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

No annotations are provided, so the description carries the full burden. It discloses HMAC-SHA256 hash-chaining, tamper-evident behavior ('any tampering invalidates subsequent signatures'), and the Pro license requirement, which is substantial behavioral 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?

Front-loaded with purpose, then cryptographic detail, license, and args. The Stripe URL is lengthy but necessary for the license requirement; overall efficient and well-structured.

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?

Has an output schema, so return values need not be explained. The description covers purpose, behavior, prerequisites, and the sole parameter, though it omits sibling differentiation.

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 0% with one parameter. The description explains export_path as an optional file path to export audit JSON, adding clear meaning beyond the schema's type-only definition.

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 'Verify' and resource 'cryptographic audit trail from the last optimization', but does not differentiate from the sibling quantaoptima_verify_chain. An agent can understand the purpose, though sibling ambiguity remains.

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 phrase 'from the last optimization' implies use after an optimization, and the Pro license requirement is a prerequisite. However, there is no explicit guidance on when to choose this over alternatives like verify_chain or chain_status.

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 are provided, so the description carries the full burden. It does disclose genuine behavioral context: three specific optimizers are run, they share the same evaluation budget, and results are returned side-by-side, plus a Pro-license access requirement. However, it omits runtime cost, whether it is compute-heavy or long-running, and any rate or resource implications.

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 purpose and mechanism are front-loaded in the first three sentences, followed by a compact Args block. The embedded Stripe purchase URL is slightly promotional noise, but it is relevant to the stated Pro-license requirement, so little is wasted overall.

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 described, and the description adequately covers purpose, mechanism, and all three parameters with ranges. The main gap is the absence of usage routing against siblings and any runtime/behavioral expectations for a multi-optimizer run.

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 0%, so the description must compensate, and it largely does: objective enumerates the valid built-in functions (sphere, rastrigin, rosenbrock, ackley, griewank, levy), dimensions gives the 2-50 range, and max_evals gives the 1000-50000 budget range. This adds meaning the bare schema lacks, though defaults (rastrigin/10/5000) are not restated.

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: 'Compare QuantaOptima against classical optimizers on the same problem,' naming the exact algorithms (QuantaOptima, Differential Evolution, Dual Annealing) and the equal-budget constraint. This distinguishes it from the sibling quantaoptima_optimize, which presumably runs a single optimizer rather than a head-to-head comparison.

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 benchmarking use case and notes a Pro-license precondition, but never explicitly says when to choose this over quantaoptima_optimize or when a comparison is warranted. Usage is inferable from the word 'Compare' but not spelled out with exclusions or a named 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?

With no annotations, the description carries the full behavioral burden. 'View' implies a read-only operation and it discloses the returned content and tier availability, but it never states there are no side effects, no auth requirements, or any pagination/rate-limit behavior. Adequate but with clear gaps for an unannotated tool.

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 purpose is front-loaded in the first sentence and the body is short and scannable. The trailing 'Returns: JSON with chain statistics' partly restates the opening line, a minor redundancy but not significant bloat.

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-value detail need not be spelled out; the description nonetheless summarizes the fields shown and notes tier availability. For a zero-argument read tool this is essentially complete, with only the missing sibling routing keeping 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?

The tool takes zero parameters, so there is nothing for the description to clarify beyond what the empty schema already shows. Baseline 4 applies for a no-arg tool.

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 (View) and resource (audit chain statistics and health) and enumerates what is shown: action count, verification status, action type breakdown, and actors. It is clearly not the same as verify_chain or export_chain, though it never explicitly names those siblings, so differentiation is inferable 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?

The description says it is 'available on all tiers,' which is an availability note, not usage guidance. It gives no indication of when to call this versus quantaoptima_verify_chain, quantaoptima_status, or quantaoptima_audit, leaving the agent to guess from names alone.

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?

With no annotations, the description carries the full burden, and it does disclose the shape of the explanation and that it concerns the most recent run. However it never states this is a read-only operation, nor the precondition that a prior optimization run must exist. Coverage is adequate but incomplete for an unannotated tool.

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 with the one-line purpose, then two clarifying lines, with no filler prose. The tier-availability sentence is marginal but short and useful to some callers.

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 parameterless read tool with a full output schema, the description supplies enough: it frames the content as a human-readable narrative about the last run. The missing precondition (a completed optimize run) is the only notable gap, and the output schema covers return details.

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 no parameter semantics to explain; the baseline for a 0-param schema is 4. Nothing in the description misrepresents the argument surface.

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?

