Hive Morph

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

The description discloses that counterparty data is anonymized via hashing and that no authentication is required, which are critical behavioral traits for an agent. The 'Read' verb clarifies it is non-mutating, though it doesn't detail pagination or error behavior. This is adequate given the lack of 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?

The entire description is a single sentence that packs purpose, fields, privacy handling, and auth requirements. No filler words or repetition.

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 read tool with one parameter and no output schema, the description covers the key aspects: what it reads, what fields are returned, and important caveats (hashing, no auth). It does not explain 'polymorphic audit log' but the sibling tool names and context make it sufficiently clear.

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 only parameter, limit, is fully documented in the schema with default and max values, achieving 100% schema coverage. The description adds no additional parameter-level detail, so the baseline score of 3 applies.

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 opens with 'Read recent rows from the polymorphic audit log' which clearly specifies the verb (read), resource (audit log), and scope (recent rows). It also lists the specific fields returned, distinguishing it from sibling tools like morph_brood_all that likely return broader brood data.

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 usage for retrieving recent audit log entries, with 'recent' suggesting time-bound reads. It doesn't explicitly name alternatives or state when not to use, but the specific resource and field list provide enough context for an agent to select it appropriately.

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 carries the full burden. It explicitly discloses the tool is READ-ONLY and 'does not actually cull,' which is the key safety-relevant behavior. It also outlines the culling criteria, providing useful context. However, it doesn't describe return format or whether repeated runs are side-effect free beyond the no-cull guarantee.

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 sentences, front-loaded with the action and resource, then the criteria and the READ-ONLY warning. Every word earns its place, with no redundant or vague phrasing.

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 parameters and no output schema, the description adequately covers the tool's purpose, criteria, and safety behavior. It explains the outcome in terms of 'which brood variants the system would cull,' which implies the output is the list of variants. The simplicity of the tool means no further detail is needed.

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 empty. The description adds meaning by explaining what the scan does and what it evaluates, compensating for the lack of parameter detail. Baseline is 4 for zero params.

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 'Run' and clearly identifies the resource as the 'auto-cull dry-run scan,' explaining its function (which brood variants would be culled) and the criteria (conversion floors, ROI thresholds, saturation). It also distinguishes from a real cull by explicitly stating it's a dry-run and READ-ONLY.

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 previewing cull decisions without executing, but it does not explicitly state when to use this tool over siblings like morph_spawn_monitor_scan or any alternatives. The READ-ONLY nature suggests a preview context, but no direct guidance or exclusions are given.

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 implies a read-only operation ('List') but does not disclose potential large result sizes, pagination, rate limits, or permissions. For a simple list 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?

Two sentences with no filler. The first sentence states exactly what it does; the second adds practical usage context. Every word 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?

For a zero-parameter, no-output-schema list tool, the description is sufficient. It explains the scope and use case, though it does not specify the return format, which is acceptable given the tool's simplicity.

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 provides complete coverage. The baseline for 0 params is 4; the description adds no parameter details but also needs none.

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 ('List') with a clear resource ('all brood variants') and scope ('across every supermodel'). This distinguishes it from siblings like morph_brood_for_supermodel, which targets a single supermodel.

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?

Provides clear context with 'Useful for fleet-wide population scans,' indicating when to use it. Does not explicitly mention alternatives or exclusions, but the sibling tools make implied contrasts.

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?

There are no annotations, and the description does not disclose any behavioral traits such as read-only nature, data freshness, permissions, or side effects. It simply lists columns, assuming the agent understands it's a read query.

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 efficiently lists the table's columns. It is concise and front-loaded with the key phrase 'Per-variant conversion table'.

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 description explains the return values (columns) but lacks context on scope, such as whether it covers all variants or is filtered, and fails to mention any limitations or usage caveats. Given no output schema, it covers the essentials but leaves room for ambiguity.

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 baseline is 4. The description adds value by listing output fields, but since there are no parameters, parameter semantics are minimal.

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 providing a per-variant conversion table with specific fields, distinguishing it from the leaderboard sibling by indicating it's the full table rather than a ranking. However, it lacks a verb like 'list' or 'return', making it less explicit about the action.

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 is provided about when to use this tool versus the sibling tools such as morph_brood_conversion_leaderboard or morph_brood_all. The description only states what the table contains, leaving the agent to infer the appropriate use case.

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 of disclosure. It reveals the tool drives auto-cull/promote signals, which is important behavioral context. However, it does not state whether the tool is read-only, whether it directly triggers actions, or any data limitations, so transparency is moderate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core function and followed by the downstream purpose. There is no redundancy or filler; every word adds value.

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 is simple (no parameters, no output schema), and the description covers what it returns, the ranking metric, and its role in the system. A brief mention of the output fields would improve completeness, but for a leaderboard this is largely intuitive. Given the low complexity, the description is sufficiently 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 and the schema is empty, so schema coverage is trivially 100%. The description does not need to explain parameters, and the baseline of 4 applies. No deductions are 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 states the tool produces a conversion leaderboard across all brood variants, ranked by settle rate × revenue. This distinguishes it from siblings like morph_brood_conversion (likely for a single variant) and morph_brood_all (general listing). The function is specific and actionable.

