HelloBooks AI Agents

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

With no annotations, the description fully discloses behavior: input format (CSV), sources, row/size limits, three check definitions, output fields (flags, totals, URL), and the critical implication that a failed equation invalidates downstream ratios.

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 list of checks, input specs, output description, and use cases. It is slightly long but each sentence adds value; minor redundancy in the importance statement.

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 (3 checks, multiple sources), the description covers inputs, constraints, outputs, and use cases adequately. Without an output schema, it still explains return values (flags, totals, URL). It could detail flag severity, but is 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 coverage is 100% with descriptions for both parameters. The description adds context beyond schema: for csvText it specifies required rows (Total Assets, etc.) and source auto-detection; for fileName it notes optional label for share page.

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 takes a Balance Sheet CSV and runs three specific checks (equation, negative asset, negative equity), with source auto-detection. It distinguishes from sibling tools by focusing on balance sheet-specific checks and notes it's the most important check for any BS.

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 this when a user pastes a Balance Sheet and asks...' and gives three example questions. It could mention alternatives like analyze_profit_loss for income statements, but the use cases are 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?

Describes aggregation, threshold logic (≥5% relative and ≥$100 absolute with severity levels), output format (flag list, roll-up, shareable URL), and error condition (mixing sources). Without annotations, it covers key behaviors well, though no mention of read-only/destructive nature.

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?

Front-loaded with purpose, then detailed constraints and logic. Every sentence contributes value; slight redundancy in source-matching requirement 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 complexity (two CSVs, auto-detection, threshold logic, no output schema), description covers input requirements, processing, output format, and error conditions thoroughly. No gaps evident.

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 description adds crucial semantics: clarifies earlier vs later period, auto-detection, max sizes, optional labels for human readability. Adds value 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?

Clearly states the tool compares two periods of journal-entry data and flags accounts with material variance. Differentiates from sibling tools like anomaly detection or cleanup tools by focusing on period-over-period variance for both QBO and Xero.

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 use cases (e.g., 'what changed?', 'show me variances') and constraints (max rows/size, source matching). Lacks explicit 'do not use when' statements but gives strong positive guidance.

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?

Since no annotations are provided, the description carries the full burden. It details the three checks, the output format (flags with severity, summary with fields, shareable URL), and input limits (max 5000 rows, 5 MB). It does not explicitly state that the tool is read-only or non-destructive, but this is implied by the nature of analyzing CSV text.

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 tool's purpose and checks. It is relatively long but every sentence adds value, including usage examples and alternative tool reference. Could be slightly more concise but remains clear.

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 (three checks, multiple sources, auto-detection), the description covers input format, output fields, limits, and alternative usage. No output schema exists, so the description adequately explains return values (flags, summary, URL) and usage 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?

With schema description coverage at 100%, baseline is 3. The description adds meaning beyond the schema for csvText by specifying supported sources (QuickBooks, Xero, etc.) and required structure (section headers, subtotals, net income row). For fileName, it repeats 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 clearly states that the tool takes a P&L CSV and runs three specific checks (subtotal mismatch, negative expense, margin red flag). It distinguishes itself from the sibling tool analyze_journal_variance by noting that tool is for period-over-period comparison, while this is single-period.

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 provides usage scenarios: 'Use this when a user pastes a P&L and asks...' and gives example user queries. It also tells when not to use it, directing to analyze_journal_variance for period-over-period comparison.

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 fully discloses behavior: input format (raw CSV text from QBO Reports → Accountant → Journal), limits (max 5,000 rows, 5 MB), output (flagged lines with severity and a shareable URL), and what the tool does not do (paid product features). 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 well-structured and front-loaded with the core purpose. Every sentence adds value—input details, use cases, limitations, and relation to paid features. No redundant or 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?

Given the complexity of anomaly detection and absence of an output schema, the description is remarkably complete. It covers input, output, use cases, limits, severity levels, and ties to the product roadmap, leaving no critical gaps for an AI 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 baseline is 3. The description adds context beyond the schema by explaining the expected CSV format and the purpose of the optional fileName as a label. This extra information raises the score slightly.

