AINumbers Fintech Intelligence Suite

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

Annotations indicate read-only, idempotent, non-destructive. Description reinforces this by stating it returns findings and receipt, no network egress. Adds detail on derivation codes and schema cross-check, exceeding annotation basics.

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?

Four tight sentences front-loading purpose, scope, behavior, and exclusions. Every sentence delivers key 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?

Description covers all necessary aspects: input, processing, derivation codes, cross-check, output type, and what is excluded. Sufficient for an agent to select and invoke correctly without 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?

Single parameter 'credential' is described as ACDC/vLEI JSON object with expected subfields (v, d, i, ri, s, a, e, r). Schema coverage 100%, but description adds meaning about how these fields are processed (SAID recomputation, schema cross-check), which is not in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it recomputes SAIDs of ACDC/vLEI credentials under specific derivation codes and cross-checks schema SAID against a pinned table. It distinguishes this as a structural integrity check separate from trust/authority/revocation 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?

Explicitly lists what the tool does (structural SAID check) and what it does NOT do (KERI chain of trust, issuer authority, revocation). Mentions reuse of T553 module, giving context for expected behavior.

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 adds behavioral context beyond annotations by stating that inputs are 'processed transiently to compute the response and are not stored, logged, or retained' and that the tool is 'Deterministic'. This aligns with the readOnlyHint and idempotentHint annotations, providing additional safety assurances.

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 moderately concise but includes technical jargon and a URL, which may not be immediately useful. It front-loads the title and key concept, but the density of information could be streamlined for an AI agent.

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?

Despite the complexity (4 parameters, nested objects, no output schema), the description fails to explain what the adjudication result is or what the output artifact contains. It mentions an 'AP2 artifact' and 'execution_hash' but does not describe the return structure, leaving a significant gap for an agent.

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 repeats some parameter information (e.g., compute modes) but adds context about the overall processing flow, not deep parameter semantics. It does not substantially enhance understanding beyond 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 states the tool as an 'EMIR Trade-Repository Reconciliation Adjudicator' and an 'OpenChainGraph compute node', which clearly indicates its role in EMIR reconciliation. However, it does not differentiate from sibling EMIR tools like 'reconcile_emir_pairing' or 'validate_emir_trade_report', relying on the name for specificity.

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 a warning to 'Use synthetic or anonymised inputs only' and explains compute modes, but it offers no guidance on when to choose this tool over siblings or in what scenario it is appropriate. The usage context is only implied by the tool's name.

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

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral details: transient processing (inputs not stored/logged/retained), deterministic nature, and compute mode delegation. No contradictions with annotations were found.

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 relatively long and contains some redundancy (e.g., 'Deterministic OpenChainGraph compute node' appears twice). While it is information-dense, it could be more concise. The URL at the end may be extraneous for an AI agent.

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?

Despite covering compute modes, transient processing, and chaining, the description lacks details about the output format or what the aged breaks data contains. Since there is no output schema, the description should compensate by describing the AP2 artifact's structure, which it does not adequately.

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 baseline is 3. The description adds meaning by explaining the role of parent_hashes and parent_tool_ids in chain provenance and the purpose of policy_parameters as input for the decision function. This context goes beyond the schema 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 clearly states the tool is for 'EMIR Reconciliation Break Ageing' as an OpenChainGraph compute node that exports an AP2 artifact. It mentions consuming upstream artifacts from an adjudicator, linking it to the EMIR domain. However, it does not explicitly differentiate from sibling tools like 'adjudicate_emir_reconciliation' or 'reconcile_emir_pairing', missing some clarity.

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 includes a safety guideline ('Use synthetic or anonymised inputs only') and explains compute modes, but it does not specify when to use this tool over alternatives. No explicit context for selection among siblings is provided, making usage guidance implied rather than explicit.

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

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds critical behavioral context: client-side execution, no network calls, no PII, uses AIN Bridge for inputs, and renders an interactive widget. This exceeds the information in 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 description is front-loaded with the main purpose and capabilities. Slight redundancy on 'client-side' and 'zero PII' across two sentences, but remains clear and well-organized.

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 presence of an output schema, the description adequately covers purpose, constraints, input mechanism, and execution environment. The mention of 'export result as a Policy Mandate' provides sufficient context for return values.

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 sole parameter 'inputs' has a schema description (100% coverage). The description adds value by explaining it's a map of element IDs to values applied via AIN Bridge prefill, and referencing the manifest input_schema for details.

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 simulates agent payment policies with specific capabilities (spend caps, MCC allowlists, velocity throttles, approval thresholds). It distinguishes itself from siblings like 'simulate_spend_policy' by emphasizing it's a sandbox with an interactive widget and client-side execution.

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 specifies the tool is browser-based, client-side only, and zero PII, which implies it's safe for experimentation. However, it does not explicitly mention when to use this tool over alternatives like 'simulate_spend_policy' or when not to use it.

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?

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds important behavioral context: 'Inputs are processed transiently to compute the response and are not stored, logged, or retained.' It also mentions deterministic execution, AP2 artifact export, and chain provenance. No contradictions with 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 description is concise at 5 sentences, front-loaded with the core purpose. Every sentence adds meaningful information: purpose, compute behavior, data handling, provenance, and a link for more details. 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?

Given that it has no output schema, 4 parameters (0 required), and comprehensive annotations, the description provides sufficient context: it explains the chain graph context, compute modes, data handling, and gives a URL. However, it does not describe the output artifact structure beyond execution_hash, which could be improved.

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. The description adds some value for the 'compute' parameter by explaining its behavior, but for other parameters (parent_hashes, parent_tool_ids, policy_parameters) it relies on the schema descriptions. No detailed elaboration or examples beyond 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 it is a 'CBAM Precursor-Emissions Aggregator' and an 'OpenChainGraph compute node (compliance_mandate)' with deterministic behavior. It differentiates from siblings like 'calculate_cbam_embedded_emissions' by specifying its role as an aggregator in a chain with specific upstream/downstream artifacts. However, it could be more explicit about what 'precursor emissions' are, leaving some ambiguity.

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 guidance on compute modes: 'auto', 'server', 'browser', and explains when each is used (e.g., gpu:true nodes always delegate to browser). Also advises 'Use synthetic or anonymised inputs only.' However, it does not explicitly state when not to use this tool or compare it to sibling tools for alternative uses.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds critical context: 'Inputs are processed transiently to compute the response and are not stored, logged, or retained.' This significantly clarifies data handling behavior beyond annotations, with no contradictions.

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 tool's purpose and type. It is well-structured but somewhat dense with technical terms (e.g., 'OpenChainGraph compute node', 'cryptographic_mandate'), which may reduce clarity for some agents. Overall, every sentence contributes 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 description covers compute modes, transient processing, and artifact export. It lists upstream and downstream artifacts for chaining. However, it does not describe the response format or what the aggregated output contains, which is a gap given no output schema. The agent lacks information on the return value.

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 all parameters have descriptions in the schema. The description does not add meaningful new information about parameters beyond what the schema provides (e.g., compute modes are covered). Baseline 3 is appropriate; the description adds marginal value.

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 tool as an 'Agent-Action Audit-Trail Aggregator' and an 'OpenChainGraph compute node', clearly indicating it aggregates execution receipts for audit trails. It specifies it is deterministic and exports an AP2 artifact. However, it does not differentiate from numerous sibling tools with similar names (e.g., 'aggregate_ownership_50pct'), relying on context rather than explicit 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 explains when to use different compute modes (auto, server, browser) based on gpu and kernel availability, providing practical guidance. It also advises using synthetic or anonymised inputs only. However, it does not mention alternatives among the many sibling tools or specify when not to use this tool.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds significant behavioral context: inputs are transient and not stored, logged, or retained; execution modes (server-side on Cloudflare Workers or browser delegation) are explained; and deterministic nature is stated. This goes beyond annotations and enhances transparency.

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 dense paragraph covering many technical details (compute modes, Cloudflare Workers, browser delegation, data handling). While it is structured with a clear sequence, it could be more concise and front-loaded. Some details may be unnecessary for an agent to decide on invocation.

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 complexity (4 parameters, no output schema), the description lacks completeness. It explains compute modes and data handling but does not describe the output artifact's structure or content beyond mentioning an execution_hash. The core function (what the 50% rule aggregation produces) is not fully explained. A URL is provided for more info, but the description itself is incomplete.

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 mentions compute modes and references policy_parameters, but does not add substantial new meaning beyond the schema's own descriptions. The 'policy_parameters' field is left vague ('See the tool's manifest for field names'), which is a minor gap.

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 tool as an 'Ownership 50%-Rule Aggregator' and an 'OpenChainGraph compute node (compliance_mandate)', which hints at aggregation of ownership data under a 50% rule. However, it does not explicitly state what the tool computes or what the output represents, leaving the core purpose somewhat vague. The name and title provide partial clarity.

