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

Discloses deletion behavior, dry_run preview mode, and soft-delete for memory; no annotations to contradict.

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?

Concise, front-loaded, no unnecessary words. Every sentence adds value.

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

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

Given no output schema, description explains return values (counts) and covers key aspects like dry_run and memory cleanup.

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

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

Schema covers both parameters with descriptions; the description adds context (dry_run preview, cleanup_memory soft-delete) 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?

Description clearly states the tool deletes expired receipts based on metadata.expires_at, distinguishing it from other tools like create_receipt or list_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?

Explicitly mentions periodic use for storage management and TTL enforcement, but does not specify when not to use or provide 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?

No annotations provided, so description carries burden. Discloses that receipt is re-signed with Ed25519 and linked via parent_receipt_id, and returns certain fields. However, does not fully describe side effects or whether the operation is final/immutable.

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 four sentences, with first sentence stating purpose, followed by details of what it records, a technical detail, and usage instruction. It is concise and front-loaded, though could be slightly 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?

No output schema, but description explains returns (judgment receipt ID, verdict, score, chain ID). Covers technical detail (Ed25519 signing, parent_receipt_id). Explains that it finalizes a pending receipt. Lacks error conditions or idempotency but sufficient for a workflow completion 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 has 100% description coverage, so baseline is 3. Description mentions parameters (verdict, score, criteria, confidence) but does not add significant detail beyond schema. The description adds process context (finalize, re-sign) but not new parameter semantics.

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

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

The description clearly states it submits evaluation results to finalize a pending judgment receipt, with specific verb 'complete_judgment' and resource. It distinguishes from sibling judge_receipt by mentioning 'finalize a pending judgment receipt created by judge_receipt' and 'Use immediately after evaluating the prompt returned by judge_receipt'.

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 says 'Use immediately after evaluating the prompt returned by judge_receipt', indicating the correct usage context. It does not explicitly mention when not to use, but the context of pending receipt implies a specific workflow step.

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

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

With no annotations, the description carries the full burden. It discloses behavioral traits: updates status, re-signs with Ed25519, and cannot complete an already-completed receipt. It does not mention potential side effects or immutability, but the disclosed traits are sufficient for the tool's purpose.

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

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

The description is three sentences long, each serving a distinct purpose: stating the action, providing usage context, and adding constraints/return info. It is front-loaded with the key verb and resource, with no superfluous 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?

Given 13 parameters (schema-documented), no output schema, and no annotations, the description covers the core functionality, usage context, constraints, and return value. It could mention the optional parameters for detailed tracking, but the schema covers them, so completeness is 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%, so the baseline is 3. The description does not add meaning beyond the schema, merely stating the tool records results and outputs. No param-specific enrichment is provided, but the schema already handles parameter semantics well.

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 verb 'Finalize' and resource 'pending receipt', detailing that it records execution results, costs, output data, updates status, and re-signs with Ed25519. It explicitly distinguishes from 'create_receipt' by mentioning two-phase tracking, making it distinct from its 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 gives explicit usage guidance: 'Use after create_receipt when you need to record results separately from creation (two-phase tracking).' It also includes a constraint: 'Cannot complete an already-completed receipt.' This provides clear when-to-use and when-not-to-use information.

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?

Discloses key behaviors: Ed25519 signing, local SQLite storage, ability to complete later, and return of receipt_id. However, lacks details on idempotency, error conditions, or permission requirements. With no annotations, the description carries a high burden and mostly meets it.

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 sentences, each essential. Front-loaded with purpose, then guidelines, then storage/return info. No redundancy.

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

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

Given the complexity (20 params, nested objects), the description covers purpose, input requirements, alternatives, storage, and lifecycle. Missing details on validation or error handling, but overall comprehensive.

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

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

Schema coverage is 100%, so baseline is 3. The description reinforces the pre-computed hash requirement and notes optionality of many parameters, but adds minimal new semantic 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 specifies a concrete action ('Create an Ed25519-signed receipt with pre-computed SHA-256 hashes') and clearly differentiates from siblings like track_action (automatic hashing) and complete_receipt (completion later).

