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

Annotations already indicate readOnlyHint=true. The description adds that summary totals apply only to the filtered subset, that hidden accounts are excluded by default, and that both account_type and subtype are checked for filtering—all beyond what annotations provide.

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 the primary purpose, no redundant text. Every sentence adds important detail without verbosity.

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 return values (summary fields) and filtering nuances. It covers all necessary context for a simple read-only tool with two 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 covers 100% of parameters, but the description adds value: lists example account types, explains the dual-field filtering mechanism, and notes that summary totals reflect only the filtered subset. This helps the agent use parameters correctly.

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 retrieves all accounts with balances and summary fields (total_balance, total_assets, total_liabilities), with optional filtering by account type and exclusion of hidden accounts. This distinguishes it from sibling tools like get_balance_history or get_transactions.

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 context on filtering options and default behavior (hidden accounts excluded), but does not explicitly contrast with sibling tools or specify when not to use it. The hint about dual-field filtering aids correct invocation.

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 context beyond the readOnlyHint annotation by disclosing downsampling behavior ('Weekly and monthly modes downsample by keeping the last data point per period') and noting the accounts array in the response. No contradictions with annotations.

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

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

The description is compact (4 sentences), front-loaded with the core purpose, and each sentence adds non-redundant information. No filler 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 6 parameters (1 required) and no output schema, the description explains return fields, accounts array, granularity modes, and filtering. It covers pagination implicitly via offset/limit but does not mention ordering or error conditions. Fairly complete for the complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the effect of granularity on response density and mentioning filtering options. It also describes the response structure, which is not in the input schema.

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

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

The description specifically states 'Get daily balance snapshots for accounts over time' and lists the returned fields. It clearly distinguishes itself from sibling tools like get_accounts or get_transactions, which deal with different data.

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

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

The description explains that granularity is required and gives guidance on when to use weekly/monthly ('Use weekly or monthly for longer time ranges'). It also mentions filtering by account_id and date range. However, it does not explicitly state when not to use this tool 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?

Annotations already indicate readOnlyHint=true. The description adds value by detailing that budgets are from native tracking, returns effective budget per category and amounts map, and explains resolved totals for parent categories. 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?

Three sentences, front-loaded with the main action, and every sentence adds necessary information. No redundancy 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?

No output schema, but the description compensates by explaining return values (effective budget, amounts map, resolved totals). It provides sufficient context for a simple read operation.

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 (active_only) with a clear description. The tool description adds no additional meaning to the parameter beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description clearly states it retrieves budgets from Copilot's native budget tracking, specifies the return content (current-month effective budget per category plus full amounts map), and explains behavior for parent categories. It distinguishes itself from sibling tools by focusing on budgets.

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 budget retrieval but does not explicitly state when to use this tool versus alternatives like get_transactions or get_categories. No exclusions or when-not-to-use 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?

Beyond the readOnlyHint annotation, the description discloses that the tool reads from a local cache that may not contain complete transaction history, adding valuable behavioral context.

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

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

The description is concise, with three sentences that front-load the purpose, then provide usage context and a behavioral caveat. 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?

For a simple tool with no parameters and no output schema, the description sufficiently covers purpose, usage, and limitations. It lacks specifics about return structure but is adequate for an agent.

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

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

The input schema has zero parameters, and the description does not need to add parameter details. Per guidelines, a baseline score of 4 is appropriate.

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

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

The description clearly states the tool retrieves information about the local data cache, specifically the date range and total count of cached transactions. This distinguishes it from sibling tools like get_accounts or get_transactions.

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 notes the tool is 'useful for understanding data availability before running historical queries,' implying a preparatory use case. However, it does not explicitly state when not to use it or name alternative tools.

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

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

Annotations provide readOnlyHint: true. The description adds that the tool includes categories with $0 spent, matching UI behavior, and explains the behavior of different views. This goes beyond annotations.

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

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

The description is fairly concise given the complexity of three views and multiple parameters. It is front-loaded with the main purpose. Minor redundancy could be trimmed, 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?

The description covers all major aspects: views, filtering, parent_id, date options, and the inclusion of zero-spend categories. Despite no output schema, the description is sufficient for an agent to use the tool correctly.

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

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

The input schema covers all parameters with descriptions (100% coverage). The description adds extra meaning by explaining that categories with $0 spent are included, and that period takes precedence over dates, which is not in the schema.

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

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

The description clearly states 'Unified category retrieval tool' and explains three distinct views (list, tree, search) with specific use cases. It distinguishes itself from sibling tools like get_accounts and get_transactions by focusing on category data.