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 mentions upstream and downstream artifact dependencies (e.g., consumes from art-90, feeds art-92 and cry-05), giving pipeline context. However, it does not provide explicit guidance on when to use this tool versus its siblings, such as compute_cdd_ownership_25pct or other aggregate tools. No when-not-to-use instructions 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by stating determinism, transient input processing ('not stored, logged, or retained'), and output behavior ('exports an AP2 artifact with execution_hash for chain provenance'). These details enhance transparency beyond annotations without contradiction.

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 purpose and contains no superfluous sentences. It is slightly long (4 sentences) but each sentence adds essential information. Could be tightened by combining some points, but overall effective.

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 complexity (4 params, nested objects, no output schema), the description covers purpose, compute modes, data handling, and output artifact. It lacks error conditions or prerequisites, but includes a reference URL for further details. Adequately complete for an agent to use 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%, but the description adds significant context beyond schema definitions. It explains the compute mode behavior in relation to GPU nodes and browser delegation, and clarifies that policy_parameters are computed server-side for auto/server modes. This reduces ambiguity for parameter usage.

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 it is a 'Provable Reputation Score Aggregator' and an 'OpenChainGraph compute node', which identifies the verb (aggregate) and resource (reputation score). It distinguishes itself as part of a specific framework (OpenChainGraph with attestation mandate). However, it does not explicitly differentiate from sibling aggregation tools like aggregate_cbam_precursor_emissions or aggregate_execution_receipts.

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 usage context for compute modes (auto, server, browser) and advises using synthetic or anonymized inputs. However, it lacks explicit guidance on when to choose this tool over alternative aggregation tools or when to avoid it. The mention of 'attestation_mandate' implies a specific compliance context but is not fully elaborated.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. Description adds details: deterministic execution, transient processing (no storage), requirement for synthetic inputs, compute mode behavior, and output artifact with execution_hash. This provides useful context beyond annotations without contradiction.

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?

Description is front-loaded with purpose, then explains compute modes and data handling. It is reasonably concise but could be tighter by removing redundant phrases like 'OpenChainGraph compute node' 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?

Given the complexity (4 params, nested objects, no output schema), the description covers compute modes, input restrictions, output artifact, and downstream consumer. However, it lacks explanation of the aggregation logic and output format, which would help 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?

Schema coverage is 100%, so baseline is 3. Description adds some value by detailing compute modes, but does not explain parent_hashes, parent_tool_ids, or policy_parameters beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description states it's a Solvency II SCR Standard-Formula Module Aggregator and an OpenChainGraph compute node, which clearly identifies its domain and action. However, it does not explicitly specify what 'aggregating' entails (e.g., combining module results) and relies on the title for context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. It does not mention prerequisites, common use cases, or exclusions, despite multiple sibling tools with similar 'aggregate_' prefixes.

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 adds significant behavioral context beyond annotations: deterministic execution, compute mode handling, transient input processing, chain provenance, and the requirement to use synthetic inputs. Annotations already provide readOnlyHint, idempotentHint, destructiveHint.

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 informative but somewhat verbose with repeated phrases like 'Deterministic OpenChainGraph compute node'. It front-loads the purpose but could be more 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?

Despite describing execution mechanics, the description does not specify the output format or what the AP2 artifact contains, which is critical since there is no output schema. This leaves the tool's return value unclear.

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 baseline is 3. The description briefly mentions compute modes and references the tool's manifest for policy parameters but adds no new meaning beyond 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 as a 'Taxonomy KPI & Green Asset Ratio Aggregator' and specifies it is an 'OpenChainGraph compute node (model_governance)'. However, it does not explicitly differentiate from sibling tools like 'score_taxonomy_alignment'.

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 explains compute modes and internal mechanics but provides no guidance on when to use this tool over alternatives. No explicit 'when to use' or 'when not to use' information is 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?

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: deterministic execution, transient input processing (no storage/logging), artifact export with execution_hash for chain provenance, and compute mode behavior. This goes beyond annotations without contradicting them.

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 tool's core identity and is structured in logical flow: identity, compute modes, data handling, output, dependencies. Every sentence adds necessary information, though could be slightly more 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 the tool's complexity (4 parameters, nested objects, no output schema), the description covers key aspects: compute modes, transient processing, synthetic inputs, artifact export, and upstream dependencies. However, it does not detail the AP2 artifact structure or explain what IHB interest allocation entails, leaving some context gaps.

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 100% schema coverage, the baseline is 3. The description provides context for the compute parameter (explains enum values) and notes that policy_parameters are for the decision function. However, it relies on the tool's manifest for field names and does not add deep semantics beyond 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 it is an IHB Interest Allocation compute node for OpenChainGraph, specifying it is deterministic and explaining compute modes. It provides a specific purpose (allocation) and resource (ChainGraph compute node). However, it does not explicitly differentiate from sibling tools, though the specificity is high enough to be clear.

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 explains when to use different compute modes (auto, server, browser) based on gpu:false vs gpu:true and kernel registration. It also instructs to use synthetic or anonymised inputs only. However, it does not provide guidance on when not to use this tool or mention alternative tools for similar tasks.

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 provides extensive behavioral details beyond annotations: deterministic execution, compute mode options (auto/server/browser), transient data processing (not stored/logged/retained), and output as an AP2 artifact with execution_hash for chain provenance. It also warns to use synthetic/anonymized inputs. This fully discloses behavior, and no contradictions with annotations exist.

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 yet comprehensive, covering purpose, compute modes, data handling, output, provenance, and workflow context in a well-structured manner. Every sentence adds necessary 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?

The description covers purpose, behavior, parameters, and output (AP2 artifact with execution_hash). With no output schema, it explains the output's role. However, it could explicitly state the expected response structure or format for completeness, but the current detail is largely sufficient given the 4-parameter schema coverage.

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 describes all parameters. The description adds meaningful context for the compute parameter by explaining the modes and their implications. It also clarifies the role of policy_parameters (computed server-side for registered kernels). The mention of the output artifact and chain provenance adds value beyond 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 purpose: amortize commissions per ASC 340-40. It specifies it's a compliance-mandate OpenChainGraph compute node, distinguishing it from sibling tools like build_amortization_schedule or reconcile_commission_statement. The reference to the upstream artifact and the link further clarify its role in a workflow.

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 context by mentioning the upstream artifact (art-266-reconcile-commission-statement) and the compliance mandate, but it does not explicitly state when to use this tool versus alternatives. With a large sibling list including similar tools, explicit guidance would improve clarity.

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?

Beyond annotations (readOnlyHint:true, idempotentHint:true), the description adds crucial behavioral details: deterministic execution, compute mode behavior (server vs browser), transient processing with no data retention, and artifact export with execution_hash. This fully informs an agent about side effects and execution model.

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 well-structured with a clear purpose statement, compute mode explanation, privacy note, and output summary. It is slightly verbose with repeated 'OpenChainGraph compute node' phrase, but overall efficient and 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 no output schema, the description explains the artifact export and execution_hash, but does not describe the actual return value (e.g., cost-benefit metrics, format). For a decision-support tool, this is a notable gap in 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?

Schema coverage is 100% (all parameters documented in schema). The description adds minimal parameter value beyond the schema, mentioning compute modes generically and policy_parameters as 'input parameters'. Baseline 3 is appropriate since schema already does the heavy lifting.

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 it performs a cost-benefit analysis between Documentary Collection and Letter of Credit. The title and first line both specify the comparison, and the resource is uniquely identified. This distinguishes it from all sibling 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 does not provide guidance on when to use this tool versus alternatives. It explains compute modes but lacks context on scenarios where this analysis is appropriate or when other tools might 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value beyond annotations by detailing that inputs are transiently processed and not stored, and that the tool exports an AP2 artifact with execution_hash. No contradictions with 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 description is front-loaded with the core purpose, followed by key behavioral details. It is concise given the complexity, with each sentence serving a purpose. Some redundancy could be trimmed, but overall 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 4 parameters, nested objects, and no output schema, the description covers compute modes, transient processing, artifact export, and output feed. It adequately informs about prerequisites (synthetic inputs) and behavior, though lack of output format details is mitigated by the artifact mention.

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 baseline is 3. The description adds minor context, such as explaining that 'compute' modes affect where execution happens and that 'policy_parameters' are computed server-side for certain nodes. This adds some value but does not significantly enhance parameter understanding beyond 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 is a 'Prediction Market Analyzer' and an 'OpenChainGraph compute node (event_market_pnl)', which identifies its specific function. It distinguishes itself from siblings like 'find_prediction_arbitrage' by focusing on computation of PnL within the ChainGraph framework.

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 explicit guidance on when to use different compute modes (auto, browser, server) and notes that inputs should be synthetic or anonymized. However, it does not compare directly to sibling tools or specify exact scenarios for choosing this tool over alternatives.

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 confirms the idempotent and read-only nature ('Deterministic', 'inputs are not stored') and explains key behaviors like compute modes and browser delegation, adding value beyond annotations which already state readOnlyHint=true and idempotentHint=true. It lacks details on error handling or default behavior when no parameters are given.

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, with each sentence providing essential information. It is front-loaded with the core identity and purpose, followed by compute mode details, data handling, and output. No redundant or wasted sentences.