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 when to use ('when you have already hashed... or need full control') and provides an alternative ('For automatic hashing, use track_action'). Also mentions return value and storage for full context.

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

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

With no annotations, the description carries full burden. It explains the aggregation, calculation, and output formats, and mentions each line item references a signed receipt for verifiability. It does not disclose potential side effects or limitations, but the generative nature is implied and the description is otherwise transparent.

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

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

The description is a single paragraph of five sentences, each sentence adding value. It is front-loaded with the main purpose and efficiently covers key aspects without redundancy.

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

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

Given the tool's complexity (14 parameters, no output schema), the description adequately covers purpose, outputs, and usage. It explains the output formats and the inclusion of signed receipts, though it could elaborate on the exact structure of the generated invoice or error conditions.

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

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

All 14 parameters have descriptions in the input schema, so schema coverage is 100%. The description adds high-level context for grouping and output formats but does not provide additional per-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 states the tool generates invoices from signed receipts within a date range, specifying aggregation and output formats. It distinguishes itself from sibling receipt-related tools by focusing on billing-oriented aggregation and cryptographic proof.

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 explicitly says 'Use to bill clients for AI agent work with cryptographic proof,' which provides clear usage context. However, it does not mention when not to use it or contrast it with alternatives like list_receipts or verify_receipt.

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

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

No annotations provided; description specifies ordering by timestamp ascending, returns complete receipt objects, and states it's read-only retrieval. Does not cover authentication or edge cases, but sufficient for typical use.

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?

Three sentences, first sentence front-loads action and resource, followed by context and use cases. 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?

No output schema but description explains return value (complete receipt objects) and ordering. For a single-param retrieval tool, all necessary context is present.

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 already covers the one parameter (chain_id) with format and example. Description adds no extra semantic value beyond stating it identifies the chain.

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 verb 'Retrieve' and the resource 'all receipts belonging to a chain', distinguishing it from sibling tools like get_receipt (single receipt) and list_receipts (filtered list).

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?

Explicit use cases are provided: audit workflow, calculate cost/duration, identify failed step. Lacks explicit exclusion guidance, but context is clear.

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

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

No annotations provided, so description carries full burden. It discloses return format (array ordered by timestamp with verdict, score, criteria, confidence) and edge case (empty array). No mention of performance or side effects, but it's a read operation.

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?

Five sentences, front-loaded with core purpose, then linking logic, return format, use cases, and edge case. No fluff; every sentence adds value.

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

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

No output schema, but description explains return format adequately. Covers verdict, score, criteria, confidence, and ordering. No pagination details, but likely unnecessary. Enough for agent to understand tool's output.

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

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

The only parameter receipt_id has 100% schema coverage. The description adds clarity by distinguishing it from judgment receipt ID. This prevents misuse.

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 verb (retrieve), resource (judgment receipts), and the input (receipt ID). It distinguishes itself from sibling tools like judge_receipt (creates) and get_receipt (gets receipt itself).

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 use cases: review evaluation history, compare judgments, audit AI quality. It lacks explicit when-not-to-use guidance or alternative tool names, but the positive usage is clear.

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

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

With no annotations, the description compensates by stating it exports (read operation), returns a hex string, and emphasizes that the private key never leaves, providing adequate 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?

Three concise sentences, each contributing value: function, return format, and security context. No superfluous 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?

Given zero parameters and no output schema, the description fully explains the tool's purpose, return format, and usage context. No 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?

No parameters exist, and schema coverage is 100%. Per guidelines, baseline 4 is appropriate as no parameter description is needed.

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

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

The description clearly states the tool exports the Ed25519 public key used for signing receipts, distinguishing it from sibling tools like create_receipt or verify_receipt.

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 (share key for verification without private key access) and implies security context, but does not explicitly mention when not to use or compare with 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?

