marz-greta-lock-network

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context that the stats are 'live' and sourced from the 'real platform ledger', implying genuine current data rather than simulated values. However, it does not disclose whether the operation is strictly read-only, what metrics are included, or whether any caching or rate-limiting applies.

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 compact phrase that front-loads the core subject ('Live network statistics'). The parenthetical adds source credibility without unnecessary expansion, though the word 'honest' is slightly informal and could be considered nonessential.

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 parameterless stats tool, this description gives the agent a general idea of what to expect, but it leaves the exact scope of 'network statistics' undefined (e.g., which metrics, time range, format). Since there is no output schema, the description would benefit from a bit more specificity about the returned data.

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 an empty input schema, so there is nothing for the description to explain. According to the rubric, a 0-parameter tool earns a baseline of 4, and the description adds no irrelevant parameter information.

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 identifies the resource ('network statistics') and the nature of the data ('live', 'honest aggregates from the real platform ledger'), which makes the tool's purpose understandable. However, it relies on the tool name for the verb and does not explicitly say 'retrieves' or 'returns'. It is distinguishable from sibling tools like get_pricing and list_profiles by the subject matter, though no explicit distinction is made.

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 or when to prefer an alternative. There is no mention of exclusions, prerequisites, or comparison with sibling tools, leaving the agent to infer usage from the name 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 disclosure burden. It does disclose what the manifest contains: fee unit, treasury wallet, escalation rules, and paid submission details. However, it does not explicitly state side effects, authorization requirements, or whether this tool itself is a read-only lookup. For a zero-parameter pricing manifest, this is adequate but not fully 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 core manifest information is front-loaded, which is good. But the description expands into paid-session hint behavior, POST /api/progress report contents, EQ training motivation, and bundle details. These are useful pricing-related facts but make the description dense and somewhat tangential for a simple pricing lookup 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?

No output schema and no annotations mean the description must explain what the tool provides. It does enumerate the manifest contents and mentions related paid endpoints and bundle options. For a zero-parameter tool, this gives an agent enough context to know what to expect, though exact response shape is not specified.

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 zero parameters and 100% schema description coverage, so the baseline for zero-parameter tools is 4. The description adds no parameter-specific detail, but none is needed. The references to POST endpoints and optional paid services do not conflict with the empty 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?

Description opens with 'Current pricing manifest' and clearly enumerates contents: fee in Solana lamports, treasury wallet, failure-escalation rules, and submission path via POST /api/simulate. This makes the tool's resource clear. However, it lacks an explicit verb like 'returns' or 'gets' and does not contrast with sibling preview_fee, so sibling differentiation is only implicit.

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 this tool is the place to inspect current pricing, which gives some usage context. But it never explicitly states when to choose get_pricing over preview_fee or other siblings, nor does it provide when-not-to-use guidance. The detailed mentions of POST endpoints are related context, not decision rules.

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 present, the description carries the behavioral burden, and it does well by using the read-only verb 'List' and specifying both the exact item count and the contained data ('trait targets'). It does not disclose output format or authorization needs, but for a simple parameterless read-only list this is a minor 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 a single front-loaded sentence with no filler. It communicates the resource, count, category scope, and return payload compactly, and every clause 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?

The tool has no parameters and a fully specified schema, so the main missing context would be output shape. The description broadly conveys that the tool returns profiles with trait targets, which is adequate for selection and invocation, though a precise return format would make it fully complete given there is no output 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 tool has zero parameters and the schema confirms this, so the baseline of 4 applies because there are no parameter semantics for the description to explain. The description adds clarity about what the returned profiles contain, which is the relevant semantic surface for this 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?

The description names a specific verb ('List'), resource ('scoreable human personality profiles'), and exact scope ('all 28'), which distinguishes it from the sibling tools that deal with stats, pricing, fees, features, text scoring, and store layers. The one-sentence definition leaves no ambiguity about what this tool returns.