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

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

The description explains when to use each view (e.g., list for spend totals, tree for hierarchy, search for keyword) and how to filter by date or parent_id. However, it does not explicitly state when not to use this tool or mention alternatives.

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

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

Discloses what the tool shows (per-institution sync health, timestamps, login requirements, error states) beyond the readOnlyHint annotation. No contradictions; annotation reinforces the read-only 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?

Two sentences with no wasted words. First sentence states purpose, second adds detail and usage guidance. Front-loaded with key 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?

Covers purpose, usage, and output details adequately for a parameterless read tool. Lacks explicit output structure format, but the description of what it shows is sufficient 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?

No parameters exist, baseline score of 4. Description adds no parameter info because none are needed.

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

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

Clearly states it retrieves connection status for all linked financial institutions, specifying the resource (connection status) and the verb (get). Distinguishes from siblings like get_accounts by focusing on sync health, timestamps, login requirements, and error states.

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 to use for checking last sync timestamps and identifying problematic connections, providing clear usage context. Does not exclude alternatives, but no sibling tool serves this specific 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?

The description adds significant behavioral context beyond the readOnlyHint annotation, explaining that the tool is always cached, ignores the --live-reads flag, and why (backend limitation). This helps the agent understand the tool's data freshness and constraints.

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

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

The description is three sentences, each serving a purpose: purpose, return fields, and cache behavior. It is front-loaded with the core function, then details, then important behavioral note. No unnecessary words.

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

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

For a tool with 5 optional parameters and no output schema, the description covers purpose, return fields, filtering, and cache behavior. It does not explain pagination parameters (limit/offset) but those are standard and described in the schema. Overall, it provides enough context for the agent to select and use the tool.

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

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

The input schema has 100% description coverage, so the description doesn't need to repeat parameter details. However, it adds value by clarifying that the tool returns 'daily data points' and 'contribution records per month,' which are not in the schema. This enriches the agent's understanding of the output.

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 monthly progress snapshots for financial goals, listing specific return fields (current_amount, target_amount, daily data points, contribution records). It distinguishes from siblings like get_goals or get_balance_history by focusing on monthly history snapshots.

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 this tool is cache-only and always returns cached data, with no live-mode counterpart. It explains the reason (GraphQL endpoint does not expose goal data), guiding the agent on when to use this tool versus others. It could be improved by explicitly stating when not to use, but 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?

Discloses beyond readOnlyHint: always returns cached LevelDB data regardless of --live-reads flag, alerting the agent to staleness. No contradiction with annotations.

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

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

Two sentences efficiently convey purpose and key constraint. Could be slightly more structured with bullet points, but no 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 simple read-only tool with one parameter and no output schema, description covers all essential aspects: goals covered, fields returned, cache limitation, and no live mode.

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

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

Input schema has 1 parameter with full description. Description adds no new info beyond schema, so baseline 3 is appropriate.

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

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

The description clearly states it retrieves financial goals from Copilot's native goal tracking, listing types (savings, debt, investment) and details returned. It distinguishes from siblings like get_accounts and get_budgets.

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 cache-only nature and lack of live-mode counterpart, indicating when to use. No explicit alternatives among siblings, but context implies this is the only goal tool.

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

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

Discloses that cost_basis may be unavailable for cash-equivalent positions, adding value beyond the readOnlyHint annotation. No contradictions with annotations.

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

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

Concise two-sentence description plus a note, front-loaded with main action and no unnecessary detail.

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 key aspects: returned data, filters, optional history, caveat about cost basis. Pagination is implied by limit/offset in schema. No output schema, but description compensates.

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

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

Schema coverage is 100%, so description adds marginal value by explaining return fields and joins, and noting default false for include_history. Parameters are well-documented in 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 'Get current investment holdings with position-level detail' and lists specific fields returned (ticker, name, quantity, etc.), distinguishing it from siblings like get_accounts and get_balance_history.

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?

Mentions filtering by account or ticker and optional history inclusion, providing clear context for when to use this tool. Does not explicitly state when not to use, but purpose is distinct from siblings.

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

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

Annotations indicate readOnlyHint=true, which is consistent. The description adds that the tool returns daily and high-frequency data and includes OHLCV data. However, it does not discuss pagination behavior, data freshness, or rate limits. The schema covers pagination parameters, so the description provides moderate added value.

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 core purpose, and includes essential details without 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, the description adequately covers return content (OHLCV) and filters. It does not specify default behavior when no filters are applied (e.g., returns recent prices for all assets), but this is a minor gap. Overall, it is fairly complete for a read-only data retrieval tool.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds meaning by explaining price_type enum values ('daily' as monthly aggregates, 'hf' as high-frequency intraday) and mentions OHLCV data availability, 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 retrieves investment price history for portfolio tracking, specifies asset types (stocks, ETFs, mutual funds, crypto), and lists filters (ticker, date range, price type). This distinguishes it from siblings like get_holdings or get_balance_history.