Describes return of full object with 27 fields and error on missing ID. Without annotations, it adequately conveys read-only behavior, though lacks discussion of idempotency or permissions.

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?

Three sentences, front-loaded with action, each sentence adds distinct value: purpose, return details, and usage context.

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 action, return, error, and usage. Simple tool missing only minor details like explicit read-only guarantee, but overall 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 already describes receipt_id format (100% coverage). Description adds no new meaning beyond the schema, meeting baseline.

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

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

Clearly states 'Retrieve a single receipt by its ID' with specific verb, resource, and storage location, distinguishing it from siblings like list_receipts and verify_receipt.

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 says 'Use to inspect a specific receipt or retrieve it before verification,' but does not mention 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?

Without annotations, the description carries the burden. It implies no destructive actions by stating it 'displays' a guide. However, it does not explicitly state it is read-only or safe, which would further enhance transparency. Still, the behavior is clearly non-mutating.

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 with no extraneous information. It front-loads the main purpose and follows with usage examples, making it concise and well-structured.

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

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

Given the tool has no parameters, no output schema, and no annotations, the description completely conveys its purpose and usage. It tells the agent what to expect (a guide with examples) and when to call it.

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

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

The input schema has zero parameters and 100% coverage. The description adds no parameter details, which is appropriate since there are no parameters. The baseline of 4 is correct for a tool with no 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 the tool displays a getting-started guide with usage examples for all Agent Receipts tools. It specifies the verb 'display' and the resource 'guide', and distinguishes itself from sibling tools by focusing on setup and reference.

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 explicitly says 'Call this tool first when setting up Agent Receipts or when you need a reference for available tools and their typical usage patterns.' This provides clear guidance on when to use it and implies it should be used before other 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?

With no annotations, the description carries full burden. It discloses the workflow: creating a pending judgment, returning a prompt, requiring a subsequent complete_judgment call, and that receipts are Ed25519-signed. This provides good 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?

Two sentences, front-loaded with purpose and structured details. No redundant information. Every sentence adds value.

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

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

No output schema exists. The description mentions returning a structured evaluation prompt but does not detail its format. However, given the complexity (nested rubric), it provides sufficient high-level 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. The description adds some context (e.g., host model evaluates, output_summary_for_review when insufficient), but the schema already adequately describes 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 states the verb 'Start an AI judgment evaluation' and the resource 'a receipt'. It distinguishes itself by mentioning the creation of a pending judgment receipt and the need to later call complete_judgment, differentiating it from siblings like verify_receipt.

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 using this tool for assessing output quality beyond simple pass/fail, implying when it is appropriate. It does not explicitly list alternatives, but the contrast with 'simple pass/fail' provides usage context.

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

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

With no annotations provided, the description adequately discloses behavior: it reads from local SQLite storage, returns paginated results with count and navigation flags, and specifies default sorting and limit. It does not cover all edge cases (e.g., empty results) but is thorough for a list tool.

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 comprises four well-structured sentences, each conveying essential information: core action, sorting, output, and defaults/use cases. No redundant or missing content.

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 (10 parameters, no output schema), the description covers core functionality, pagination details, and use cases. It does not describe the receipt structure, but that is likely known from context; still sufficiently complete for tool selection.

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 description repeats some parameter info but adds context on sorting format and defaults. However, the schema already describes parameters well, so the added value is marginal.

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 queries and paginates receipts with filtering and sorting, and specifies use cases like auditing and report generation. It distinctively focuses on listing multiple receipts, differentiating from siblings like get_receipt (single) and generate_invoice.

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 explicitly tells when to use the tool ('Use to audit agent activity, generate reports, or find specific receipts'). It does not mention when not to use it or alternatives, but the context is clear given 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?

No annotations are provided, so the description bears full responsibility for disclosing behavioral traits. The description does not indicate whether this operation is read-only, whether it requires special permissions, or what side effects (if any) occur. As a report generator, it is likely safe, but that is not explicitly stated, leaving ambiguity.