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 the tool is for retrieving personality profile data, and the examples of bot categories give useful context about when it would be relevant. However, it does not explicitly state when to choose this tool over alternatives or mention any exclusion conditions.

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 of behavioral disclosure. It does communicate an important behavioral trait ('fees escalate with consecutive failures') and the word 'Preview' strongly suggests a read-only operation. However, it does not explicitly state that no state changes are made, nor does it describe any potential error conditions or constraints, leaving some ambiguity.

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, front-loaded sentence contains the essential verb, object, target, and a critical clarifying parenthetical. There is no redundancy or filler, making it highly efficient and easy to parse.

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 tool with only one required parameter and no output schema, the description is functionally complete: it names what is returned (exact lamport fee), the subject (wallet's next paid validation), and the relevant fee-escalation context. The only minor gap is that the concept of 'paid validation' could use a bit more definition, but overall it is sufficient for a simple preview 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?

The single parameter wallet_address is already fully described in the schema as 'Solana wallet address.' The tool description does not add extra meaning beyond mentioning that this is the wallet whose fee is being previewed. Because schema coverage is 100%, the baseline of 3 is appropriate; no further compensation is required.

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 verb ('Preview'), its target resource (exact lamport fee for a given Solana wallet), and the specific context (next paid validation with fee escalation). It is a specific and well-defined purpose, but it does not explicitly name or contrast itself against the sibling tool get_pricing, so it misses the highest level of sibling differentiation.

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 the primary use case (finding the fee for a wallet's next paid validation), but it does not provide explicit guidance on when to choose this tool over alternatives, such as get_pricing, or when not to use it. There are no exclusions or scenario-based instructions.

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 present, so the description must disclose behavioral traits itself. It does disclose that requests are 'Free' and 'directly shape the roadmap', which implies a persistent side effect on the product. However, it does not disclose whether the submission is public, whether authentication is required, rate limits, or what happens after submission. For a simple feedback-submission tool this is adequate but not richly 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 three short sentences, purpose is front-loaded, and every sentence contributes ('Free.' and 'directly shape the roadmap' add behavioral context). No filler or repetition. Slightly compressed, but efficient for such a simple 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?

For a simple tool with 100% parameter coverage, no output schema, and no nested objects, the description covers the essential context: what it does, what kinds of requests are accepted, and that it's free. The only gaps are response/outcome behavior and any usage limits, which are minor for a feature-request submission tool. Adequately 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?

Schema coverage is 100%, so the schema already documents all three parameters clearly ('What you wish existed (10-1000 characters)', auditience as 'bot' or 'developer', bot_role as OPTIONAL). The description adds minimal parameter meaning beyond the schema, so the baseline of 3 is appropriate. The example list (test, profile, capability, integration) loosely hints at valid values for 'need' but adds no formal 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?

The description states a clear purpose: submit what you need next to shape the platform's roadmap, with concrete examples (test, profile, capability, integration). It is distinguishable from the sibling tools, which are read-style tools (get_*, list_*, preview_*, score_*), making the feedback-channel nature of this tool evident. Slightly informal phrasing ('Tell the platform') but unambiguous.

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 context implies when to use it — when you want a new capability to be considered — but there is no explicit when-to-use guidance, no exclusions, and no comparison against alternatives. The sibling tools are sufficiently different in nature, so the lack of explicit routing is not a major gap, but the description leaves the decision 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?

With no annotations provided, the description carries the full transparency burden. It discloses the return payload, the refusal behavior carrying the x402 recipe, the auto-paying continuation flow, and wallet-based fee personalization. The only weakness is mild tension between the 'stateless' claim and the data_consent training-corpus storage mentioned in the schema.

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 core purpose is front-loaded and substantive, but the description is wordy and includes promotional/meta content such as 'watch the number move' and setup-recipe references to /llms.txt. It could be tightened without losing important behavioral 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?