Names a specific verb (explain) and resource (last optimization run) and enumerates what the explanation covers: what happened, how quantum operators contributed, and whether the result is likely optimal. This distinguishes it from siblings like optimize/benchmark/observe/audit, though it does not explicitly name an alternative.

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?

No guidance on when to call this versus quantaoptima_audit, observe, or status, all of which could plausibly overlap. The only usage-adjacent statement is a tier availability note ('Available on all tiers'), which is licensing info, not invocation guidance.

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?

With no annotations the description carries the full burden and does well: it discloses what the export contains (HMAC-SHA256 signatures, timestamps, before/after state, chain linkage), that JSON is independently verifiable with the HMAC key, that HTML is self-contained, and the tier availability. It omits permissions/rate-limit context, so not a full 5.

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?

Purpose is front-loaded in the first line, followed by contents, verification modes, tier note, and structured Args/Returns. Mostly earns its length, though some stakeholder-oriented marketing phrasing and the Pro-tier aside are marginal 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?

An output schema exists so return-value detail is not required, and the description still notes inline vs file-save behavior. Both parameters, contents, and verification paths are covered; only safety/permission context is thin, which is minor for an export operation.

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 0%, so the description must compensate, and it largely does: it documents format's values and default, filepath's empty-string inline behavior, and the .html extension requirement. The only gap is not enumerating format values formally, but the semantics for an agent are complete.

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 (export) and resource (the full audit chain) plus the two output forms, so the action is unmistakable. It does not explicitly differentiate itself from siblings like verify_chain or chain_status, so it lands at clear-but-no-sibling-routing rather than 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 implied through benefit statements ('perfect for sharing with stakeholders, compliance officers, or collaborators') and the JSON-verification note, but there is no explicit when-to-use/when-not guidance and no named alternative tool for the adjacent tasks (verify, status). Adequate but leaves routing to 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 are absent, so the description carries the full behavioral burden and does so well: it discloses the signing algorithm, the hash-chain linkage, tamper-evidence semantics (tampering invalidates the chain from that point forward), and tier availability. It omits failure modes, auth requirements, and whether logging is synchronous, but the core behavioral profile is unusually explicit.

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 with the purpose and the security guarantee before the Args/Returns sections, and the two-sentence chain explanation earns its place. The six-item bullet list of loggable things is somewhat padded but functions as concrete usage examples.

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, return values need no further explanation, and the Args section covers every input. The remaining gap is workflow context – no prerequisites, auth model, or relationship to the sibling verify/export/status tools for a 5-param mutating 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 0%, so the description must compensate, and it does: each of the five parameters gets an explanation (action_type with concrete example values, state_before/state_after as JSON of input/output, metadata as extra tags/params, actor defaulting to "ai-agent"). This adds real meaning beyond the bare titles and types 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 (log) and resource (action to the cryptographic audit chain), and the mechanism (HMAC-SHA256 signed, hash-chained) immediately tells the agent this is the write/append tool in a family that otherwise verifies, exports, and reports status. An agent can distinguish it from quantaoptima_verify_chain and quantaoptima_export_chain without opening any schema.

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?

"Use this to make any AI agent workflow auditable" plus an explicit enumeration of what to log (decisions, API calls, file operations, transformations, queries, tool invocations) gives clear context for when to reach for it. It stops short of naming alternatives or exclusions – nothing says when NOT to log, or how this relates to quantaoptima_audit/verify_chain in a workflow.

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?

With no annotations, the description carries the full behavioral burden. It discloses the Pro-license gate (a real access constraint) and describes the interpretability payload, and 'Inspect'/'Returns' implies a read-only operation. It does not confirm read-only status, whether a prior run must exist and what happens if none does, or any rate/credit 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 opening line is front-loaded with the verb, resource, and scope, and the return-value summary is efficient. The middle 'AI safety / interpretability' paragraph is somewhat rhetorical ('what the black-box optimizer is thinking') but still informative, and the Stripe link is a minor promotional intrusion.

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 the description need not enumerate return fields, and it helpfully still characterizes the data. With no parameters and a Pro gate disclosed, the main omission is routing relative to the explain/audit/status siblings.

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 (empty schema, 100% coverage), so per the baseline there is nothing for the description to compensate for. It correctly adds no spurious parameter guidance.

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: 'Inspect the optimization landscape from the last run', plus the returns ('how the optimizer explored the search space, where entropy concentrated...'). It is clearly distinct from mutation-oriented siblings like quantaoptima_optimize. However it never names or contrasts with the closest informational siblings (quantaoptima_explain, quantaoptima_audit), so a 4 rather than 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?