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

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

Two sentences provide a clear and complete overview. The first sentence states the action, the second specifies what the report shows. No unnecessary words, and the information is front-loaded.

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

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

For a tool with no output schema, the description should explain the report's output structure. It mentions what information is included but does not specify the format (e.g., list, summary, counts) or behavior when no results are found. Given the parameter count and lack of annotations, some additional context 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% with clear descriptions for each parameter. The description adds context about the report content (remembered, forgotten, merged, by agents), which aligns with the parameters. However, it does not add significant new meaning beyond summarizing the filter capabilities already 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 starts with 'Generate an audit report of memory operations,' which is a specific verb+resource combination. It further details what the report shows (remembered, forgotten, merged, by agents, over time period), distinguishing it from sibling tools like memory_recall (retrieve specific memories) or memory_forget (perform forgetting).

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 when an audit overview is needed, but it does not explicitly state when to avoid using this tool or mention alternative tools for more granular queries. Given the context of many sibling tools, explicit guidance would help, but the current text at least suggests its 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?

No annotations present, so description carries full weight. It discloses that every pull is logged as a signed receipt, but lacks details on performance implications, rate limits, or authorization needs. Acceptable but not comprehensive.

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

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

Two sentences with no wasted words. First defines the tool's result, second gives usage guidance, third a behavioral note. 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?

Despite no output schema, the description enumerates return contents (entities, observations, relationships, preferences) and mentions logging. Adequate for a straightforward context dump tool. Some missing details on ordering or format, but overall sufficient.

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

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

Schema covers all four parameters with descriptions (100% coverage). The description adds no additional parameter meaning beyond what the schema already provides, so baseline score applies.

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

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

The description clearly states the tool retrieves a 'complete context dump' of memories, listing specific components (entities, observations, relationships, preferences). It distinguishes from sibling tools like 'memory_entities' (which focuses on entities only) by being a comprehensive snapshot.

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 recommends calling at conversation start to understand what's known, providing clear context. Does not explicitly exclude alternative tools but implies this is for full context, not filtered queries.

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

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

With no annotations, the description should disclose behavioral traits. It only says 'List known entities' and 'returns entities with their observation counts'. It does not mention read-only nature, authentication needs, rate limits, or any side effects, providing minimal 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 extremely concise (two sentences), with the purpose in the first sentence and return info in the second. No waste, front-loaded.

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

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

Given the tool has 5 parameters, no required ones, and no output schema, the description is too sparse. It does not explain the entity_type or scope enums, default behaviors, or how this differs from similar tools like memory_recall. The description is insufficient for an agent to use the tool correctly without additional context.

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 60%, but the tool description adds no parameter explanations. The description's mention of 'people, projects, organizations' hints at entity_type enum values but does not clarify the enum or other parameters like scope, query, etc. Thus it adds little 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 lists known entities with optional filtering, and specifies what entities are (people, projects, etc.). The verb 'list' and resource 'entities' are specific, and it distinguishes from sibling tools by focusing on entity listing rather than observations or audits.

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

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

The description implies usage for listing entities with optional filtering, but it does not explicitly state when to use this tool vs alternatives like memory_recall or memory_audit. No when-not or alternative guidance is provided.

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?

Describes bundle contents (entities, observations, etc.) but no annotations provided; no mention of side effects, format, or resource impact beyond the description.

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?

Three sentences, no fluff. Front-loaded with action and resource, then lists contents and 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?

Covers purpose and contents but lacks details on output format, size limits, or verification process. No output schema. Adequate but not thorough.

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 little beyond schema. However, it summarizes the bundle components, which provides context.

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

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

Clearly states 'Export memories as a portable, verifiable bundle' — a specific verb and resource. Distinguishes from siblings like memory_import_bundle.

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?

Implies usage for sharing with other agents or importing into another instance, but does not explicitly exclude alternatives or state when not to 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?