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 tool's purpose, compute modes, data privacy, and output artifact, which is adequate given the 4 parameters (all optional) and no output schema. It references a documentation URL for further details, but could be slightly more explicit about the response format or error states.

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%, and the description repeats some schema details (e.g., compute enum) without adding significant new meaning. It provides limited additional context beyond the schema, such as clarifying gpu:true behavior for compute, but does not enrich understanding of other parameters.

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 this is a 'Document Integrity & eIDAS Electronic Timestamp Anchor' and a 'Deterministic OpenChainGraph compute node', which precisely identifies its function. It differentiates itself from siblings like 'verify_timestamp_attestation' by focusing on anchoring rather than verification.

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 explains when to use different compute modes ('auto', 'server', 'browser') based on node type and gpu flag, and warns to use synthetic inputs only. However, it does not explicitly contrast with alternative tools for similar tasks, missing a clear when-to-use vs. alternatives statement.

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

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

Annotations claim no read-only, no idempotency, no destructiveness. The description elaborates meaningfully: success is only reported after independent confirmation of hash equality, failures return {ok:false, unanchored:true, reason} verbatim, never fabricating bindings, and the authority default is delegated/mirrored from anchor-suite. This is deep and valuable behavior disclosure beyond 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?

Well organized with clear cause-effect chains, but somewhat long — the failure-mode sentence and trust rationale could be tightened. Front-loads the core action first, which is good. No wasted 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?

Complex tool (real-time authority, batch anchoring, cryptographic verification, OCG shaping) with no output schema, yet the description fully explains the success/failure contract, the verification guarantee, the batch mechanics, and the calling context. An agent can predict exactly what happens in every path.

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 baseline is 3. The description adds meaningful semantics for execution_hash (single vs 2+ batch array, RFC 6962 Merkle root, per-hash merkle_inclusion) and authority (mirrors anchor-suite default). Only minor gap: array items lack 'sha256:' prefix documentation but this is minor.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear, specific action: timestamping execution_hash(es) via anchor-suite at a real timestamp authority, shaping results as OCG v0.7 section 20 anchor_bindings. Distinguishes from siblings (e.g., verify_execution_hash, verify_timestamp_attestation) by noting server-to-server vs browser relay and the explicit OCG shaping.

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?

Explains when this is called (caller-invoked only, not wired into automatic emission), the batch vs single path, and its trust posture. Lacks explicit when-not/alternative tool naming, but the caller-invoked note and clear scope give sufficient 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?

The description adds valuable behavioral context beyond the annotations: it confirms zero PII, client-side only, zero network, and explains it renders as a widget with inputs via AIN Bridge. Annotations already indicate readOnlyHint=true and destructiveHint=false, which are consistent. No contradictions.

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 four sentences and front-loads the core purpose. It efficiently conveys the transformation, execution model, and user link. Minor redundancy between the first and last sentences could be tightened, but overall it is appropriately sized.

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 complexity (nested input, output schema exists), the description covers the transformation, execution constraints (client-side, zero PII), and directs users to an interactive version. It addresses the main aspects for an agent to understand and use the tool, though it lacks detail on output structure (output schema not shown).

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 the single 'inputs' parameter, describing it as a map of element IDs to values. The tool description does not significantly add to that; it repeats 'inputs are applied via the AIN Bridge'. With high schema coverage, baseline is 3.

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 it translates AML/BSA controls, TM rules, and customer risk policy into a Policy Mandate JSON. It identifies itself as an 'anchor agentic tool for Cat-12'. However, it does not differentiate from sibling tools like 'build_google_ap2_mandate' which likely has a similar purpose, missing explicit 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 provides no guidance on when to use this tool versus alternatives. It mentions 'Cat-12' which implies a specific category but does not explicitly state when not to use it or how it compares to siblings like 'compile_work_mandate' or 'build_google_ap2_mandate'. Usage context is only 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?

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavior such as 'deterministic', transient processing without storage, and GPU delegation rules. It does not contradict annotations and provides meaningful additional context.

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

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

The description is well-structured, starting with the tool's title and core purpose, then detailing behavior and dependencies. It is informative without excessive verbosity, though slightly long.

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 key aspects: compute modes, transient processing, artifact export, and upstream/downstream links. However, it does not explain 'AP2 artifact' or the decision function in depth, which may be needed given the tool's complexity.

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?

Input schema covers 100% of parameters with descriptions. The description adds context for the 'compute' parameter but does not elaborate on 'parent_hashes' or 'parent_tool_ids' beyond what the schema provides. The baseline is 3 due to schema 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 clearly states the tool applies climate scenarios (NGFS / Fit-for-55) and identifies it as an OpenChainGraph compute node. It mentions upstream and downstream dependencies, distinguishing it from sibling tools that perform other specialized tasks.

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 guidance on compute modes (auto, server, browser) and conditions for server vs. client-side execution. It advises using synthetic or anonymized inputs. However, it does not explicitly compare with alternative tools or state when not to use this tool.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds beyond these: it states determinism, transient processing with no storage/logging/retention, and artifact export with execution_hash for chain provenance. No contradictions with 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 description is detailed but efficient, with each sentence contributing meaningful information about purpose, behavior, parameters, and provenance. It is well-structured with technical specifics, though slightly verbose in listing upstream/downstream artifacts.

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?

Despite lacking an output schema, the description covers input behavior, compute modes, transient processing, and artifact export. It references upstream and downstream artifacts for chain context, providing sufficient completeness for an agent to use the tool effectively.

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 descriptions for each parameter. The description adds context beyond the schema, explaining compute mode behavior (e.g., server-side on Cloudflare Workers, browser delegation for gpu:true), which enhances understanding of how to use the parameters.

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 an AI Addendum Assembler within the OpenChainGraph, specifying it as a compliance mandate compute node and linking to specific upstream/downstream artifacts. It distinctively mentions its role in chain provenance, though it does not explicitly differentiate from siblings like 'assemble_aiuc1_evidence_pack' or 'assemble_license_terms', which share a similar pattern.

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 usage constraints such as using synthetic or anonymised inputs only and explains compute modes ('auto', 'server', 'browser'), but lacks explicit guidance on when to use this tool versus alternative siblings or when not to use it. The context given is helpful but not comparative.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds valuable context: it confirms determinism, explains transient processing (inputs not stored/logged/retained), and describes the export of an AP2 artifact with execution_hash. This goes beyond the annotations and paints a clear picture of the tool's behavior. No contradictions with 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 description is well-structured and front-loaded with the purpose. Key details follow in a logical order: compute modes, transient processing, input warnings, output, and chain links. There is minor redundancy ('OpenChainGraph compute node' repeated twice), but overall it is efficient and clear for an agent.

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 complexity of four parameters (including nested objects) and no output schema, the description covers the essential aspects: compute modes, transient nature, chain provenance, and input sensitivity. It lacks details on the AP2 artifact's internal structure or hash derivation, but the read-only and idempotent annotations reduce the need. The description is largely complete for agent decision-making.

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% and the schema descriptions are adequately detailed (e.g., compute enum with mode explanations, policy_parameters referencing manifest). The tool description does not add significant new meaning beyond the schema, except for the general compute mode behavior already captured. Baseline 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 as an 'OpenChainGraph compute node (compliance_mandate)' that assembles an AIUC-1 evidence pack and exports an AP2 artifact. It specifies the verb (assemble), resource (evidence pack), and role in a chain. However, it does not explicitly differentiate from similar sibling tools like 'assemble_ocg_evidence_bundle' or 'build_226j_response_evidence_pack', which could cause confusion for the agent.

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 internal usage guidelines for compute modes ('auto', 'browser', 'server') and warns to use synthetic or anonymized inputs. It also lists upstream and downstream artifacts. However, there is no explicit when-to-use or when-not-to-use guidance compared to sibling tools, leaving the agent to infer context without clear alternatives.

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

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

Annotations indicate readonly and idempotent. The description adds that inputs are processed transiently (not stored, logged, retained) and that it exports an AP2 artifact with execution_hash. This goes beyond annotations by detailing data handling and provenance output, with no contradictions.

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 moderately concise, with each sentence adding new information. It starts with the tool name and purpose, then compute modes, data handling, chain context, and URL. It is well-structured but slightly dense; could be trimmed of minor redundancies like the repeated 'deterministic OpenChainGraph compute node'.

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 complexity (4 parameters, nested objects, no output schema), the description covers purpose, compute options, data transient nature, artifact export, and chain dependencies. It does not explain return values, but the artifact export is mentioned. Overall, it provides a solid contextual picture.

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 description adds limited value. It explains the compute modes and that policy_parameters are for the decision function, but this mostly paraphrases the schema. No additional parameter details like formats or constraints are provided beyond 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 it is a 'License Terms Assembler' and an 'OpenChainGraph compute node', specifying the verb (assemble) and resource (license terms). It distinguishes from siblings by referencing the specific chain context (compliance_mandate, upstream/downstream artifacts). However, it could be more explicit about the exact action performed on license terms.

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 explicit guidance on compute modes (auto, server, browser) and when each is used (e.g., gpu:true nodes delegate to browser). It advises to use synthetic or anonymized inputs only, and mentions upstream/downstream artifacts, giving context for chaining. It lacks explicit exclusions or alternatives but provides sufficient context for use.

