the schema caps step at 1-60).

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