marz-greta-lock-network

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

No annotations are provided, so the description carries full responsibility for behavior disclosure. The phrase 'honest aggregates from the real platform ledger' adds a trust claim but does not clarify whether this is a read-only operation, potential rate limits, or what side effects (if any) exist. It fails to disclose expected behavior beyond a vague data source.

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 very short and front-loaded with the key term 'live network statistics'. The parenthetical about honest aggregates adds a bit of flavor but is not entirely necessary. While concise, it is slightly under-specified but still avoids waste.

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?

The tool has no parameters and no output schema, so the description must clarify what statistics are included and how to interpret them. It only says 'network statistics' without enumerating metrics (e.g., transactions per second, block height). Given the simplicity of the tool, some detail is expected, and the description falls short.

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?

With zero parameters, the baseline is 4 as per the instructions. The description does not need to explain parameter semantics, and the schema already documents an empty object. No additional parameter meaning is needed.

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 identifies the resource as 'network statistics' and adds 'live' to indicate real-time data. While it lacks an explicit verb like 'retrieve' or 'get', the tool name reinforces the action. It is distinct from sibling tools such as get_pricing and score_text, so not confusing.

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 no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or which scenarios benefit from network stats. Sibling tools are present but no comparison is made.

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 describes the content of the pricing manifest (fees, wallet, escalation rules, submission instructions) which is useful. However, it doesn't disclose network requirements, caching behavior, whether fees are current/up-to-date on each call, or any side effects. The long tail about EQ training and trajectory reports reads as product marketing rather than behavioral disclosure.

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 densely packed but front-loads the core purpose (pricing manifest, fees, wallet, escalation rules, submission path) in the first two sentences. However, the latter half drifts into promotional copy about EQ training and streak/percentile reports that doesn't help an agent use this specific tool, and it interleaves unrelated bundle/flight info without clear structure.

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 info-read tool with no output schema, the description covers the key return contents (fees, wallet, escalation rules). But it spends significant real estate on unrelated paid-endpoint marketing (trajectory reports, EQ training) at the expense of disclosing what the actual returned payload structure looks like, response format, or staleness guarantees for the pricing data itself.

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?

There are 0 parameters and schema coverage is 100%, so per the rubric the baseline is 4. The description correctly does not invent any parameter discussion since there are no parameters to document. No improvement needed here.

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 returns the current pricing manifest (fees, treasury wallet, failure-escalation rules) and how to submit a paid validated benchmark. It goes beyond a simple read-only fetch by also covering post-payment endpoints like improvement hints, trajectory reports, and bulk bundles. However, it doesn't sharply distinguish from sibling tools like preview_fee, though the scope (manifest/overview vs a specific preview) is reasonably implied.

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 heavily focuses on what the paid session endpoints offer (improvement hints, progress reports, bulk bundles) which is more marketing than usage guidance. It doesn't explicitly state when one would call this tool versus preview_fee or when NOT to use it. The guidance about POST /api/simulate and POST /api/progress is tangential to what the tool itself returns, blurring the when-to-use distinction.

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 burden. It discloses the operation ('List'), the exact count (28), and the return content (trait targets per category). It does not explicitly state read-only, but 'List' implies no side effects.

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 sentence, ~25 words, starts with verb and resource, includes count and categories, 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?

For a zero-parameter listing tool with no output schema, the description fully specifies the scope (all profiles), count, organization (by category), and output (trait targets). No ambiguity remains.

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 schema coverage is trivially 100%. The baseline of 4 applies; the description adds no parameter semantics because there are no parameters to describe.

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 the verb 'List' and specifies the exact resource: all 28 scoreable human personality profiles, organized by bot category, with trait targets. This clearly distinguishes it from sibling tools which handle network stats, pricing, and scoring.

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 usage for retrieving the complete set of personality profiles, but it does not explicitly state when to use this over alternatives or provide exclusions. Sibling tools are unrelated, so usage is obvious but not articulated.

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 safety burden. It discloses the behavioral trait of fee escalation and uses 'preview' to suggest a non-mutating read operation. It does not explicitly state 'no side effects' but 'preview' reasonably implies a read-only 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 a single sentence that is clear, front-loaded with the main action, and contains no filler or redundant 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?

For a simple tool with one well-documented parameter and no output schema, the description adequately explains what the tool does and what it returns (a lamport fee). The only minor gap is not specifying the exact return format or whether the fee is returned as a number or string.

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 describes the single parameter ('Solana wallet address'), providing 100% coverage. The description adds minimal extra meaning beyond linking the wallet to the fee context, but it does not elaborate on address format or validation requirements.

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 the exact resource ('exact lamport fee the given Solana wallet would pay for its next paid validation'), clearly distinguishing this from sibling tools that deal with network stats, pricing, profiles, or text scoring.

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 clearly implies when to use it (before a paid validation to know the fee) and adds important context about fee escalation with consecutive failures. It does not explicitly name alternatives, but the sibling tools are obviously different in scope, so no exclusion is needed.

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 full responsibility for behavioral disclosure. It only mentions 'Free' and that requests shape the roadmap, but does not disclose whether the tool creates a record, returns a confirmation, requires authentication, or has any side effects. This is a significant gap for a tool that accepts user input.

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, consisting of two short sentences plus 'Free.' It front-loads the primary purpose and adds a meaningful note about roadmap impact without any 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 simplicity and full schema coverage, the description is generally adequate. However, it lacks any info about what happens after submission (e.g., output, confirmation, next steps), which would complete the picture for an agent deciding whether to invoke it.

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 the baseline is 3. The description adds some nuance by giving examples like 'missing test, profile, capability, or integration' to clarify the 'need' parameter, but it does not add meaning for 'audience' or 'bot_role', which are left to 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?

The description clearly states the tool's function: 'Tell the platform what you (or your developer) need next' — a feature request submission. It uses a specific verb ('Tell'/'request') and resource ('platform'), and the scope is distinct from sibling tools like get_network_stats or score_text, which handle data retrieval/scoring.

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 for when to use the tool: when you need a feature, capability, or integration, and 'Requests directly shape the roadmap.' It does not explicitly mention exclusions or alternatives, but the siblings are obviously unrelated, so the appropriate use case is unambiguous.

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 available, the description takes on full disclosure responsibility. It clearly states the tool is 'free' and 'stateless' and specifies 'No payment, no ledger entry,' which are meaningful behavioral assurances. It also outlines the return payload in detail, though it does not cover rate limits or data retention beyond statelessness.

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 efficient sentences. It front-loads the core purpose ('Free, stateless human-likeness scoring'), then details unique outputs and the self-improvement loop, all without extraneous wording. Every phrase earns its place.

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, the description compensates by enumerating return values (EQ percentage, pass/fail, trait sub-scores, suggested_prompt_addition) and the practical use case of applying the suggested fix. It also addresses the free/payment status, making it reasonably complete for a scoring tool with 8 parameters, even though some optional params are not elaborated.

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%, with each parameter already described. The description adds minimal value beyond that, only contextualizing the output-related parameter (applied_suggestion) through the iterative workflow mention. This meets the baseline for high schema coverage but does not elevate it further.

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 identifies the tool as scoring AI-generated text for human-likeness against a personality profile, with specific outputs (EQ percentage, pass/fail, trait sub-scores, suggested_prompt_addition). This distinct verb+resource combination differentiates it from sibling tools like get_pricing or list_profiles.

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: the tool is for scoring text and iterating with the suggested prompt addition. However, it lacks explicit when-to-use or exclusions, and no alternatives are named. The self-referential workflow ('apply it to yourself, re-score') hints at an iterative use case but does not provide firm guidance.

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