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 adds significant behavioral context beyond annotations: deterministic computation, transient input processing (no storage), GPU delegation logic, and output of an AP2 artifact with execution_hash. Annotations already indicate readOnly and idempotent, so the description expands on safety and execution model.

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 dense paragraph with some redundancy (e.g., 'OpenChainGraph compute node' stated twice). It front-loads the title but mixes technical compute details with purpose. Slightly less concise than ideal; could be streamlined.

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, the description explains the output artifact type and its destination ('Output feeds: art-277-agreement-acceptance-binder'). It covers compute modes and input handling. However, it does not describe the artifact's content (e.g., NDA terms, fields included). Still, it provides enough context for an agent to understand the tool's role in a pipeline.

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?

All 4 parameters have schema descriptions (100% coverage), so the description adds minimal value for parameter meaning. It mentions compute modes and parent linking but does not elaborate on policy_parameters structure beyond referring to a manifest. Baseline 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 title 'Mutual NDA Composer' and description establish it as a tool for assembling mutual NDAs via an OpenChainGraph compute node. However, the description is heavy on compute node technicalities, and the purpose is not explicitly separated from sibling tools that also build documents (e.g., assemble_license_terms). Clear but lacks 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 provides usage constraints like 'Use synthetic or anonymised inputs only' and compute mode options ('auto', 'server', 'browser'). But it does not guide when to prefer this tool over alternatives (e.g., when to use a different NDA builder). No explicit when-not-to-use or intra-sibling comparison given the extensive 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?

The description adds significant behavioral context beyond annotations: it explains compute modes (auto, server, browser), server-side vs client-side execution, transient processing with no storage, and the export of an AP2 artifact. Annotations already indicate idempotentHint and readOnlyHint, which align.

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 relatively verbose with technical jargon (e.g., 'OpenChainGraph compute node (attestation_mandate)', 'gpu:false nodes', 'AP2 artifact'). It could be more concise and front-loaded with the core purpose.

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 complexity (4 params, nested object, enums, no output schema), the description explains compute behavior well but does not clarify what the output artifact contains or what 'tier labeling' means. More detail on return value would improve 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?

Input schema covers all 4 parameters with descriptions (100% coverage). The description adds minor context for policy_parameters (decision function, manifest) but does not significantly enhance understanding beyond 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 states it's an OpenChainGraph compute node that exports an AP2 artifact, indicating a specific verb (assemble) and resource (evidence bundle). However, it does not distinguish from sibling tools like assemble_aiuc1_evidence_pack or assemble_license_terms, which also assemble bundles.

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 lacks any guidance on when to use this tool vs alternatives. It does not mention prerequisites, when-not-to-use, or provide context about the tier labeling 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?

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable beyond annotations: inputs are processed transiently and not stored, the tool exports an AP2 artifact with execution_hash, and compute modes are explained in detail. This provides transparency about processing and output behavior that annotations alone do not cover.

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 fairly long and includes technical details about compute modes, transient processing, and upstream artifacts. It front-loads the core purpose but then dives into specifics. While informative, it could be more concise; for example, the compute mode explanation could be streamlined. The structure is adequate but not exemplary.

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 4 parameters with full schema coverage and no output schema. The description covers purpose, compute behavior, transient nature, output (AP2 artifact), and an upstream dependency. However, it lacks a clear explanation of the diagnostic output (e.g., what the readiness result looks like). For a diagnostic tool, describing the return format is important, and the description is insufficient in that regard.

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 schema itself documents all parameters. The description adds context for 'policy_parameters' by explaining they are computed server-side for certain conditions, but this is limited. Overall, the description does not significantly increase parameter understanding beyond the schema, so baseline 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 is an 'Agent Directory Publish Readiness Diagnostic' and identifies it as a deterministic OpenChainGraph compute node. The verb 'assess' combined with the specific resource 'agent directory publish readiness' provides a clear purpose. While it doesn't explicitly differentiate from siblings like 'assess_mica_casp_readiness', the unique name and technical context (compliance_mandate, upstream artifact reference) sufficiently distinguish it.

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 does not explicitly state when to use this tool versus alternatives, but it provides operational guidance: 'Use synthetic or anonymised inputs only' and explains behavior based on compute mode (auto/server/browser) and gpu flags. It mentions consuming upstream artifacts from a specific tool, implying a dependency chain. However, lacking explicit when-not conditions or alternative tool recommendations keeps it at 3.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that inputs are processed transiently and not stored, plus compute modes detail. No contradictions.

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?

Description is well-structured and front-loaded with purpose. Every sentence adds value, though dense. No wasted 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?

Covers output (AP2 artifact with execution_hash) and output feeds. However, policy_parameters field names are deferred to external manifest, and output artifact structure is not detailed. Adequate but incomplete.

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%, baseline 3. Description adds context on compute modes and purpose of policy_parameters, exceeding baseline by clarifying behavior beyond schema 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 clearly states the tool assesses EU AI Act conformity for credit-scoring, with specific regulatory deadline and context. It distinguishes from general compliance tools but does not explicitly differentiate from siblings.

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 context is implied: for credit-scoring high-risk obligations under EU AI Act. Provides input hygiene ('synthetic or anonymised inputs only') but no explicit when-not-to-use or alternatives.

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

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

Annotations declare the tool as read-only, idempotent, and non-destructive. The description adds substantial context: it is deterministic, inputs are processed transiently (not stored or retained), and it exports an AP2 artifact with execution_hash for provenance. This explains behavioral traits like side-effect freedom and data handling beyond the 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 description is relatively long and contains technical details about compute infrastructure (e.g., Cloudflare Workers, gpu:false nodes, registered kernel) that may be unnecessary for an agent to decide when to use the tool. While structured, it could be more concise by focusing on core functionality and usage rather than implementation specifics.

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 provides pipeline context (upstream/downstream artifacts) and input privacy warnings, which are helpful. However, it does not explain the return value beyond 'exports an AP2 artifact with execution_hash', leaving the agent unclear on what output to expect. Given the lack of an output schema, more detail on the artifact structure would improve 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?

With 100% schema coverage, the description adds modest value beyond the schema. It explains the compute enum behavior in detail (modes, delegation) and gives an example upstream artifact for parent hashes, but for other parameters like policy_parameters it merely defers to a manifest. Overall, the description provides some additional clarity but not significantly more than 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 tool name and title indicate it assesses circumvention diligence, but the description focuses heavily on compute infrastructure (OpenChainGraph, compute modes, transient processing) rather than clearly stating what the tool actually does. The purpose is implied but not explicitly defined, making it vague for an agent to understand the core functionality.

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 mentions consuming upstream artifacts from 'art-94-eccn-dual-use-classifier' and outputting to specific downstream artifacts, which gives pipeline context. It also advises using 'synthetic or anonymised inputs only.' However, it does not explicitly state when to use this tool over alternatives or any conditions of non-use, relying on implied chain 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?

The description discloses that inputs are processed transiently, not stored or retained, and that the node is deterministic. These details go beyond the annotations (readOnlyHint, idempotentHint) by explaining data handling practices. There is no contradiction with annotations; the description adds valuable behavioral context.

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

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

The description is detailed but not excessively long. It is front-loaded with the title and then systematically covers compute modes, data handling, and provenance. While some sentences could be tightened, the structure is logical and every sentence contributes useful 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?

Given the tool's complexity (4 parameters, nested objects) and the presence of annotations and output schema absence, the description covers purpose, input handling, compute modes, data transient processing, upstream artifact dependency, and artifact export. It lacks explicit return value details but mentions 'Exports an AP2 artifact', which is sufficient for an assessment tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining compute modes in detail (e.g., 'auto' computes server-side for gpu:false nodes with registered kernels) and that parent_hashes are for chaining provenance. For policy_parameters, it defers to the manifest but still provides context. This adds enough value to raise the score to 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 title and description clearly indicate it assesses CRA vulnerability reporting readiness per Article 14. The description mentions it is a deterministic ChainGraph node that exports an AP2 artifact with execution hash. While it doesn't use a simple verb+resource structure, the purpose is clear and distinguishes from siblings like 'assess_mica_casp_readiness' by focusing on CRA and referencing the specific upstream artifact.

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 guidance on compute modes (auto, server, browser) and warns to use synthetic or anonymised inputs only. It explains when server-side vs browser execution occurs. However, it does not explicitly compare to alternative tools or state when not to use this tool. The guidance is present but incomplete for decision-making among siblings.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful context: inputs are transient and not stored; compute modes and delegation behavior are explained; deterministic nature and artifact export are mentioned. No contradiction with 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 description is informative but contains redundancy (e.g., 'OpenChainGraph compute node' repeated). It front-loads the title but could be more 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?

No output schema is provided. The description mentions exporting an 'AP2 artifact with execution_hash' but does not describe the content of the artifact (e.g., health/liquidation results). For a monitoring tool with nested objects, the output is underspecified.

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 baseline is 3. The description adds little extra meaning beyond the schema; 'policy_parameters' remains vague ('Input parameters for this tool's decision function').