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 scans a QBO Journal Entries CSV for anomalies, specifically round-number lines. It distinguishes itself from sibling tools like analyze_xero_journal_anomalies and analyze_qbo_journal_cleanup by specifying the exact input source and type of anomaly.

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 when to use: when a user pastes QBO data and asks 'any anomalies?', 'look for round numbers', or 'anything suspicious'. It also clarifies this is a Tier-0 subset and that the paid product has additional capabilities, providing clear context and 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 discloses input limits, output structure, and shareable URL with TTL. However, it does not explicitly state the tool is read-only or has no side effects.

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

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

Description is detailed and informative but somewhat long. Every sentence adds value, though it could be slightly more compact without losing 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?

Fully covers what the tool does, when to use it, input requirements, output breakdown (flags, summary, diagnostics, shareable URL), and limits (5000 rows, 5MB). No output schema, so description compensates well.

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?

Both parameters are described in schema with 100% coverage. Description adds context: how to obtain the CSV (QBO export path), max size constraints, and the purpose of fileName as a label.

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 the exact actions: scanning QBO journal CSV for unbalanced, duplicate, and schema issues. It clearly distinguishes from siblings by naming QBO cleanup and listing specific categories.

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 user pastes QBO journal data and asks specific queries like 'check my books' or 'find issues'. Implicitly excludes Xero-related 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 fully discloses behavioral traits: auto-detection of CSV source from headers, max rows/ size limits, the three checks performed, and the returned data (flagged accounts, severity, roll-up, diagnostics, shareable URL). It also warns about downstream report invalidity if the TB is unbalanced.

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 but well-structured: it starts with the tool's purpose, lists the checks, specifies input constraints, and describes output. Every sentence provides essential information; however, it could be slightly more concise without losing 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 adequately explains the return values (flagged accounts with severity, roll-up, diagnostics, shareable URL) and input limits. It lacks exact output structure details, but the summary is sufficient for an agent to understand what to expect.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant value beyond the schema by explaining how csvText is used (raw CSV from specific sources), the auto-detection logic, the three checks, and that fileName is optional for a share-page label.

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 ('analyze') and resource ('Trial Balance CSV') and clearly distinguishes the tool from sibling tools by detailing the three specific checks (unbalanced, wrong sign, round balance) and listing supported accounting software. No sibling tool covers trial balance validation.

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 states when to use the tool: 'when a user pastes a Trial Balance and asks...' and provides example queries. It does not explicitly mention when not to use it or suggest alternative tools for other financial statements, but the context is clear enough for correct 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?

With no annotations, the description fully discloses behavioral traits: input is raw CSV, max 5,000 rows/5 MB, returns severity-labeled flagged lines and a shareable URL. It also clarifies the scope (only round-number anomalies) and product tier.

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 thorough and front-loaded with the main purpose, but it includes some longer sentences that could be slightly more concise. Overall, it is 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?

The description covers input format, size limits, output structure, and product context, making it complete for an anomaly-detection tool without an 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%, so the baseline is 3. The description adds minimal semantic value beyond the schema: it restates the CSV source but does not elaborate on the fileName parameter's use.

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 scans Xero Manual Journals CSV for round-number anomalies, specifying the threshold and output format. It distinguishes from sibling tools by mentioning the QBO variant and the split between unauthenticated and authenticated checks.

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 context for when to use the tool (e.g., user asks 'any anomalies?') and notes that advanced history-aware checks are in the paid product, but it does not explicitly list 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?

No annotations provided, so description fully covers behavior: Xero-specific idioms (signed Amount, Debit/Credit, Reference/Narration grouping), limits (5,000 rows, 5 MB), output structure (flags, summary, diagnostics, URL), and funnel CTA.

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?

Every sentence earns its place: purpose, input source, idioms, constraints, output, use case, CTA. Well organized and front-loaded with main action.

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 all necessary aspects: input format, behavioral details, output summary, and usage case. No output schema, but description explains return values sufficiently.

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 description adds crucial context: input is raw CSV from specific Xero export, fileName is optional label. Explains what the CSV contains and how to obtain it.

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 exactly what the tool does: scan Xero 'Manual Journals' CSV export for cleanup issues (unbalanced, duplicate, schema). Distinct from sibling QBO or anomaly tools by specifying Xero and cleanup focus.