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 use for comparing brood performance to inform cull/promote decisions, but it does not explicitly state when to use this tool over alternatives. No when-not guidance or alternative tool names are mentioned, leaving the agent to infer context.

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. The verb 'List' indicates a read-only operation, and the example adds context, but the description does not disclose return format, error behavior, or what qualifies as a brood variant.

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, front-loaded with the action, no filler. The example is concise and useful.

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 a simple one-parameter list tool with no output schema, the description is adequately complete. It states what is listed and for whom, though it omits details about the result set.

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 covers 100% of the single parameter, and the description adds an example ('MONROE / W1') and reinforces 'specific supermodel', going beyond the schema's generic 'name or id'.

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 ('List') with a clear object ('brood variants') and scope ('for a specific supermodel'), with an example value. This distinguishes it from sibling tools like morph_brood_all, which apparently covers all broods.

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 use when you have a specific supermodel in mind and gives an example of a valid identifier. It does not explicitly mention alternatives or exclusions, but the context is otherwise clear.

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 and explicitly discloses the read-only nature ('Read-only') and that approval operations are outside this tool's surface. This adds meaningful behavioral context beyond the name and input schema, though it does not mention response format or auth requirements.

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 exceptionally concise: two sentences that deliver the core function, the pending-approval scope, and a read-only disclaimer. Every word contributes value, and the primary verb is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters, no output schema, and a straightforward listing purpose, the description fully covers the key information needed to invoke it correctly. It clarifies the state (awaiting approval), the read-only behavior, and where approval actually happens.

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, and per the baseline rule for no parameters, a score of 4 is appropriate. The description adds no parameter-specific details because there are none to document.

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: 'List brood variants currently awaiting human/operator approval before promotion.' It uses a specific verb (List) and resource (brood variants) with a precise scope (pending approval), distinguishing it from sibling tools like morph_brood_all or morph_brood_conversion.

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 gives clear context for when to use this tool (to view pending approvals) and what not to do: 'approval itself is performed via the private hivemorph operator surface.' It implicitly tells the agent this tool is read-only and not for performing approvals, though it does not explicitly name alternative tools for other brood-listing needs.

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 that this is a read operation ('Read'), which implies it is non-destructive, but it does not provide further behavioral details such as authentication requirements, rate limits, or the format of the returned data. Given no annotations, this minimal disclosure carries the full burden.

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 front-loads the main action and includes all essential details (primary shape, verticals, contrails) with no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple with no parameters and no output schema, and the description covers the main purpose and the components being read. It could potentially elaborate on what 'contrails' are, but the description is adequate for the tool's simplicity.

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 description does not need to explain parameter meanings. The baseline of 4 applies since there is nothing to document.

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 specific verb 'read' and names the resource 'polymorphic carousel', explicitly listing the 7 verticals and contrails, which clearly distinguishes it from sibling tools like morph_audit_recent or morph_brood_all.

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 is provided on when to use this tool versus the many sibling tools. The description only states what it does without any context on when it would be preferred.

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 reveals the return shape (polymorphic envelope with four fields), which is helpful. However, it does not explicitly state that the operation is read-only, nor does it mention auth requirements or error behavior. For a lookup tool, the risk is low, but the description does not fully disclose side-effect potential or constraints.

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, efficient sentence that leads with the action ('Look up') and specifies the resource and return content. There is no redundancy or filler, making it highly concise 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?

Given the tool has only one parameter, no output schema, and no annotations, the description provides substantial context by listing the expected return fields. It covers the core purpose and outcome. It could additionally explain what happens for invalid IDs or not-found cases, but for a simple get lookup, the coverage is strong.

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% because the only parameter (mii_id) is described as 'MII identifier'. The description does not add further semantic detail about the parameter, such as format, examples, or constraints. Baseline 3 is appropriate since the schema handles parameter documentation adequately.

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: 'Look up a Morph Identity Index (MII) record.' It uses a specific verb ('look up') and resource ('MII record'), and further specifies the return content (capabilities, shape, supermodel parent, trust score). This distinguishes it from sibling tools like morph_get_supermodel, which targets a different resource.

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 looking up MII records but provides no explicit guidance on when to use it versus alternatives. There is no mention of exclusions or when to prefer other tools like morph_get_supermodel. The intended usage is clear from the resource type, but explicit differentiation is absent.

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. 'Fetch' conveys a read-only operation, and the return field list adds context, but it omits details about permissions, side effects, or error behavior. It is adequate but not rich in 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 two concise sentences, front-loaded with the purpose and followed by a list of return fields. Every word earns its place, with no redundancy or 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 simple, one-parameter fetch tool with no output schema, the description adequately specifies both the input format (name or id with examples) and the return contents (role, lane, lead-shape, tagline, address, brood conversion summary). It provides sufficient context for an agent to 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% for the single parameter (name_or_id), and the description repeats examples already present in the schema description. It adds no new semantic information beyond the schema, so the baseline 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 clearly states the tool fetches a single supermodel by name or id, with concrete examples ('MONROE', 'W1'). The word 'single' distinguishes it from sibling tools like morph_list_supermodels, providing a specific verb, resource, and 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?