Discloses soft delete behavior and signed receipt recording, but lacks details on required permissions, reversibility, or side effects on related data.

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

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

Two sentences, front-loaded with the main action, no redundant information. Every sentence adds value.

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

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

Covers purpose and soft delete, but missing explanation of return value (signed receipt format) and whether forgetting an entity cascades to its observations.

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

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

Schema covers all parameters with descriptions; the tool description implies that one of entity_id or observation_id is needed but doesn't explicitly state requirement. Adds minimal 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 the action (forget) and resource (specific observation or entire entity), but does not explicitly differentiate from sibling tools like memory_audit or memory_recall.

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 like memory_observe or memory_audit; no context on prerequisites or typical use 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?

No annotations are provided, so the description carries the full burden. It discloses checksum verification, skipping existing memories, and recording the import as a signed receipt. This is good for a write operation, though it doesn't mention potential failure modes.

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

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

The description is three sentences, front-loaded with purpose, and contains no fluff. Every sentence provides essential information.

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

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

For a tool with no output schema and no annotations, the description covers purpose, input handling, deduplication, and side effects. It lacks explanation of return values, but that is partially compensated by the 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 baseline is 3. The description adds context for 'bundle' (from another instance) and aligns with 'skip_existing' schema, but doesn't compensate beyond that.

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 imports a memory bundle from another instance, using specific verbs ('Import') and resources ('memory bundle'). It distinguishes from siblings like 'memory_export_bundle' by focusing on import behavior.

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 importing from another instance and skipping existing memories, but lacks explicit guidance on when to use this tool versus alternatives (e.g., memory_audit, memory_context). It does not 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?

The description adds behavioral details beyond the input schema, such as automatic entity creation and cryptographic signing with receipt linking. Since annotations are absent, the description carries the full burden and does well, though it could mention potential side effects or permissions.

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 extremely concise with two sentences, front-loading the primary purpose and key behaviors. Every sentence adds value with no redundant or irrelevant 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 8 parameters, no output schema, and no annotations, the description covers the core action and important behaviors (entity creation, cryptography). It omits return value details but is largely sufficient for an agent to understand the tool's use.

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 does not elaborate on individual parameters beyond what the schema provides, so it meets the minimum but adds no extra semantic value for 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 the verb 'store' and the resource 'memory observation', and specifies the scope as persons, projects, preferences, or entities. It distinguishes from sibling tools by noting automatic entity creation and cryptographic signing linked to a receipt, which are unique features.

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 what the tool does but does not provide any guidance on when to use this tool versus alternatives like memory_recall or memory_forget. No explicit when or when-not scenarios are mentioned.

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

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

With no annotations, the description effectively discloses the tool's behavior: it is read-only and returns creation details, conversation, agent, and modification history. It does not mention permissions or rate limits, but the read-only nature is clear from 'Get the full provenance chain.'

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

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

The description is two sentences, front-loaded with the action and resource. Every word contributes meaning without redundancy. It is efficient and easy to parse.

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

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

Given the tool's moderate complexity (provenance chain), the description adequately outlines what is returned but does not detail the structure of the chain. With no output schema, a bit more structure detail could help, but overall it is sufficient for most use cases.

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 covers 100% of parameters, each with a description. The tool description adds context by explaining what the parameter 'observation_id' is used for (tracing provenance), but does not provide additional value beyond what the schema already conveys.

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: 'Get the full provenance chain for a memory observation.' It specifies the resource (memory observation) and the action (trace provenance), distinguishing it from sibling tools like memory_audit or memory_context by detailing what it shows (creation, conversation, agent, modifications).

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 usage when provenance information is needed, but it does not explicitly state when to use this tool versus alternatives like memory_audit or memory_recall. No when-not-to-use or conditional guidance is provided.

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

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

With no annotations, the description adds one behavioral note: 'Every recall is logged as a receipt.' However, it does not disclose read-only nature, side effects beyond logging, or rate limits. The note on receipt logging is partially contradicted by the 'audited' parameter description (see parameter semantics).