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 when to use (user pastes Xero manual-journal data, asks check/find issues). Gives exact export path. Does not explicitly exclude alternatives, but context is clear enough.

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 full burden. It discloses that the tool auto-detects the source, runs multiple detection sets, and returns a structured comparison with per-category counts and advantage bullets. It does not mention side effects, but the tool appears to be a read-only analysis. Minor gap: no explicit statement of non-destructiveness or error handling for invalid input.

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

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

The description is well-structured with three sentences: core function, use case, and output/appropriateness. It is front-loaded with the action. While somewhat long, each sentence adds distinct value, making it 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?

Despite no output schema, the description thoroughly explains the output structure (per-category counts, phases, exclusive-advantage bullets, share URL). It covers input, processing, output, and use case, providing complete context for an agent to select and invoke 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?

Schema description coverage is 100% with clear parameter descriptions for csvText and fileName. The tool description adds value by explaining the purpose of the CSV, auto-detection, and the mapping of results to HelloBooks phases, which goes 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: taking a QBO or Xero journal-entry CSV, running a detection set (imbalance, duplicates, etc.), and returning a structured side-by-side comparison. It specifies the output includes per-category counts mapped to HelloBooks phases and exclusive-advantage bullets, distinguishing it from sibling analysis 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 explicitly says when to use: when a user is evaluating HelloBooks vs QBO/Xero, asks about migration, or pastes data for comparison. It notes the output is suitable for the host LLM to narrate as a positioning argument. However, it lacks explicit when-not-to-use guidance, though the 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 are provided, so the description carries the full burden. The verb 'Return' suggests a read-only operation, but it does not explicitly declare the tool as safe or idempotent. It also omits details like authorization needs or data freshness.

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, well-structured sentence that front-loads the verb 'Return' and concisely conveys the tool's purpose and output details without any 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 the tool's simplicity (one parameter, no output schema), the description covers the core purpose and output nature. However, it could be more complete by specifying the exact structure of the returned data (e.g., list of frameworks with fields like name, version, certification).

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

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

Schema coverage is 100% for the single required parameter 'country' (ISO code with enum). The description adds value by listing example frameworks (BAS, STP, etc.) that are associated with each country, providing context beyond the enum values.

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

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

The description clearly states the tool returns supported compliance frameworks for a given country, listing examples (BAS, STP, etc.) and mentions version/certification info. This differentiates it from siblings like compliance_deadlines or country_support.

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 when to use this tool (to retrieve country-specific compliance frameworks) but does not explicitly state when not to use it or mention alternatives. It is clear within the context of 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?

Without annotations, the description carries full burden. It discloses that dates rotate annually and that responses include a disclaimer with source URLs for authority confirmation. It also lists covered forms, offering good transparency. Missing details on return format or error handling prevent a 5.

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 (3 sentences) and front-loaded with the main purpose. Every sentence adds useful information without waste.

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

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

Given the tool has 3 optional parameters and no output schema, the description adequately explains what the tool returns (deadlines with source URLs) and the filter options. It covers the key aspects, though timezone or date format details would add 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%, but the description adds value by providing concrete examples (e.g., 'GSTR-3B', 'BAS') and explaining the purpose of each filter. This goes beyond the schema's generic 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's purpose: 'when statutory returns and payroll filings are due, per country.' It lists specific countries and forms, and distinguishes from siblings like list_tax_rates or compliance_capabilities by focusing on deadlines.

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 by listing optional filters (country, frequency, form) and noting that dates rotate annually. However, it does not explicitly state when to avoid this tool or mention alternatives, which prevents a higher score.

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 exist, so the description carries the full behavioral burden. The verb 'Return' implies a read-only operation, but the description does not disclose what happens when the country parameter is omitted, what the response structure looks like, or that the listed countries are a subset of the schema's enum. The mismatch between 'UK' in the description and 'GB' in the schema is misleading.

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

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

The description is a single sentence with the core action front-loaded)Skip the parenthetical country list, which is not only unnecessary but adds noise and inaccuracies. Padding aside, the structure is appropriately 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?