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

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

The description provides clear context for when to use this tool (portfolio tracking) but does not explicitly state when not to use it or suggest alternatives. The sibling tools are related but the description implies its scope effectively.

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

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

Annotations already declare readOnlyHint=true, and the description adds details about output structure (one row per security/date), the meaning of the multiplier, the join with securities, and cache coverage. No contradictions.

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

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

The description is front-loaded with purpose and details, then usage guidance. It is somewhat lengthy but all sentences add value. Could be slightly more concise, but structure is logical.

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 fully explains the output structure, including the multiplier meaning and joined fields. All 5 parameters are well-documented. The tool's behavior is completely described.

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 good descriptions, but the description adds extra context (e.g., case-insensitive ticker, inclusive date bounds, effective_date field name). This enhances clarity beyond the schema.

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

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

The description clearly states it retrieves stock split events from the local Firestore cache, specifying verb 'get', resource 'stock split events', and scope. It distinguishes from siblings like get_investment_prices by noting that those return already-adjusted prices.

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 this tool ('when you need the split events themselves') and when not to ('you do NOT need to apply these multipliers to the prices yourself'). Also mentions coverage limitations, guiding the agent appropriately.

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

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

Annotations already indicate readOnlyHint=true. The description adds value by detailing the dual data sources and what each returns (estimated frequency, confidence score, next expected date, user-confirmed subscriptions). It does not contradict 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 three sentences, front-loaded with the purpose, and no unnecessary 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?

The description provides a good overview of the tool's behavior and output fields. However, because no output schema exists, a brief note on the overall output structure would improve completeness, though the mention of specific fields is helpful.

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 does not add significant meaning beyond the schema's parameter descriptions; it only explains the tool's purpose.

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 identifies recurring/subscription charges and explains it combines two data sources (pattern analysis and native subscription tracking). This distinguishes it from siblings like get_transactions which return all transactions.

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 finding recurring charges but does not explicitly state when to avoid using it or name alternatives like get_transactions for non-recurring 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?

The description discloses behavioral traits beyond the readOnlyHint annotation: it explains the tool reads from a local LevelDB cache that may lag, describes return format (human-readable category names, normalized merchant names), and details six distinct usage modes. No contradiction with annotations exists.

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-organized with numbered list for modes and key caveat upfront. However, it is slightly verbose with some redundant phrasing (e.g., 'Unified transaction retrieval tool' followed by detailed enumeration). Every sentence is useful, but conciseness could be improved.

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 (25 parameters, no output schema), the description thoroughly covers all usage modes, data source characteristics, caching latency, and output format. It provides complete guidance for an AI agent to correctly select and invoke the tool.

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

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

Despite 100% schema coverage, the description adds substantial contextual meaning by grouping parameters into intuitive modes (e.g., 'location-based: Use city or lat/lon with radius_km'). It explains how parameters interact, such as 'transaction_id ignores other filters' and 'exclude_split_parents avoids double-counting'. This goes far beyond the schema descriptions.

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

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

The description clearly states 'Unified transaction retrieval tool' and enumerates multiple specific modes, each with distinct purposes (filter-based, single lookup, text search, special types, location-based, tag filter). The name 'get_transactions' directly indicates the action and resource, effectively distinguishing it from sibling tools like 'get_accounts' or 'get_budgets'.

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 warns about cache lag and directs users to 'get_transactions_live' for real-time data. It outlines when to use each mode (e.g., 'single lookup: Provide transaction_id'). However, it lacks explicit when-not-to-use guidance or exclusion of other tools beyond the live alternative.

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 that it reloads from local database, returns cache info, and auto-refreshes. No contradiction with readOnlyHint=true. Adds value beyond annotations.

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

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

Three sentences, no fluff, front-loaded with purpose, then usage, then additional context. Every sentence earns its place.

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

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

Given no parameters and simple operation, description is complete. Mentions return of cache info, which is sufficient though slightly vague.

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; schema coverage 100%. Baseline score 4 for zero-param tool; description adds no parameter info but none 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?

Clearly states 'Refresh the in-memory cache by reloading data from the local Copilot Money database'. Verb and resource specific, distinct from sibling get_* tools.

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

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

Explicitly says when to use: after user syncs new transactions or when cache may be stale. Notes auto-refresh every 5 minutes but lacks explicit exclusions or alternatives.

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