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 title 'DeFi Lending Health and Liquidation Monitor' clearly states the domain and resource type. The description expands on the compute node nature and data handling, but does not explicitly differentiate this tool from other assess_* siblings aside from naming. It's clear what the tool is about, but lacks 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?

Provides guidance on compute modes and input handling ('Use synthetic or anonymised inputs only'). However, it does not explain when to prefer this tool over alternatives, such as other assess tools or similar monitoring functions. No exclusion criteria or context for selection.

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 provides extensive behavioral context beyond annotations: deterministic execution, transient input processing with no storage/logging, compute mode implications, artifact export with execution_hash, and the requirement to use synthetic/anonymised inputs. There is no contradiction with annotations (readOnlyHint true, etc.).

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 well-structured, starting with the tool's purpose, then explaining compute modes, safety notes (transient processing, anonymised inputs), and output details. It is dense but each sentence provides necessary context. Minor redundancy with schema descriptions, but overall efficient.

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 covers execution mechanics well, it lacks detail on the assessment output (e.g., what the AP2 artifact contains beyond execution_hash) and the expected structure of 'policy_parameters'. Given no output schema, the description should more clearly specify the return value. The link to a webpage partially compensates.

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 detailed descriptions, especially for 'compute' and 'policy_parameters'. The description adds context about Cloudflare Workers and delegation URLs for compute modes, but does not significantly deepen understanding of 'parent_hashes', 'parent_tool_ids', or 'policy_parameters' beyond the schema. 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 is for ISO 42001 AIMS clause conformance, identifying it as a specific assessment tool. However, it mixes implementation details (compute modes, transient processing) with the core purpose, which slightly dilutes clarity. The verb 'assess' is implied, and the resource is well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus its many siblings (e.g., assess_ai_act_conformity, assess_mica_casp_readiness). The description focuses on how the computation works (server/browser) rather than decision criteria for selection. The deterministic nature is noted but not positioned as a differentiator.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: inputs are processed transiently and not stored/logged/retained, the tool is deterministic, and compute modes affect execution location (server vs. browser). This goes beyond the annotations without contradiction.

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 moderately concise at 6 sentences, front-loading the purpose. However, it includes repetitive details (e.g., 'Deterministic OpenChainGraph compute node' appears twice) and a URL that is extraneous for tool selection. It could be trimmed for better clarity.

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, the description explains the output ('Exports an AP2 artifact with execution_hash for chain provenance'), upstream/downstream artifact links, and compute mode behaviors. It covers inputs, processing, and return format fairly completely for a tool with 4 parameters and nested objects.

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 baseline is 3. The description provides additional context for the 'compute' parameter (default server-side for gpu:false nodes with registered kernel) and policy_parameters (decision function inputs, server-side computation conditions). However, for parent_hashes and parent_tool_ids, the description adds no new meaning beyond 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 explicitly states the tool's purpose as an 'MAR-Crypto Surveillance-Readiness Assessor' and identifies it as an 'OpenChainGraph compute node (compliance_mandate)' with a clear verb-resource combination. It distinguishes from siblings like 'assess_mica_casp_readiness' by specifying the exact regulatory scope (MAR-Crypto).

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 explicit guidance on when to use this tool versus sibling tools is provided. The description mentions upstream and downstream artifact dependencies, hinting at workflow placement, but lacks comparative context or when-not-to-use instructions. The only usage hint is 'Use synthetic or anonymised inputs only.'

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

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

Annotations already indicate readOnlyHint, idempotentHint, and no destructiveness. The description adds valuable context: inputs are transient and not stored, the tool is deterministic, and it exports an AP2 artifact with execution_hash for provenance. No contradiction with 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 description is well-structured with a clear purpose statement, compute mode details, data handling, and chain context. However, it is somewhat lengthy and includes technical jargon that could be streamlined.

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 complexity, the description covers purpose, compute behavior, data privacy, and chain integration. It does not explain the output format explicitly but references artifact export. The nested parameter is a minor gap.

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 description adds limited value. The 'policy_parameters' object is described incompletely ('See the tool's manifest'), and the description does not clarify its fields. Comput modes are detailed, but overall parameter semantics are not enhanced beyond 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 identifies the tool as a 'CASP Authorization-Readiness Assessor' for OpenChainGraph, with a specific compliance mandate under MiCA. It distinguishes itself from sibling assess tools by referencing specific upstream and downstream artifacts.

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 explains compute modes and advises using synthetic or anonymised inputs, but does not explicitly compare against sibling assess tools or specify when to use this tool over alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context about transient processing, no storage, and artifact export. This adds some value but does not contradict annotations. However, it omits behavioral details like error handling or output interpretation.

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 verbose, containing technical details about compute modes and Cloudflare Workers that may be excessive for quick comprehension. It is front-loaded with the name but later sections could be trimmed for clarity.

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 complexity (4 params, nested objects, no output schema), the description covers compute modes and transient processing but fails to explain the output artifact's content or how to interpret the validation status result. This leaves a gap in 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?

Schema description coverage is 100%, so the schema already documents all four parameters. The description touches on 'compute' mode but does not add meaningful new information beyond what the schema provides. 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 identifies the tool as a 'Model Validation Status Assessor' and a compute node, but does not explicitly define what 'model validation status' means or what specific assessment it performs. The purpose is implied by the name but not clarified in the description.

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 details on compute modes and transient processing but offers no guidance on when to use this tool versus its numerous sibling assess tools. It lacks explicit when-to-use or when-not-to-use 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?

The description goes beyond annotations by detailing deterministic behavior, compute mode selection (auto/server/browser), transient processing (inputs not stored/logged/retained), requirement for synthetic inputs, artifact export with execution_hash, and upstream artifact consumption. Annotations only indicate readOnly, idempotent, and non-destructive; the description adds critical behavioral context.

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

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

The description is front-loaded with the purpose and then provides technical details concisely. It is slightly dense but each sentence adds necessary information. Could potentially be streamlined, but overall efficient.

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 complexity (4 parameters, nested objects, no output schema), the description covers compute modes, processing location, data handling policy, artifact export, and upstream dependency. It provides enough context for an AI agent to understand the tool's operation, safety, and integration requirements.

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 baseline is 3. The description adds value by explaining that policy_parameters are computed server-side for gpu:false nodes with registered kernel, and that browser mode returns delegation URL. This contextualizes parameter behavior beyond schema 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 clearly identifies the tool as 'NAIC AI Systems Program Readiness Assessment' and specifies it is an OpenChainGraph compute node with compliance mandate. The name and description distinguish it from other assess_* tools (e.g., assess_ai_act_conformity) by targeting NAIC AI Systems Program specifically.

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 explains compute modes and deterministic behavior but provides no explicit guidance on when to use this tool versus sibling tools (e.g., assess_iso42001_aims_conformance, assess_model_validation_status). It does not state prerequisites, when not to use, or which scenarios warrant using this assessment over others.

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?

Beyond annotations (readOnly, idempotent, non-destructive), the description adds that inputs are transient and not stored, that execution_hash is exported for provenance, and details compute mode behavior, including browser delegation. No contradiction with 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 description is structured and front-loaded with key information, but contains multiple sentences that could be more concise. Each sentence adds value, though the link and output feeds list are slightly verbose.

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 compute modes, data privacy, provenance, and output feeds. However, it lacks details on what the readiness assessment output contains (e.g., score, report), which is critical since 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?

Schema coverage is 100% with parameter descriptions. The description reiterates compute modes and vaguely mentions policy_parameters without adding specific field names. It adds some value but does not significantly enhance understanding beyond 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 it is a PSD3/PSR Readiness Checker and explains its role as an OpenChainGraph compute node with regulatory deadlines. However, it does not explicitly differentiate from sibling 'assess_*' tools, relying on the name for context.

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 technical instructions on compute modes and data handling (use synthetic/anonymised inputs), but offers no guidance on when to use this tool over alternatives like assess_mica_casp_readiness or other readiness checkers.

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 adds value beyond annotations by stating that 'Inputs are processed transiently to compute the response and are not stored, logged, or retained', and that it 'Exports an AP2 artifact with execution_hash'. This provides transparency on data handling and output. No contradiction with 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 description is well-structured with an opening statement followed by key details on compute modes, transient processing, and output artifact. Every sentence adds value, though it is slightly verbose but not wasteful.

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 complexity (compute modes, deterministic, AP2 artifact) and the absence of an output schema, the description covers essential aspects: compute modes, transient processing, input guidelines, and output artifact. It could be more specific about the response structure but 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?

Schema description coverage is 100%, so the description adds no additional meaning beyond the schema. It references 'See the tool's manifest for field names' for policy_parameters, which is generic and does not enrich parameter understanding.

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 title and description clearly state the tool is a 'Restaking Delegation and Slashing Risk Analyzer'. The description explains it is an OpenChainGraph compute node with deterministic execution, processing inputs transiently. This provides a clear purpose, though it does not explicitly differentiate from sibling tools with similar 'assess' prefixes.