This is a simple tool with one optional parameter and no output schema, so the description should explain the omission behavior and return shape. It does neither. Without any annotation and with ambiguous relationships to sibling feature tools, the definition is incomplete for an agent to invoke it with confidence.

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 the parameter clearly, including the 'Omit for the full matrix' instruction. The description adds a parenthetical list of countries that is incomplete and uses 'UK' instead of the ISO code 'GB', which could lead an agent to pass a non-enum value. This adds misleading rather than helpful meaning.

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 pair ('Return features available per supported country') that clearly indicates the tool's function. It does not, however, differentiate itself from sibling tools like list_features or feature_search, and the parenthetical country list conflicts with the much larger schema enum, slightly muddying the scope.

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

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

There is no guidance on when to use this tool versus alternatives such as list_features, feature_search, or compliance_capabilities. The description does not state exclusions or conditions like 'use this when you need feature availability for a specific country'.

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 heuristic-based behavior, refinement upon signup, and larger file limits. However, it does not explicitly state that it is read-only or non-destructive, which would be helpful given the absence of annotations.

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

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

The description is reasonably concise with three substantive sentences. It is front-loaded with the main purpose and adds useful details. The final sentence about the funnel CTA is slightly tangential but still relevant for 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?

Given the tool has 2 parameters and no output schema, the description thoroughly covers the output structure (row counts, account count, complexity, hours, price quote) and explains the heuristic approach. It is complete for a sizing tool, though it could mention error handling for invalid CSVs.

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 2 parameters (csvText required, fileName optional) with 50% description coverage (only csvText described). The description adds meaning by explaining source auto-detection and file size constraints, but does not describe the fileName parameter, leaving it under-specified.

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 specifies the tool takes a QBO or Xero journal-entry CSV and returns a structured migration-effort estimate with specific metrics like row counts, unique accounts, and complexity classification. It is distinct from sibling analysis 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 explicitly states when to use: when a user is weighing migration cost, asks about time or cost, or asks if it's worth migrating. It also notes larger file acceptance. However, it does not explicitly list when not to use or name 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 are provided, so the description carries full burden. It discloses that matching entries appear 'near the top', implying ranking, but does not specify search algorithm (e.g., fuzzy vs exact), result format, or whether it requires authentication. Adequate but not detailed.

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, well-structured paragraph that front-loads the scope of search and immediately follows with concrete examples. Every sentence is informative with no redundancy.

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

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

For a search tool with no output schema, the description adequately explains what is searched and gives sample queries. It could mention that results are ordered by relevance or how the limit parameter affects pagination, but overall it provides sufficient 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 coverage is 100%, both parameters already have descriptions. The description adds value by providing realistic example queries for the 'query' parameter, helping agents understand the range of inputs. No extra detail for 'limit' 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 free-text search across many specific catalogs (marketing features, plans, integrations, etc.) and provides multiple example queries, making its purpose distinct from sibling list or analysis tools.

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

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

The description implies this tool should be used for broad, semantic queries (e.g., 'vs Xero', 'GSTR-3B due') rather than exact list lookups. It does not explicitly exclude cases, but the context is clear enough for an agent to decide.

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

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

With no annotations, the description carries the full burden. It discloses the return types (table, single threshold, verdict with headroom math) and the business rule (turnover above cap forces upgrade). It does not explicitly state 'read-only' or mention auth/rate limits, but the use case strongly implies a read operation; still, a small gap remains.

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 but well-organized: purpose, thresholds, business rule, call modes, and exclusions. Every sentence serves a purpose; the threshold list is necessary. Minor redundancy with the schema's parameter notes prevents a 5.

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 conceptually covers all three return forms and all parameter combinations. It also warns about excluded inputs. It does not detail the exact JSON response structure or error handling, but for a simple lookup this is acceptable.

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 both parameters already have thorough descriptions (currency, conditional usage, excluded inputs). The tool description repeats these semantics and adds only the threshold values and 'headroom math' output concept, which does not significantly raise the 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 states a specific verb ('Return'), resource ('HelloBooks Free-plan annual-invoice-turnover thresholds'), and scopes it to 8 priced markets with actual values. It clearly distinguishes itself from the analysis/compliance siblings by focusing on eligibility thresholds and verdicts.