'from the last run' implies the tool is used after an optimization run, and the Pro-license note is stated. But there is no explicit statement of when to prefer this over quantaoptima_explain, quantaoptima_audit, or quantaoptima_status, and no when-not guidance. Usage is implied 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses that every step is logged to the audit chain, that built-ins are negated because the optimizer MAXIMIZES, and that cryptographic auditing/telemetry are included. It doesn't state runtime cost or whether tier limits cause errors vs. silent truncation, but the operational semantics are unusually well covered.

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 purpose, then tiers, then args, then returns. The tier block and Args list are efficient; the marketing phrasing ('interference-enhanced', 'quantum-inspired') is a bit redundant but short. Overall well-structured and skimmable.

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?

Output schema exists so return values are covered, yet the description still names the return fields (best_solution, best_fitness, quantum_metrics, audit status). With no annotations, param coverage filled by the description, and tier constraints explained, an agent has everything needed to invoke correctly.

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 0%, so the description must compensate, and it does: it documents all 8 parameters with valid ranges (dimensions 2-100, max_iterations 10-5000, population_size 10-200), objective names, MAXIMIZES/negation behavior, and temperature semantics (higher = more exploration). Ranges and semantics go beyond what the bare schema provides.

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 pair ('Run quantum-inspired optimization using the Measurement-Collapse Pruner' with interference-enhanced selection) plus an explicit scope of built-in objectives. The 'optimize' framing and audit-chain logging distinguish it from siblings like benchmark, observe, and explain.

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?

Clear context on when to use (black-box optimization with auditing) and a tier table indicating which objectives/dimensions/iters apply to FREE vs PRO. No explicit when-not-use guidance or named alternatives, but the tier constraints implicitly route the agent.

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 are provided, so the description carries the full burden. It indicates this is a read-only informational check (implied by 'status' and 'shows'), but doesn't explicitly state it's non-mutating or that it requires authentication. It does describe what is returned (tier, limits, available tools, upgrade options), which is helpful. However, an output schema exists, so explaining return values is redundant. The description could note whether this requires an active license or network call, but it's adequate for a simple status check.

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 two short sentences, front-loaded with the primary purpose and followed by a concise list of what's shown. Every sentence earns its place with no filler. It's appropriately sized for a simple status tool.

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 no annotations, no parameters, and an existing output schema, the description covers the basics but misses some contextual details. It doesn't clarify how this differs from quantaoptima_chain_status, nor does it mention any prerequisites (e.g., authentication) or behavioral traits like rate limits or caching. For a status tool, it's minimally adequate but lacks the sibling differentiation that would make it complete.

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, so parameter semantics are not applicable. According to the rules, 0 params gives a baseline of 4. The description doesn't need to discuss parameters, and it doesn't.

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+resource combination: checking QuantaOptima license status and available features. It clearly describes what the tool does, and the second sentence adds what information is shown (tier, limits, tools, upgrades). However, it doesn't explicitly differentiate this status tool from the sibling quantaoptima_chain_status, which could confuse an agent about which 'status' to call.

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 guidance about when to use this tool versus quantaoptima_chain_status or other status-related siblings. The description implies it's for checking license status, but doesn't say when an agent should invoke it (e.g., before attempting optimizations, after a failed call due to licensing). No exclusions or alternatives are mentioned.

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?

With no annotations, the description carries the full burden and does reasonably well: it discloses the crypto mechanism, the all-or-nothing failure semantics ('verification fails from that point forward'), and tier availability. It stops short of stating whether the operation is read-only, what permissions it needs, or cost/latency traits.

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 action and mechanism, then the failure behavior, then tier and args. The Args/Returns scaffolding is slightly verbose for a single parameter but every sentence still carries information.

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 the description need not explain the return payload in detail, and it points to it ('JSON with verification status and chain statistics'). For a one-parameter, no-annotation tool this is largely complete, missing only permission/read-only and cross-tool routing detail.

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 0%, so the schema alone leaves 'detailed' opaque; the description compensates by explaining 'If true, return per-block verification results.' That is the only parameter and it is now documented, so the gap is closed.

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 ('verify') and resource ('the cryptographic integrity of the audit chain'), and adds the mechanism (HMAC-SHA256 signatures and hash links). This lets an agent distinguish it from siblings like quantaoptima_chain_status and quantaoptima_export_chain without opening any schema.

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 implied by the purpose — you run this to validate the chain — but there is no explicit when-to-use guidance, no mention of how it differs from quantaoptima_chain_status, and no note on prerequisites or when to prefer the 'detailed' mode. Adequate but leaves the routing decision to inference.

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