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 usage context such as compute modes (auto, server, browser) and advises 'Use synthetic or anonymised inputs only'. However, it lacks explicit guidance on when to use this tool versus alternatives, nor does it state when not to use it.

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?

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds key behavioral details: inputs are processed transiently and not stored, compute modes affect execution location, and the output is an AP2 artifact. This enriches the agent's understanding of side effects and data handling.

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 structured with a clear title, followed by compute explanation and data handling policy. Each sentence contributes meaningful information, though it could be slightly more condensed without losing 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?

Given 4 parameters, nested objects, and no output schema, the description explains compute modes and data handling well but fails to specify the output structure or the decision function's return format. The 'policy_parameters' object is left vague, leaving the agent uncertain about expected input structure.

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%, providing baseline. The description adds context for 'compute' mode (server vs. browser delegation) and hints that 'policy_parameters' drive the decision function. While not exhaustive, it complements the schema 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 is a 'DSCSA Suspect/Illegitimate Product Quarantine Assessor' and 'OpenChainGraph compute node', specifying its role in assessing suspect product status. It distinguishes from siblings by naming, but does not explicitly contrast usage.

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 mentions inputs should be synthetic/anonymised and lists an upstream artifact ('art-113-saleable-returns-verifier'), implying a workflow. However, it lacks explicit guidance on when to use this tool versus alternatives or specific 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?

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds significant behavioral details: transient processing of inputs (no storage), dynamic compute modes (auto/server/browser) depending on gpu flag, and export of an AP2 artifact with execution_hash. This helps an agent understand side effects and execution environments.

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 structured with clear sections: tool type, compute modes, data handling, input guidelines, output description, and links. It is informative without being overly verbose, though it could be slightly shorter for quicker scanning.

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 compute modes, chaining, and transient processing well. However, it lacks explicit description of the output artifact's structure or what a 'TRAIGA assessment' actually contains. Given no output schema, the agent would benefit from a clearer statement of return value semantics.

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 baseline is 3. The description goes beyond the schema by explaining the 'compute' parameter's enum values in detail (including default behavior and gpu interaction). It also clarifies 'parent_hashes' and 'parent_tool_ids' for chain provenance. The 'policy_parameters' object is referred to the manifest, which is acceptable but not fully detailed.

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 tool as a TRAIGA Exposure Assessor and an OpenChainGraph compute node. It clearly states its role in compliance mandates and its deterministic nature. However, it focuses more on mechanism than a crisp verb+resource statement, and the exact output (exposure assessment) is implied rather than explicit.

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 usage context like compute modes and a warning to use synthetic inputs only. It mentions that output feeds into a specific downstream tool (art-314-traiga-safe-harbor-pack-builder), implying a chain context. However, it does not explicitly state when to choose this tool over its many siblings, such as 'build_traiga_safe_harbor_pack'.

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

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) are reinforced by description: 'Inputs are processed transiently...not stored, logged, or retained' and 'Deterministic'. Description adds context on compute delegation (gpu:true delegates to browser), client-side execution details, and privacy advice ('synthetic or anonymised inputs only'). No contradiction with 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?

Description is well-structured with a clear header and logical flow, but somewhat lengthy due to technical details and a URL. Not every sentence earns its place; the URL and some compute mode details could be condensed. It is front-loaded with the primary purpose.

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, the description partially compensates by mentioning exported AP2 artifact with execution_hash. It explains compute modes and pipeline dependencies. However, it does not describe the return format or the policy_parameters fields in detail (defers to manifest), leaving gaps for an agent to fully understand inputs/outputs.

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 baseline is 3. Description does not add significant meaning beyond schema: it reiterates compute modes and briefly explains policy_parameters as decision function inputs, but relies on external manifest for field names. No additional syntax or constraints provided.

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 it as a ViDA DRR Transaction Reporter and an OpenChainGraph compute node for compliance_mandate. It explains the deterministic nature and compute modes, but the business function of 'reporting obligation' is somewhat implicit in the technical pipeline context. It distinguishes from siblings through specific artifact IDs (consumes art-159, outputs art-161).

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 as part of a ChainGraph pipeline by stating upstream and downstream artifacts. It provides details on compute modes and processing scope, but does not explicitly state when to prefer this tool over alternatives or when not to use it. No comparison with sibling tools like assess_vida_recapitulative_migration.

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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds critical detail: inputs are processed transiently and not stored, gpu:true delegates to browser, and it exports an AP2 artifact with execution_hash for chain provenance.

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 informative but slightly verbose. It is front-loaded with the purpose and uses clear structure, but every sentence earns its place. Could be slightly more 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 4 params including nested objects and no output schema, the description covers behavior, compute modes, data handling, output (execution_hash), upstream dependencies, and provides a link for more info. It is fully sufficient for an agent to understand and invoke the 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?

All 4 parameters are described in the schema (100% coverage). The description adds context, particularly for 'compute' (explains the three modes) and 'policy_parameters' (describes their role in decision function).

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 explicitly states the tool is an OpenChainGraph compute node for 'ViDA Recapitulative Statement Migration' assessment, which is specific and distinguishes it from numerous sibling tools that start with 'assess_'.

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 explains compute modes (auto, server, browser) and when to use each, and advises using synthetic/anonymised inputs. However, it does not explicitly contrast with similar tools or state when not to use it.

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?

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses critical behaviors: deterministic execution, transient input handling ('not stored, logged, or retained'), compute-mode-specific client-side delegation, and AP2 artifact export with execution_hash for chain provenance. These add substantial transparency that annotations do not cover.

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 moderately concise but starts with a redundant phrase ('Bulk Disbursement Integrity: ...') that repeats the title. The compute-mode explanation is somewhat verbose and could be trimmed. No filler words, but the structure is not optimally front-loaded with the core purpose.

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?

Although the description covers operational details (compute modes, privacy, chain provenance), it lacks a clear statement of the tool's actual decision function or what 'bulk disbursement integrity' means. No output schema exists, and the description only mentions the AP2 artifact without detailing its structure. The provided URL offers an escape hatch but is not enough for an agent to fully understand the tool's semantics.

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% property coverage, so baseline is 3. The description reinforces the compute enum behavior and mentions policy_parameters as the 'decision function' input, but it does not explain what fields policy_parameters might contain or clarify parent_hashes/parent_tool_ids beyond their schema descriptions. It adds minimal value on top of 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 opens with 'Bulk Disbursement Integrity: OpenChainGraph compute node (attestation_mandate)', which restates the tool name without specifying a clear verb or core function. It never states what 'attest_bulk_disbursement_integrity' actually does (e.g., verifying or computing integrity over disbursement records), and it does not distinguish itself from sibling attest_* tools beyond the title.

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 technical guidance about compute modes (auto/browser/server) and a privacy warning to use synthetic or anonymised inputs, but it does not specify when to choose this tool over alternatives. No exclusions or explicit when-to-use scenarios are given, leaving the selection rationale to the agent's guesswork.

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 significantly expands on the annotations by disclosing that inputs are processed transiently and not stored/logged/retained, that compute can be server-side or browser-delegated, and that the tool is deterministic. It also mentions the export of an AP2 artifact with execution_hash, which helps the agent understand side effects and guarantees beyond the readOnlyHint and idempotentHint 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 description is fairly concise and front-loaded, but it repeats 'OpenChainGraph compute node' twice and includes a URL that may be unnecessary for tool selection. Each sentence adds some operational or privacy-related value, and the structure flows logically from identity to compute modes to data handling.

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 existence of an output schema and rich annotations, the description covers the essential behavioral aspects: compute execution locations, transient input handling, the synthetic/anonymised data requirement, and the artifact export. It does not mention preconditions or the precise meaning of an 'independence attestation', but the output schema likely clarifies the returned artifact.

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 documents all parameters exhaustively (100% coverage). The description adds context about compute mode semantics (e.g., 'auto' server-side vs 'browser' delegation) and reinforces that policy_parameters are computed server-side, but it does not detail parent_hashes or parent_tool_ids beyond what the schema provides. This is a reasonable baseline when the schema carries the load.

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 tool name and title clearly indicate an attestation for calculation-agent independence, and the description identifies it as a deterministic OpenChainGraph compute node. However, the actual operation — that it computes an attestation response and exports an AP2 artifact — is implied rather than explicitly stated. The meaning of 'attestation_mandate' and the specific independence criteria are left vague, so a new agent cannot fully grasp what it accomplishes.

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 some operational guidance (e.g., compute modes, browser delegation, and the warning to use synthetic or anonymised inputs), but it gives no explicit instruction on when to use this tool versus any of the many sibling tools. No alternatives or exclusions are mentioned, leaving the selection 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?

The description goes well beyond the annotations by disclosing that the tool is deterministic, processes inputs transiently, does not store/log/retain data, and exports an AP2 artifact with execution_hash. It also explains compute-mode side effects (server-side execution vs browser delegation URL). These are non-obvious behavioral traits that materially affect invocation, and there is no contradiction with the readOnlyHint, idempotentHint, or destructiveHint 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 description is information-dense but somewhat repetitive: 'OpenChainGraph compute node' appears in the first two sentences, and the trailing URL is not necessary for core invocation understanding. It is front-loaded with the title and compute-node context, but could be tightened into a more concise paragraph without losing important details.