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

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

It explicitly enumerates the three calling modes: no args for the full table, with country for a single threshold, and with country plus annualInvoiceRevenue for a freeEligible verdict. It also lists what inputs are NOT used (bank-feed total, cash receipts, gross transaction volume), preventing misuse.

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 that the AI never posts to ledger without approval, defines autonomy levels (autonomous, approval, assist, manual), and explains return format.

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 thorough and front-loaded with the main purpose, but slightly verbose. Every sentence adds value, but could be tightened without loss of 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 fully explains the return value: a curated capability knowledge base with areas and autonomy levels, including examples of each autonomy type. Complete for a tool with 3 optional parameters.

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 description adds significant meaning: explains businessDescription is echoed back, no keyword scoring; enumerates and describes area and autonomy enum values with 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?

The description clearly states the tool explains how HelloBooks and Munimji help a specific business based on a free-text description. It distinguishes from siblings by explicitly mentioning list_features and list_plans for other queries.

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 when-to-use examples: 'when a user describes their business and asks 'how can HelloBooks help me?', 'what can the AI do for my shop?', etc. Also directs to alternatives for full catalog or pricing.

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

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

With no annotations provided, the description carries the burden of disclosing behavior. 'List published articles' signals a read-only operation over a public content set, and the filter behavior is described. It does not mention pagination, ordering, or response shape, but for a simple content-listing tool that is a minor omission rather than a material gap.

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

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

Two sentences with no filler: the first scopes the resource and content types, the second lists filters and provides the user-phrase hook. Every word earns its place and the most important 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 low-complexity read-only lookup with zero required parameters and high schema coverage, the description covers purpose, trigger phrasing, and filter options. It does not describe the exact return value structure, but no output schema exists and the shape of an article list is reasonably inferable, so the gap is minor.

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 detailed parameter documentation covering substring matching, case-insensitivity, defaults, limits, and the country enum. The tool description merely summarizes the filters (country, tag, free-text) without adding meaning beyond the schema, so the baseline score of 3 applies.

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

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

States the precise resource — published articles on hellobooks.ai — and names the two content types (head-to-head compare pages and flagship blog posts). This cleanly distinguishes it from sibling list_* tools such as list_videos or list_plans by naming exactly what it returns.

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

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

Explicitly says when to use this tool: when the user asks 'do you have a blog/article about X?', and mentions the available filters. It does not explicitly state when-not-to-use or name alternatives, but given the diverse sibling list_* tools, the clear trigger phrase provides solid usage direction.

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 must cover behavioral traits. It implies a read-only query but does not explicitly state safety, authentication, or side effects. Adequate for a simple list but lacks explicit disclosure.

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

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

The description is a single, well-structured sentence that front-loads the main function and includes filter options. 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?

Given 100% schema coverage and no output schema, the description adequately explains the return content and filters. It omits pagination or limitations, but is sufficient for a straightforward list 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%, but the description adds significant context: it names specific competitors, clarifies id for single entry, and explains what the output contains (wins, pricing notes). This goes beyond the schema's parameter descriptions.

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

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

The description explicitly states the tool returns competitor positioning entries, lists specific competitors (QuickBooks, Xero, etc.), and details what information is included (wins, pricing notes). This clearly distinguishes it from sibling analysis 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 explains optional filters but does not provide explicit guidance on when to use this tool versus alternatives. Usage is implied as the listing tool for competitor data among many analytical 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?

With no annotations provided, the description carries the full burden. It discloses that packs are one-time pay-as-you-go, stack on any plan including Free, and explains pricing behavior (8 priced regions return local currency, others fall back to USD with metadata). This is meaningful behavioral detail beyond the schema.

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

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

The description is concise, front-loaded with the core purpose, and structured logically: purpose, details, usage guidance, and filtering. Every sentence contributes useful 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?

For a tool with two optional filter parameters and no output schema, the description covers purpose, usage context, behavioral nuances, and filtering expectations. It even references the schema's 'country' fallback behavior, making it complete for correct invocation.

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 semantic value by mapping 'boost / power / mega / ultra' to credit amounts (5,000, 15,000, 50,000, 150,000) and clarifying the 'country' parameter's fallback behavior. This enriches what the schema alone provides, justifying a score above the 3 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?