The description implies usage for individual supermodel lookups by name or id, but it does not explicitly contrast with alternatives like morph_list_supermodels or state when not to use it. No exclusion or alternative guidance is given, making it merely implied.

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 rate limits and pricing ('Free up to 100 calls/day per agent-DID; $0.001/call thereafter via x402 USDC settlement on Base') and describes the returned field set. It stops short of specifying the exact return format or any pagination behavior, but for a simple list tool the coverage is solid.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: the first front-loads the action and outputs, the second adds context and cost details. No filler or 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?

For a no-parameter, no-output-schema list tool, the description is complete: it names the full set of items, the fields returned, its purpose in the lineage system, and cost/rate-limit constraints. Nothing essential is missing.

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 baseline is 4. The description adds clarity by naming the implicit scope (all supermodels W1–W19), but there are no params to explain.

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 'List' plus the exact resource 'all supermodels (W1 through W19)' and enumerates the returned fields (id, name, role, lane, lead shape, tagline). This clearly distinguishes it from sibling tools like morph_get_supermodel, which likely targets a single supermodel.

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 states the directory is 'the canonical lineage for every spawned brood variant,' which gives clear context for when the tool is relevant. However, it does not explicitly name when to prefer this over siblings or list exclusions, so it falls short of a 5.

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 discloses the output fields and mentions the 'most recent settlement window' context, but it does not explicitly state whether the operation is read-only or if there are side effects, permissions, or rate limits. 'Probe' implies read-only, but it is not 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?

The description is a single, front-loaded sentence that immediately states the action and resource, then enumerates the specific components returned. Every word earns its place with no 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?

For a zero-parameter tool with no output schema, the description is complete. It clearly states the time window ('most recent settlement window') and exactly what histograms and counts are produced, giving the agent a full picture of what to expect.

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 baseline is 4. The description does not need to explain any parameters; it focuses on the operation and output, which 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 a specific verb ('probe') and resource ('money-flavor classifier') and lists concrete outputs. It is clear, though it does not explicitly differentiate from the sibling morph_money_flavor_stats, so it misses a point for sibling distinction.

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 by describing what it does (probing the most recent settlement window), but it provides no explicit guidance on when to use this tool versus alternatives like morph_money_flavor_stats. No when/when-not or exclusions 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 full burden. It discloses the rolling 1-hour window and the specific aggregates, but does not state whether the tool is read-only, potential side effects, or return format. The phrase 'default 1 hour window' hints at configurability contradicted by the empty schema, creating slight confusion.

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, information-dense sentence. It front-loads the main purpose, lists specific content, and includes the gate context with no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the key output dimensions and ties the tool to a specific business process (CLEAN-MONEY gate). Given the lack of an output schema, it is reasonably complete, though it could clarify the return format and the distinction from morph_money_flavor_probe.

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 zero parameters, so the schema is fully covered. The description's mention of a 'default' window implies a parameter that does not exist, which could mislead agents, but per the rubric for 0 parameters, baseline is 4.

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 computes rolling aggregate money-flavor statistics with specific dimensions (asset class, chain class, flow class distributions, ratios). The phrase 'Drives the CLEAN-MONEY gate' adds purpose context. However, it does not explicitly differentiate from the sibling tool morph_money_flavor_probe, only implying distinction through the word 'aggregate'.

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 the CLEAN-MONEY gate but gives no explicit guidance on when to use this tool vs. alternatives, nor any exclusions. It does not mention sibling tools like morph_money_flavor_probe, leaving the choice to the agent without clear criteria.

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 explicitly states 'READ-ONLY — does not actually spawn,' which is a key safety disclosure. It also explains the detection logic (conversion gaps, opportunity surface, saturation). However, it does not describe the return format or any other side effects, though the read-only nature is the most critical 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 two concise sentences. The first states the main purpose, and the second adds the crucial read-only note. No wasted words, and the structure is front-loaded with the verb.

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, read-only scan with no output schema, the description is fairly complete. It explains what the tool detects and that it is safe. The lack of return-value guidance is a minor gap, but the tool's name and the 'detect' wording imply the output is the eligible supermodels.

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 is empty. Per the rubric, the baseline is 4. The description does not need to explain any parameters, and it adds no param-related detail, which 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 clearly states the tool's function: 'Run the spawn-monitor scan: detect supermodels eligible for new brood spawns based on conversion gaps, opportunity surface, and saturation.' It uses a specific verb ('detect'), names the resource ('supermodels'), and distinguishes this scan from siblings like culling or approval 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 implies the tool should be used to check supermodel eligibility for spawns, but it does not explicitly state when to use it versus alternatives or provide exclusions. Sibling tools like morph_auto_cull_scan exist but are not referenced.

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