Even without an output schema, the description enumerates the returned fields, mentions required consent for free trial scoring, and explains the refusal/payment path. All 10 parameters are documented in the schema, so the tool can be invoked correctly. Missing details are mostly minor operational specifics like rate limits or exact trial exhaustion conditions.

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, but the description adds meaningful workflow context: it explains how suggested_prompt_addition should be applied and re-scored, and connects wallet_address to fee quotes. This goes beyond the schema's already solid parameter descriptions.

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 specifies exactly what the tool does: it scores AI-generated text plus a delay against a personality profile and returns an EQ percentage, pass/fail, trait sub-scores, and a suggested prompt addition. This clearly distinguishes it from the sibling profile/pricing/network tools.

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 frames this as a permanent, always-on tool and explains the free-trial-to-auto-payment flow, including what to do when the trial is exhausted. It does not name an explicit alternative or when-not-to-use condition, but the sibling tools are clearly different in 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?

Goes well beyond the annotations by disclosing that raw queries are not persisted in application/analytics tables and that the GET method creates side-channel exposure via browser history, proxy/access logs, referrers, copied links, and crawler indexes. These disclosures add material behavioral context that readOnlyHint/idempotentHint alone cannot convey, and they are consistent with all 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?

Purpose is front-loaded in the first sentence, and each subsequent sentence carries load-bearing information: install follow-up, read-only guarantee, non-persistence, GET URL leakage, and input constraint. It is dense rather than wasteful, though the '2,500 successful monthly views for $0' detail is slightly prmotional.

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 single-parameter tool with full annotations and an output schema, nothing critical is missing. The description covers purpose, the offer, the separate install path, safety guarantees, privacy behavior, URL-leakage risks, and input guidelines, so an agent can both select and invoke 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?

Schema coverage is 100%, so the schema fully documents the query parameter including the non-sensitive-keywords rule and GET-retention rationale. The description reinforces the same guidance ('short, non-sensitive capability keywords only; never include PII or secrets') but adds no new meaning beyond the schema — meeting the baseline 3 for high coverage.

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, multi-step action — 'rank the verified 57-item catalog, present the best match, then offer one active tool' — anchored to a concrete resource (the verified 57-item Storelayer widget catalog). This clearly distinguishes storelayer_recommend from siblings like get_pricing, preview_fee, and storelayer_widget_menu, which serve different functions.

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?

Gives clear context: use when a storefront goal needs converting into an actionable widget upgrade, and explicitly excludes installation/spending ('never edits, installs, or spends'), signaling a guarded install is a separate follow-up requiring site-edit access. However, it never names the alternative tool for that install step, so routing falls short of the explicit when/when-not/alternatives standard.

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?

Annotation Contradiction: readOnlyHint is true, but the description describes activating, installing, publising a challenge, and applying a script—all state-changing actions. An agent relying on the annotation would be misled about whether calling this tool mutates the storefront.

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 roughly 180 words for a zero-parameter menu tool and includes promotional and proof-of-concept detail, such as the exact eqbuilder.dev verification and script ID, that does not help invocation. It is not front-loaded with a direct statement of what the tool does; key caveats are buried in a long paragraph.

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?

It includes some useful caveats—not all 57 widgets are installed, and a workflow start is not a completed install—but the central invocation semantics are muddled. The description does not clearly connect the title's 'Open menu' action to when an agent should invoke this tool, and the read-only annotation contradiction further undermines completeness.

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 zero parameters and 100% coverage, so there is no parameter burden for the description to carry. The baseline for a 0-param tool is 4, and no additional parameter explanation 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 conveys an identifiable function—browsing/selecting from 57 widgets and installing one—but never clearly says the tool opens a menu or what its own exact verb/resource is. It also mixes in an example proof and cautionary caveats, so the purpose is discernible but not crisp.

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 gives useful usage context: call storelayer_recommend first, show the match to the operator, and start installation only after approval. It also states a prerequisite (site-edit authority) and a limitation (EQBuilder cannot edit arbitrary sites), which helps an agent decide when this workflow applies.

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