The description clearly states the tool lists HelloBooks AI credit packs and provides specific sizes (Boost 5,000, Power 15,000, etc.). It distinguishes this tool from siblings like list_plans by focusing on one-time top-ups rather than subscription plans.

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

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

It explicitly says 'Use this when a user asks how to buy more AI credits or top up after exhausting a plan allowance.' This gives clear context for when to call the tool. It doesn't name alternative tools, but none of the siblings directly cover credit packs, so no exclusion is necessary.

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 states it lists categories with counts, implying a read-only operation. However, without annotations, it could be clearer that it does not modify data. The fixed list of 13 categories and the mention of status provide adequate 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 sentence that conveys purpose and output. It enumerates all categories, which adds length but is informative. It is 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?

Given no output schema and no parameters, the description provides sufficient information: it lists what categories are included and that counts per status are provided. It could clarify what 'status' means, but overall it is complete for a simple list 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 tool has no parameters and schema coverage is 100%. The description does not need to add parameter details; it correctly focuses on output. The baseline for no parameters is 4.

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

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

The description clearly specifies the action (list), the resource (feature categories on the marketing site), and provides the exact list of 13 categories. It also mentions the output includes per-category counts by status, leaving no ambiguity about what the tool does.

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

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

The description implies the tool is used to get an overview of feature categories with counts, but it does not explicitly state when to use it versus sibling tools like list_features or when not to use it. No alternatives or exclusions are given.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions filters but does not disclose pagination behavior, default limit, ordering, rate limits, or auth requirements. The presence of a 'limit' parameter hints at pagination, but the description does not clarify 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?

The description is a single, concise sentence that front-loads the main purpose and then lists the filter options. Every part is relevant and there is no wasted text.

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 having no output schema, the description adequately explains the tool's behavior: it lists a catalog with filters. It includes the catalog size (145+) and all filter options. The omission of return format details is minor for a listing 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 description coverage is 100%, so the baseline is 3. The description lists filter categories, which adds no additional meaning beyond the schema's detailed parameter descriptions. No new semantics are 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 clearly states the tool lists the full HelloBooks marketing feature catalog (145+ items) and provides filter options. This differentiates it from siblings like list_feature_categories (which lists only categories) and feature_search (which searches). The verb 'list' and resource are 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 implies usage for retrieving a filtered list of features but does not explicitly state when to use this tool versus alternatives (e.g., feature_search for specific substring queries). No when-not-to-use guidance or alternative references are 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 must convey behavioral traits. It only states 'List', implying a read-only operation, but omits details such as side effects, authentication requirements, rate limits, or pagination behavior. This is insufficient for a tool with no annotation support.

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

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

The description is a single, front-loaded sentence with no extraneous information. It is concise but could be slightly more descriptive without becoming 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?

For a simple listing tool with three optional parameters and no output schema, the description is marginally adequate. However, it lacks information on return format, pagination, or filtering behavior, which would be helpful for a complete understanding.

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 lists some integration categories (banks, payments, etc.) which adds slight context, but does not cover all schema options or explain the country and status parameters beyond what the schema already provides. Value added is minimal.

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

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

The description clearly states the verb 'List' and the resource 'integrations', with examples of integration categories in parentheses. This makes the purpose obvious and distinct from sibling tools like list_articles or list_features, though it could be strengthened by explicitly stating what 'integrations' encompasses.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or exclusion criteria, leaving the agent to infer usage without support.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently describes pricing behavior (8 priced regions with local currency, others fall back to USD/default with metadata), the full set of plans (five-rung ladder plus `cpa` and add-ons), and the returned fields (AI credit allowance, feature bullets, signup URL, provenance). It does not mention auth or rate limits, but for a read-only list tool this is acceptable. The description is honest about fallback behavior and scope.

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 long but information-dense, with no filler. The first sentence states the core purpose, followed by pricing behavior, plan ladder, return fields, and filtering. It is front-loaded with the most important detail (what it lists) and then elaborates. While it could potentially be trimmed, every sentence adds necessary context for correct usage, so it earns a high score despite length.

