marz-greta-lock-network

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

With no annotations, the description must disclose behavior, but it only mentions 'honest aggregates from the real platform ledger' without explaining return format, rate limits, or read-only nature. This is insufficient for an agent to predict the tool's 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 a single sentence, front-loaded with the main purpose. The phrase 'honest aggregates' adds minimal value but does not bloat the description, making it appropriately concise.

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 output schema and no parameter information, the description should clarify what statistics are returned and any relevant context. It does not, leaving the agent uncertain about the tool's output and utility.

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 the schema is trivially complete. The description adds no parameter-specific details, but since there are none, the baseline of 4 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 states 'Live network statistics' which identifies the general resource but lacks a specific verb or details on what statistics are provided. It vaguely differentiates from siblings by topic but does not clearly define the tool's exact 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?

There is no guidance on when to use this tool versus siblings like get_pricing or score_text. The description implies it is for network stats, but provides no context for selection or 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?

With no annotations provided, the description carries the full burden. It discloses the return content (fee, treasury wallet, escalation rules, submission endpoint), plus details about improvement hints and the POST /api/progress and /api/bundle options. It's honest about what data users get — actionable hints per session and trajectory reports — which exceeds the minimum disclosure bar.

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 front-loaded with the core content (fees, wallet, escalation, submission endpoint) but then extends into promotional detail about improvement hints, POST /api/progress, and fleets/bundles. These additional details are informative but somewhat structured more like marketing copy than a tight API description, and the long continuous sentence reduces scannability.

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 0-parameter informational/manifest tool with no output schema, the description is quite complete. It covers the main content, secondary offerings (progress reports, bundles), and related endpoints. The only gap is that it doesn't explicitly state the return format or whether the pricing is retrieved from a live source, but for a manifest document tool this is adequately comprehensive.

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 0 parameters, so the description receives a baseline of 4 per the rubric. There's nothing additional to clarify since the schema already fully covers an empty parameter set (100% coverage trivially). The description correctly focuses on output content rather than parameter semantics.

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 itself as the current pricing manifest covering fees in Solana lamports, treasury wallet, failure-escalation rules, and benchmark submission. It distinguishes itself from siblings like preview_fee (which presumably predicts a fee) by positioning get_pricing as the authoritative manifest document. The verb is implicit ('retrieves/describes') but the resource scope is explicit and specific.

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 communicates substantial context about what the manifest contains, but does not explicitly state WHEN to use this tool versus alternatives like preview_fee or get_network_stats. It implies usage as a reference/manifest lookup, which is reasonable for a 0-param informational tool, but there's no explicit when/when-not 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. It clearly indicates this is a read-only 'list' operation and details the return content (all 28 profiles with trait targets, one per bot category). It does not state sorting or exact output format, but for a simple listing tool, the behavior is sufficiently transparent.

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 a single, well-structured sentence that front-loads the key action and scope ('List all 28...'). Every element contributes meaningful information without redundancy.

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 there is no output schema or annotations, the description fully explains what the tool returns and its scope. For a straightforward list endpoint, this is complete and sufficient for an agent to use it 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?

The tool has 0 parameters, so the empty schema already provides full parameter coverage. The description adds value by explaining what the returned data contains (profiles with trait targets), exceeding the baseline for parameterless tools.

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 the exact action ('List'), the resource ('all 28 scoreable human personality profiles'), and the scope (one per bot category with trait targets). This clearly distinguishes the tool from siblings like score_text or get_pricing, which serve entirely different purposes.

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 'scoreable human personality profiles' implies these profiles are used for scoring, so the tool is useful before calling score_text. However, there is no explicit when-to-use or alternative exclusions, leaving the agent to infer the context from the sibling list.

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 provided, the description carries the burden of behavioral disclosure. It adds useful context by disclosing that fees escalate with consecutive failures, and the word 'Preview' implies a read-only operation without side effects. It does not detail other behavioral aspects like rate limits or exact calculation inputs, but covers the most important 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 a single, well-structured sentence that front-loads the action ('Preview the exact lamport fee') and immediately identifies the target. Every word earns its place; there is no redundancy or 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 one-parameter, no-output-schema tool, the description is complete enough: it states what the fee is for, for which wallet, and the key escalation behavior. It omits any explicit mention of return format or error conditions, but these are not necessary given the simplicity and the 'exact fee' implication.

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 input schema already fully describes 'wallet_address' as 'Solana wallet address', achieving 100% schema description coverage. The description only references 'the given Solana wallet' and adds no new detail about parameter format or requirements, so a baseline score of 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 uses a specific verb 'Preview' and identifies a distinct resource 'exact lamport fee' for a given Solana wallet's next paid validation. This clearly differentiates it from sibling tools like get_pricing, which likely covers general pricing, or get_network_stats.

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 that the tool is for previewing the fee before a paid validation, and notes that fees escalate with consecutive failures. However, it does not explicitly state when to use this over alternatives or exclude scenarios, so it misses the top score.

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 provided, the description carries the burden of disclosing behavior. It states the action is 'Free' and that requests 'directly shape the roadmap,' implying a non-destructive, one-way submission. However, it does not clarify what happens after submission (e.g., confirmation, data retention, or limits), leaving some behavioral aspects unspecified.

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 composed of two concise sentences that front-load the core purpose ('Tell the platform what you need next') and follow with impact ('shape the roadmap') and cost ('Free'). Every word contributes value, with no redundancy or 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?

Given the tool's low complexity, no output schema, and full parameter documentation in the schema, the description is reasonably complete. It provides purpose, impact, and cost information. It could be slightly more complete by mentioning the response or confirmation behavior, but that is not critical for a simple feature-request tool and is partially covered by 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?

The input schema has 100% description coverage for all three parameters, so the schema already explains the meaning of 'need', 'audience', and 'bot_role'. The tool description adds no additional parameter-level detail beyond what the schema provides, placing it at the baseline score.

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 request a feature or need from the platform, with specific examples ('missing test, profile, capability, or integration'). It distinguishes itself from siblings like get_network_stats or get_pricing by focusing on submitting a request rather than retrieving information.

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 to use this tool (when you need to request a feature), and notes that requests shape the roadmap. However, it does not explicitly mention alternatives or provide when-not-to-use guidance, leaving the user to infer usage context from the tool's name and examples.

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?

The description discloses the stateless nature and the trial refusal behavior, but omits the data-storage requirement implied by the mandatory data_consent parameter, which contradicts the 'stateless' claim. With no annotations provided, the description carries the full burden, and this is a significant transparency gap.

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 somewhat verbose and includes marketing language like 'watch the number move' that adds little functional value. It is organized into two sentences that convey core purpose and usage, but could be more concise without losing meaning.

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?

While the description explains outputs and the overall trial/payment flow, it lacks explicit mention of the data-storage requirement and detailed behavior around delay scoring. With no output schema, the description should provide more clarity on these functional aspects.

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 schema covers all parameters with descriptions (100% coverage), so baseline is 3. The description adds context for wallet_address and applied_suggestion but does not substantially enhance understanding of the parameters beyond what the schema already 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?

The description clearly defines the tool's purpose: scoring AI-generated text against a personality profile and returning EQ percentage, pass/fail, trait sub-scores, and a suggested prompt addition. It distinguishes itself from siblings by focusing on text scoring and providing outputs like suggested_prompt_addition.

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 explicitly describes a permanent, always-on use case for free trial scoring and explains the transition to a paid SDK/REST flow with wallet addresses. It does not name alternative tools as substitutes, but gives clear context for when this tool is intended to be used.

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