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 compute behavior, privacy/transient processing, determinism, and artifact output, which is strong given the annotations and schema. However, there is no output schema, and the description only vaguely defines the AP2 artifact as containing execution_hash; it does not describe the full return structure. It also references a manifest for policy_parameters fields without embedding those details, leaving meaningful invocation gaps for a complex attestation 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 input schema already documents all four parameters with 100% coverage, so the baseline is 3. The description adds operational meaning to the compute parameter by explaining what auto/server/browser do and why gpu:true forces browser delegation. It also contextualizes policy_parameters as inputs to the decision function, though it does not list the actual field names, leaving some burden on the schema and manifest.

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 tool as a deterministic OpenChainGraph compute node with an attestation_mandate and says it exports an AP2 artifact with execution_hash, but it never plainly states what the daily reconciliation attestation verifies or what attestation means in this context. It relies heavily on the title and name, and does not differentiate itself from sibling attestation tools like attest_bulk_disbursement_integrity or check_agent_attestation.

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 concrete operational guidance: compute modes (auto/server/browser), the rule that gpu:true nodes delegate to the browser, and the instruction to use synthetic or anonymised inputs only. However, it does not state when a user should choose this tool over alternative attestation or reconciliation tools, nor does it describe prerequisites or expected use cases beyond the compute mechanics.

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 substantially exceeds the annotations by detailing deterministic behavior, server-side execution on Cloudflare Workers, browser delegation for gpu:true nodes, transient processing with no storage/logging/retention, and AP2 artifact export with execution_hash. This provides rich behavioral context beyond the read-only, idempotent, non-destructive hints.

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 reasonably sized and follows a logical flow: identity, compute modes, data handling, output, and a reference URL. It has minor redundancy (mentions 'OpenChainGraph compute node' twice) but is still efficient and 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 lack of an output schema, the description does explain the output artifact and data-handling guarantees, which is useful. However, it leaves the actual decision function and the meaning of 'attestation_mandate' underspecified, so an agent still lacks a clear picture of what the computation entails.

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 schema documents all parameters in detail. The tool description adds no new meaning for parameters beyond repeating compute-mode behavior already covered in the schema's 'compute' description. It does not elaborate on policy_parameters fields or parent_hashes usage beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies it as an 'OpenChainGraph compute node' for 'Member Margin Call Lifecycle' and mentions exporting an AP2 artifact, but it does not state a specific action verb or explain what the attestation actually asserts. It is not merely tautological, but the core purpose remains vague and indistinguishable from sibling attest_* 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 provides compute-mode selection guidance (server vs. browser) and warns to use synthetic inputs, but it never states when to use this tool or what alternatives exist. There is no comparison to sibling tools, and the 'when-not-to-use' guidance 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?

Annotations already declare readOnly, idempotent, and non-destructive. The description adds beyond annotations: it states the tool is deterministic, inputs are transient and not stored, and it exports an AP2 artifact with execution_hash. This provides rich behavioral context without contradicting 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 description is dense with technical details (upstream/downstream artifacts, compute modes, processing semantics). While front-loaded with the primary purpose, it includes verbose lists and could be more concise. Every sentence adds information, but brevity is sacrificed.

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 4 parameters, no output schema, and rich annotations, the description provides sufficient context: compute behavior, transient nature, artifact dependencies, and a URL. It covers most operational aspects an agent would need 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 description coverage is 100%, so baseline is 3. The description adds context for the 'compute' parameter by explaining modes, and for 'policy_parameters' by linking to the tool's manifest. However, it does not significantly extend understanding beyond 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?

Title and description explicitly state 'Auditor' and 'ACP/UCP Product-Feed Conformance'. The verb 'audit' and resource are clear, and the tool is distinct from siblings like 'validate_acp_checkout' due to its focus on conformance auditing.

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 guidance on compute modes (auto, server, browser) and advises using synthetic or anonymised inputs only. However, it does not clarify when to use this tool versus other similar tools (e.g., when to choose this over a validation tool), nor does it specify prerequisites or alternative tools.

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

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate safe behavior. The description adds valuable context: inputs are processed transiently and not stored, logged, or retained. It also states determinism and server-side execution options. This goes beyond annotations and provides useful behavioral insight.

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 moderately concise but includes technical clutter like URLs and detailed compute mode explanations that may not be necessary for a quick understanding. The first sentence is good, but the subsequent sentences are dense and could be trimmed. Every sentence earns its place, but some could be more direct.

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

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

With no output schema, the description partially compensates by mentioning the AP2 artifact and execution_hash. However, the core auditing logic (what exactly is checked?) is not explained. The tool is complex (4 params, nested objects) and could benefit from a brief explanation of the audit procedure. The reference to an external URL may not be accessible to the agent.

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 parameters. The description mentions the 'compute' mode and 'policy_parameters' but does not add new semantic meaning. For example, 'parent_hashes' and 'parent_tool_ids' are only restated. Baseline 3 is appropriate as the schema does the heavy lifting.

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 tool as an 'Agent Key Rotation Auditor' and mentions it's an OpenChainGraph compute node. The name is specific, and the description includes key details like determinism and output to another artifact (art-133). However, the heavy focus on compute modes rather than the core auditing function slightly dims clarity. Sibling tools like 'audit_acp_ucp_product_feed' suggest a family, but 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 advises 'Use synthetic or anonymised inputs only', which is a clear guideline, and mentions output feeds to another tool. However, it lacks explicit when-to-use or when-not-to-use statements, and no alternatives are provided. The context of being a compliance mandate is implied but not actionable.

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

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

Annotations already declare readOnly and idempotent. The description adds critical context: runs client-side, zero PII, zero network, renders interactive widget. This goes well beyond annotations, providing full disclosure of 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?

Two densely informative sentences with no wasted words. Front-loaded with the purpose, then usage, then behavioral details. Perfect 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?

Covers purpose, usage, behavioral traits, and parameter semantics. Output schema exists so return values need not be described. A minor gap: the term 'AINumbers tool' is not explained, but given the sibling 'list_ainumbers_tools', it's likely known. Overall very 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%; baseline 3. The description adds meaning by explaining that inputs come from the manifest input_schema and are applied via AIN Bridge prefill, clarifying the parameter's role beyond the schema description.

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 specific RFC validations (9728, 8707) and risk assessment (token-passthrough/confused-deputy), clearly distinguishing it from siblings like 'audit_mcp_tool_scope_revocation' and 'validate_mcp_authorization_metadata'.

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?

Explicitly states 'Use when a developer is securing an MCP server's authorization,' providing clear context. Could be stronger by explicitly noting when not to use it or comparing to related siblings, but the guidance is effective.

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 important behavioral traits: deterministic compute, transient input processing with no storage, and artifact export with execution_hash. This goes beyond the annotations (readOnlyHint, idempotentHint) by detailing the compute mode effects and data handling, with no contradictions noted.

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 well-structured, front-loading the tool's identity and purpose, then explaining compute modes and data handling. It is information-dense but could be slightly more concise; for example, the compute mode details could be summarized.

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 output artifact and feed destination, but lacks clarity on the core auditing function (e.g., what specific checks are performed, what triggers revocation). Without an output schema, more detail on the audit result would improve 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?

Schema description coverage is 100%, so the baseline is 3. The description adds some context for the compute parameter but repeats information already present in the schema for other parameters. No new semantic value is added beyond what is already in the input 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 identifies the tool as an auditor for MCP tool scope and revocation, part of a compliance mandate. It includes the specific context of 'OpenChainGraph compute node', which distinguishes it from generic tools, though it does not explicitly differentiate from sibling tools that also perform audits or validations.

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 guidance on compute modes and their behavior (auto, server, browser). It advises using synthetic or anonymised inputs only, and notes the output feeds into a specific validator. However, it does not explicitly state when not to use the tool or mention alternatives.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral context: browser-based, client-side only, zero PII, zero network, and renders the AINumbers widget. This goes beyond annotations and fully discloses execution environment and privacy characteristics.

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 long, front-loaded with purpose and dimensions, and each sentence earns its place. 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?

Despite the tool's complexity (10 dimensions, weighting matrix, client-side execution), the description covers purpose, dimensions, output (weighted comparison matrix and Markdown memo), execution environment, privacy, and integration details. With an output schema present, the description is fully adequate.

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% for the single parameter 'inputs', with a description that explains it is a map of input element IDs to values. The tool description adds context about how inputs are applied via AIN Bridge prefill, which enhances understanding of the parameter's role.

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 'Score and compare BaaS providers across 10 capability dimensions' with a specific verb ('score and compare') and resource ('BaaS providers'). It distinguishes from sibling tools implicitly as the only BaaS-specific comparator among many general comparison 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 implicitly indicates when to use (when comparing BaaS providers) but does not explicitly exclude alternatives or provide context for when not to use it. The context is clear but lacks exclusions or comparisons to similar tools.