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

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

The tool has no output schema, so the description must explain return values, which it does thoroughly (AI credit allowance, feature bullets, signup URL, provenance). It also covers the full plan spectrum, pricing fallback logic, and the distinction from a sibling product. For a tool of this complexity, the description is complete enough for an agent to call it correctly without needing 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 coverage is 100% and both parameters have descriptions, but the tool description adds substantial meaning: it explains the `plan` enum values (free/starter/pro/business/scale/cpa, and that cpa is the Partner Program) and the `country` behavior (which regions have local pricing, fallback for others, and that omitting country returns priced markets). It also clarifies the meaning of the returned fields. This goes well 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 states a specific verb and resource: 'List HelloBooks pricing plans' with explicit scope (monthly + annual prices, plan ladder, add-ons). It clearly distinguishes itself from the sibling `practice_management_info` by explicitly stating that practice management is a separate product and should NOT use this tool. The purpose is unambiguous and differentiated.

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 this tool (to retrieve pricing plans) and explicitly names an alternative (`practice_management_info`) for a specific case. It also tells when to filter by plan or country. However, it doesn't provide a broader 'when not to use' beyond practice management, nor compare against other list tools like `list_credit_packs` or `list_features`. Still, the key alternative is clearly identified.

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 that each entry includes an effective-from date and authoritative source URL, and advises agents to confirm rates against the source before quoting. It does not explicitly state that the tool is read-only, but the term 'list' implies a non-mutating operation. It adds useful context about data provenance and reliability, which goes beyond a minimal description, though it could have explicitly stated that it does not compute or assign taxes.

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 that is front-loaded with the core purpose, followed by the jurisdiction breakdown, filter explanations, and usage guidance. While lengthy, every sentence provides necessary information given the tool's complexity—eight jurisdictions, multiple tax types, and schemes. It is structured with clear punctuation and clauses, making it readable. It could be broken into sections for easier scanning, but it is not wasteful or redundant.

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 adequately describes the return structure ('Every entry carries an effective-from date and an authoritative source URL'). It covers all major use cases, provides filter options, and mentions the authoritative sources by name. It also distinguishes itself from related tools. Given the tool's breadth, this description is exceptionally complete, leaving little ambiguity about what the agent will receive or when to invoke it.

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

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

Schema description coverage is 100%, but the description adds substantial meaning beyond the schema. It lists all supported countries with their ISO codes, enumerates the tax types with examples, and explains the scheme values (standard, reduced, zero, exempt, etc.) in context. For instance, it clarifies that 'composition' applies to India and 'input-taxed' to Australia, giving the agent concrete expectations for each filter value. This greatly enhances parameter understanding beyond the schema's basic 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 opens with a clear verb+resource: 'List statutory tax-rate slabs by jurisdiction', and immediately enumerates all supported jurisdictions with their specific rates, distinguishing it from sibling list tools (list_articles, list_features, etc.) and the lookup_tax_rate tool by explicitly stating it is the public statutory reference. The detailed enumeration makes the tool's exact scope 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 explicitly states when to use: 'Use this when a user asks "what is the GST rate on X?", "what VAT band does Y fall into?", or "what are the composition slabs in India?"' and when not to use: 'for an org-specific tax assignment use the authenticated books_classify_event tool.' This provides clear guidance and an alternative, fully satisfying the usage guideline criterion.

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 full burden. It discloses the curated nature (not a live mirror) and explains what each video returns. There's no contradictory information. Adding details about rate limits or auth needs would improve it, but current disclosure is sufficient.

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 states purpose and returns, second lists filters, third gives usage guidance and caveat. Front-loaded, every sentence adds value, no fluff.

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

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

No output schema, but the description enumerates return fields (title, description, category, watch URL, embed URL, thumbnail), covering user expectations. Filtering options and caveats are explained. No gaps for intended 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 coverage is 100% with parameter descriptions already present. The description adds minor context (e.g., query matches title+description) but does not significantly expand beyond the schema. 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 it lists curated HelloBooks product videos with specific fields (title, description, watch URL, etc.). It mentions filtering by category, featuredOnly, and query. Sibling tools are unrelated (analysis, compliance), so this tool is distinct and its purpose is 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 explicitly says 'Use this when a user asks for a demo, walkthrough or video.' It also notes the curated nature and that the response includes the channel URL for the full catalog. While it doesn't explicitly state when not to use it, the guidance is clear and helpful.