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: first states the core purpose, second lists key features and a behavioral claim. No redundant words, front-loaded, and 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?

For a tool with 7 optional parameters and no output schema, the description covers basic use and one side effect. However, it omits explanation of the return format, pagination behavior, and the relationship between receipt logging and the 'audited' parameter, leaving ambiguity.

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

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

Schema coverage is 100%, so the baseline is 3. The description summarizes filtering by entity_type, entity_id, and scope but adds no novel meaning beyond the schema. The mention of receipt logging conflicts with the audited parameter description, causing slight confusion.

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

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

The description uses a specific verb+resource phrase 'Search and retrieve stored memories' and distinguishes from siblings like memory_observe (create) and memory_forget (delete) by focusing on retrieval and filtering.

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

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

The description implies usage for text search and filtering but does not explicitly state when to use this tool versus alternatives like memory_audit or memory_entities. No exclusions or alternative tool names 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?

Discloses bidirectional querying but directional storage, which is useful. No annotations provided, so description bears full burden; however, it omits side effects, permissions, or implications like idempotency or overwrites.

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

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

Two sentences, concise and front-loaded with purpose. No redundant information.

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

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

Lacks description of return value (no output schema). For a creation tool with 5 parameters, details on what is returned upon success or failure are missing. Adequate for basic understanding but not fully self-contained.

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

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

Schema covers 80% of parameters with descriptions. The description adds minimal parameter insight beyond the schema; the example is helpful but does not clarify optional parameters like 'strength' or 'context' beyond their 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?

Clearly states the tool creates a relationship between two entities with an example ("Amin builds ModQuote"). Differentiates from siblings like memory_observe and memory_entities by focusing on relational creation.

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 (e.g., memory_observe for observations, memory_entities for entity creation). Lacks context about prerequisites or when not to 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?

No annotations provided, so description carries full burden. It discloses automatic SHA-256 hashing, no raw data storage, and returns signed receipt. Lacks explicit mention of write nature but is clear enough. Good behavioral context beyond schema.

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

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

Two sentences, no waste. First sentence packs core functionality, second provides usage guidance. Efficient and well-structured.

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

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

Given 18 parameters, nested objects, and no output schema, description covers main purpose, key behaviors, and usage. Mentions return of receipt_id, sufficient for selection. Could elaborate on constraint evaluation but schema covers details.

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% (18 parameters all described), baseline is 3. Description adds value by explaining automatic hashing for input/output and constraint evaluation, which is 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?

Description clearly states the tool creates a completed Ed25519-signed receipt for AI agent actions with automatic hashing. It also explicitly differentiates from siblings by recommending use over create_receipt + complete_receipt for single-step actions.

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?

Explicit guidance: 'Use this as the primary tool for recording agent actions — prefer over create_receipt + complete_receipt for single-step actions.' This clearly indicates when and why 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?

No annotations provided, so description carries full burden. It explains the verification process (extract, canonicalize, verify) and mentions the return field. But it does not disclose behavior on invalid inputs (e.g., missing receipt_id, tampered receipt, or invalid signature).

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

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

Two sentences, no redundancy, front-loaded with the main action. Every sentence adds value and the structure is optimal for agent parsing.

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

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

Given the tool's simplicity (1 param, no output schema), the description covers purpose, process, and use case. However, it does not explicitly describe the failure case (e.g., returns false or error) or the full return format, which would be beneficial.

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

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

Schema covers 100% of the single parameter with clear description. The description adds no new semantic detail beyond the schema, so 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 uses specific verb+resource: 'cryptographically verify an Ed25519 signature on a stored receipt'. It clearly states the core operation but does not explicitly differentiate from sibling tools like create_receipt or judge_receipt.

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 explicit context: 'Use to audit receipts before using them as evidence or before completing payments based on agent work.' However, it does not mention when NOT to use it or suggest alternative tools.

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