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds significant context: deterministic computation, transient input processing (not stored/logged), compute mode details (auto, server, browser), and GPU delegation rules. No contradiction with 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 description is well-structured with key information front-loaded (purpose, compute mode, transient processing, provenance). Each sentence adds value, though it could be slightly more 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?

The description covers compute modes, input handling, and provenance, but omits specifics about the benchmark output (e.g., format, values). Without an output schema, the agent lacks details on return structure. Policy_parameters are not fully explained.

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 detailed parameter descriptions. The description adds value by contextualizing parent_hashes/parent_tool_ids with the specific upstream artifact (art-472-cbcr-builder) and mentioning compute mode behavior already covered in schema. The policy_parameters reference to 'tool's manifest' is vague but acceptable.

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 is a 'Transfer-Pricing Interquartile Range Benchmark' and an 'OpenChainGraph compute node' with a compliance mandate. It distinguishes itself from siblings by specifying its role in the ChainGraph ecosystem and its consumption of upstream artifacts from art-472-cbcr-builder.

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 within a compliance chain but does not explicitly state when to use this tool versus alternatives. It mentions consuming specific upstream artifacts but offers no guidance on selection criteria 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?

Annotations already provide readOnlyHint (true) and idempotentHint (true). The description adds valuable behavioral details: inputs are processed transiently and not stored/logged/retained, deterministic execution, and export of AP2 artifact with execution_hash. This goes beyond annotations and does not contradict them.

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 structured with key information front-loaded: identity, compute modes, data handling, security warning, output, upstream dependency, and a link. It is concise yet covers essential aspects without excessive verbosity, though some sentences could be tightened.

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 complexity (multiple parameters, compute modes, artifacts, chain provenance), the description provides adequate context for an experienced user: data handling, compute modes, output artifact, and upstream dependency. However, it lacks details about the exact return format (no output schema) and assumes knowledge of 'OpenChainGraph' and 'AP2 artifact', limiting completeness for new users.

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 parameters are fully documented in the schema. The description briefly mentions the 'compute' parameter mode switching and 'policy_parameters' as input for the decision function, but adds minimal extra semantic meaning beyond what the schema already provides. 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 identifies the tool as an 'Agreement Acceptance Binder' and an 'OpenChainGraph compute node (compliance_mandate)', specifying that it consumes upstream artifacts from a named tool and exports an AP2 artifact. This provides a specific verb (bind) and resource (agreement acceptance), but does not explicitly differentiate it from sibling tools like 'bind_attested_subject'.

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 explains compute modes (auto, browser, gpu:true) and warns to use synthetic or anonymised inputs only, which are usage guidelines. However, it does not state when to use this tool versus alternatives, nor does it explicitly mention when not to use it. The context of when to use is implied but not contrasted with siblings.

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 adds significant behavioral details beyond annotations: it explicitly states that inputs are processed transiently, not stored/logged/retained, and that the node is deterministic. Annotations (readOnlyHint, idempotentHint, destructiveHint) are consistent, and there is no contradiction.

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 succinct but not excessively concise. It front-loads the essential purpose and then details compute modes, data handling, and output. Every sentence adds value, though it could be slightly shorter 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?

Given the complexity (4 parameters, nested objects, no output schema), the description covers purpose, behavior, compute modes, input guidelines, and output nature (AP2 artifact with execution_hash). It lacks details on return values, but annotations and schema mitigate this. Overall, it provides sufficient context for an agent to use the tool 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?

With 100% schema coverage, parameters are well-documented. The description adds meaningful context: explains the purpose of 'parent_hashes' and 'parent_tool_ids' for chaining, and clarifies that 'policy_parameters' are inputs to a decision function and are computed server-side for certain modes. This adds value beyond the schema definitions.

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 as an OpenChainGraph compute node for binding attested subjects, specifying it exports an AP2 artifact with execution_hash for chain provenance. It distinguishes itself from siblings by being specifically about binding attested subjects and provides a link for further details.

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 explains the three compute modes ('auto', 'server', 'browser') with clear conditions (e.g., 'gpu:true nodes always delegate to the browser'), and advises using synthetic or anonymised inputs. It does not explicitly compare to sibling tools or state when not to use, but the context is adequate for the intended use.

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?

Beyond annotations (readOnly, idempotent, non-destructive), the description adds key behavioral traits: deterministic compute, transient input processing (no storage/logging/retention), and artifact export with execution_hash for chain provenance. This significantly aids agent understanding.

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 purpose and efficiently covers compute modes, privacy, and output. Slight redundancy (e.g., 'OpenChainGraph compute node' twice), but overall each sentence adds value. Could be marginally tighter.

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?

Covers purpose, privacy, and compute behaviors but lacks detail on the output artifact's structure (no output schema). For a tool with nested parameters and many siblings, this leaves some ambiguity for the agent.

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% (all 4 parameters documented). The description repeats the compute mode behavior already in the schema, adding no additional meaning. Baseline 3 is appropriate as the schema already does the heavy lifting.

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 builds a 226J Response Evidence Pack using OpenChainGraph compute node. The name and title are specific, but no explicit differentiation from sibling tools like 'assemble_ocg_evidence_bundle' is provided.

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 mentions using synthetic or anonymised inputs and consuming upstream artifacts, providing some usage context. However, it does not specify when to use this tool versus alternatives among hundreds of siblings, leaving the agent without clear selection 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?

Beyond annotations (readOnlyHint, idempotentHint), the description adds significant detail: deterministic behavior, transient processing (no storage or logging), compute mode behavior for server vs. browser, GPU delegation, and export of an AP2 artifact with execution_hash. No contradiction with 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 description is front-loaded with the core purpose and follows a logical structure. It is somewhat lengthy but every sentence adds necessary detail. Could be trimmed slightly, but overall well-organized.

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 complexity and absence of an output schema, the description covers key aspects: purpose, compute modes, transient handling, output artifact, and downstream linkage. It lacks explicit output format details but provides enough context for typical usage within a workflow.

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, but the description adds value by explaining the compute parameter's behavior in different modes, the role of parent_hashes and parent_tool_ids for chaining, and that policy_parameters are the decision function inputs. This enriches understanding beyond schema 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 clearly states the tool builds an adverse action notice and is an OpenChainGraph compute node for compliance mandate. It distinguishes from the sibling validate_adverse_action_notice by mentioning it feeds into that validation step. The verb and resource are specific and 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 description provides usage context: compute modes (server vs. browser), transient input handling, and that output feeds into a validation tool. It also warns to use synthetic or anonymised inputs. However, it does not explicitly state when to choose this tool over other build tools or excluding cases.

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?

Beyond annotations (readOnlyHint, idempotentHint), the description discloses that inputs are processed transiently and not stored, and that it exports an AP2 artifact with execution_hash for chain provenance. This adds significant behavioral context not captured by 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 description is somewhat lengthy and includes a URL at the end. There is minor repetition ('Deterministic OpenChainGraph compute node' appears twice). It could be more concise without losing core 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?

Given the complexity (4 parameters, nested policy_parameters, no output schema), the description covers compute modes, transient processing, and artifact export. However, it lacks details about the incident record structure or output content beyond execution_hash. The external link provides some context, but completeness is moderate.

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, so the schema already documents each parameter. The description adds no new information about parameters beyond summarizing compute modes. Baseline 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 identifies the tool as building an agent incident record using the OpenChainGraph compute node. The verb 'build' and resource 'incident record' are specific. However, it does not explicitly distinguish this tool from sibling build tools like 'build_agent_test_evidence' or 'build_ai_decision_log_record'.

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 guidance on compute modes (auto, server, browser) and cautions to use synthetic or anonymized inputs. However, it does not explain when to use this tool over alternatives or provide exclusions. The context of compliance mandate is implied but not explicit.

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

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

Annotations indicate readOnly, idempotent, non-destructive. The description adds transparency about determinism, transient processing (inputs not stored/logged/retained), and AP2 artifact output with execution_hash. This goes beyond annotations, providing valuable behavioral insight.

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 tool's role and key characteristics. It is informative but slightly verbose; each sentence adds value (compute modes, data handling, output). Could be trimmed slightly 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?

For a tool with 4 parameters (including nested policy_parameters) and no output schema, the description covers: role in ChainGraph, determinism, compute modes, data privacy, output artifact type, and a reference URL. It adequately prepares the agent for invocation and chaining.

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 parameters are already documented. The description adds overall context (e.g., compute modes, 'See manifest for field names') but does not significantly enhance individual parameter meaning beyond 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 identifies the tool as a 'Quarterly Agent Test Evidence Composer' for OpenChainGraph, stating it is deterministic and computes evidence. It distinguishes itself from siblings by specifying its compute node nature and AP2 artifact output, but does not explicitly differentiate from many similar 'build_*' 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 provides usage context (use synthetic/anonymised inputs, compute modes) but lacks explicit guidance on when to choose this tool over alternatives or when not to use it. Among numerous sibling tools, no comparative selection criteria 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?

The description aligns with annotations (e.g., idempotent, read-only) and adds significant detail about the compute modes, server-side vs browser execution, and transient input processing. It goes beyond what annotations provide by explaining the artifact export and chain provenance.

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