verify

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

With no annotations, the description fully discloses behavioral traits: it is free, returns conventions, tolerance, and source, and is clearly a read-only operation. The 'FREE.' indicator is a notable behavioral trait.

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?

The description is extremely concise: a single sentence with a colon that efficiently lists the returned data. Every word serves a purpose, and it is front-loaded with 'FREE.' and the critical instruction. No wasted text.

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?

Given the tool has no parameters, no output schema, and low complexity, the description is complete. It fully specifies what the tool returns (calculation conventions, tolerance, source) and provides usage context ('call this first'). No gaps remain.

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 has zero parameters and 100% schema coverage, so per guidelines the baseline is 4. No parameter information is needed, and the description adds no param semantics beyond what the schema provides, which is sufficient.

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 provides exact calculation conventions for both engines (FRN and OTC), verification tolerance, and index source. It uses a specific verb ('get') and resource ('conventions'), and the purpose distinguishes it from siblings like get_latest_index or verify_* coupons.

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 explicitly says 'Call this first,' which gives clear usage guidance. It implies this tool should precede the others, but does not explicitly state when not to use it or list alternatives. However, the instruction is strong and context-rich.

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?

Description indicates it is free and returns latest data, suggesting a read-only operation. However, with no annotations, more detail on caching, update frequency, or side effects would enhance transparency.

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 with front-loaded 'FREE.' and no wasted words. Efficiently communicates purpose and usage context.

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?

Given zero parameters and no output schema, description covers return value and usage link to verification. Minor gap: could mention update cadence or that it returns a single record.

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?

No parameters exist, so baseline of 4 applies. Description adds meaning about the output (date and 12dp value), which is sufficient.

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?

Description clearly states the tool retrieves the latest published SARB Compounded ZARONIA Index point with date and 12dp value. It is distinct from sibling tools like get_conventions and verify_frn_coupon.

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 advises use to confirm data freshness before verifying, providing clear context. No explicit exclusions, but sibling differentiation is implied by purpose.

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?

Since no annotations are provided, the description carries full burden. It discloses that the tool is paid ($0.25), returns coupon to 4dp, audit workings, and a verdict at 0.05bp tolerance. It explains payment flow. As a verification tool, it is likely read-only, and the description is transparent about its 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?

The description is concise, using two sentences and a short instruction. It is front-loaded with key info: paid, purpose, returns. Every sentence adds value; no fluff.

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 adequately describes the return (coupon, audit workings, verdict). It covers payment, formula, and tolerance. It is fairly complete for an 8-param tool with high schema coverage, though it could detail audit workings format.

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 88% (high), so baseline is 3. The description mentions 'expected_coupon_pct' and 'api_key' but adds no significant meaning beyond the schema. The formula reference (SARB §2.2.3) provides context but not per-parameter details. No improvement over baseline.

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's purpose: to independently recompute and verify a listed South African ZARONIA FRN coupon using the SARB §2.2.3 formula. It distinguishes itself from sibling tools like 'get_conventions' and 'verify_otc_coupon' by specifying 'listed' and referencing the specific formula.

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 provides clear context by mentioning the paid nature and payment via api_key or x402. It implies use for listed ZARONIA FRN coupons, differentiating from OTC coupons (sibling 'verify_otc_coupon'). However, it does not explicitly state when not to use this tool or compare to other siblings.

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?

No annotations provided, so description carries full burden. Discloses paid nature, independent recomputation, tolerance, and return structure (coupon, workings, verdict). Also explains payment process.

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 dense sentences cover all key aspects: purpose, conventions, payment, and outputs. No redundant or unnecessary 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?

Adequately describes inputs, outputs, and behavioral context for a verification tool with no output schema. Could optionally mention behavior when expected_coupon_pct is omitted, but not a major gap.

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 50%; description adds meaning for settlement_date (issue date) and expected_coupon_pct (used for tolerance). margin_pct and api_key lack additional context beyond 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?

Clearly identifies the tool as verifying an OTC ZARONIA coupon with specific conventions (1-bday lookback, Act/365, half-up 4dp). Distinguishes from siblings like verify_frn_coupon by specifying money-market OTC context.

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?

States when to use (to verify coupon with expected_coupon_pct) and mentions payment mechanics (api_key or x402). Does not explicitly contrast with alternatives but implies use case.

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