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, but description fully discloses what the tool returns (rail types, use-cases, issuing authority, support level, operational notes). It implies read-only behavior without stating it directly.

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

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

One paragraph, front-loaded with purpose. Contains a lot of information but is still relatively concise. Could be slightly more structured (e.g., bullet points), but no unnecessary 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?

No output schema, but description lists what the response contains (rail, use-cases, authority, support, notes). All parameters covered, filtering explained, defaults provided. Complete for a list 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% coverage with descriptions. The description adds context: default useCase scope, examples of IDs (e.g., 'in-upi'), and enumerates many payment methods, which adds 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?

Description clearly states it lists local payment methods with specific examples (UPI, RuPay, etc.) and details on what it returns (rail, use-cases, etc.). It is distinct from sibling tools which are analytical/compliance in nature.

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?

Description explicitly states filtering capabilities by country, useCase, rail, or id. It also mentions default scope for useCase and how to widen it. No explicit when-not to use, but siblings are different so 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?

With no annotations, description carries full burden. It explains the two lookup modes, confirms it's a deterministic vs fuzzy match, and states returns (rate, score, source URL). Lacks mention of error handling or edge cases like no match.

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-structured, front-loaded with purpose, then details modes, examples, and sibling differentiation. Every sentence adds value; 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 3 params, full schema coverage, no output schema, and no annotations, description covers both use cases, examples, and sibling tool. Lacks explicit return format details but lists returned fields. Slightly incomplete for edge 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?

Schema coverage is 100%, baseline 3. Description adds significant value: shows id format examples, explains id overrides country/category, notes category is free-text fuzzy, and provides example queries. This exceeds 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?

Description clearly specifies the tool picks a single tax-rate slab by exact id or fuzzy country+category match. It uses strong verbs ('pick','lookup') and distinguishes from sibling list_tax_rates for broader exploration.

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 tells when to use (e.g., 'what slab does X fall into in India?') and when not to (use list_tax_rates for broader exploration). Provides concrete examples and excludes customer data.

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?

While no annotations are provided, the description details the behavior: it returns tier structure, status, and points to next tier based on inputs. It also notes that the retired SKU is gone and clarifies identity with a plan ID. It does not explicitly state it's read-only, but that is clear from 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 a single paragraph that efficiently conveys purpose, usage modes, and distinctions. It is dense but not verbose; some structure (e.g., bullet points) could improve readability, but it remains clear.

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 what is returned for each input scenario (full ladder with no args, status with points, derived status with clients). It also provides necessary context about the partner program's relationship to list_plans and distinguishes a different product.

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 explains parameters. The description adds value by explaining how points are calculated (1 pt for Pro, 4 pts for Business) and how different parameter combinations map to different outputs.

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

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

The description clearly states the tool returns the Partner Program structure, specifies the resource, and distinguishes it from sibling tools like list_plans and a different product (HelloCPA Practice Management).

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 input combinations (no args, with points, with client counts), and clarifies what the Partner Program is not, preventing misuse.

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

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

No annotations are provided, so the description carries the full burden. It implies a read-only operation by stating it returns info, and it discloses availability status (US shipped, others roadmap). However, it doesn't explicitly state that the tool is non-destructive or read-only.

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 containing essential information: product definition, distinctions, pricing, markets, and usage. No redundant or unnecessary words.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description is fully complete. It covers the return content, usage modes, pricing, market availability, and sibling differentiations.

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

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

Schema coverage is 100% for the single country parameter, so baseline is 3. The description adds significant value by explaining the two usage modes (omit for full matrix, include for single region) and listing the specific country codes and their status. This goes 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 returns HelloCPA Practice Management info, defines the product, and distinguishes it from the Partner Program and list_plans. It uses specific verbs and resource references.

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 when to use this tool (to get PM info) and what not to use it for (NOT Partner Program, NOT a tier in list_plans). It also explains the two calling conventions: with or without country argument.

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