Inferventis MCP Server

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

Discloses connectivity requirement (bank API credential) and default behavior when account_id omitted. However, lacks details on rate limits, error handling, or data freshness. No annotations present, so description carries full burden; still reasonably transparent.

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

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

Four well-structured sentences: first states purpose, second and third provide usage alternatives, last states prerequisite. No fluff, 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?

No output schema exists, and description only says 'Returns a list of transactions' without detailing fields. Lacks pagination or cursor info. Adequate but could be more complete given no annotations or 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?

Input schema has 100% coverage with clear descriptions. Description adds value by explaining the effect of omitting account_id (returns all accounts), going 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 retrieves bank account details and transaction history. It explicitly distinguishes from sibling tools open_banking_transactions and stripe_payments by highlighting different use cases.

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 guidance on when to use this tool vs. alternatives: prefer open_banking_transactions for Open Banking providers, prefer stripe_payments for Stripe merchant accounts. Also mentions credential requirement.

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 description carries full burden. It discloses the data source (CoinAPI), that it consumes quota, and the key requirement. No side effects or destructive actions are mentioned, which is appropriate for a read-only conversion tool. Could be slightly more detailed about response format or rate limits, but 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?

Description is five sentences long, each contributing meaningful information. It is front-loaded with the main action and then specifies usage context. Could be trimmed slightly but remains efficient and well-structured.

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

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

Given the tool has 3 parameters, no output schema, and no annotations, the description covers all essential aspects: purpose, usage conditions, alternatives, prerequisites, and examples. An agent can confidently decide whether to invoke this tool based on the description alone.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by providing example pairs and clarifying that amount is denominated in the 'from' currency and that codes are case-insensitive. This goes beyond what the schema provides.

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

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

Description clearly states it converts amounts between any cryptocurrency and fiat pair, or two cryptocurrencies, using real-time rates from CoinAPI. It distinguishes between crypto-to-fiat, fiat-to-crypto, and cross-crypto conversions, making the purpose 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?

Explicitly states when to use: when conversion involves at least one cryptocurrency and live CoinAPI rates are needed. Also provides alternative tools for spot prices (crypto_price) and fiat-only conversions (currency_convert, fx_convert_lite), and notes the requirement for a CoinAPI key.

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 bears full burden. It implies a read-only operation ('retrieves') but does not explicitly state non-destructive nature, rate limits, or authentication needs. For a simple data retrieval tool, it is adequate but not fully transparent.

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

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

The description is a single paragraph that fronts the purpose and key details. It is reasonably concise without excess, though it could be slightly trimmed 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 the tool has three parameters and no output schema, the description explains return fields and provides examples. It lacks error handling or rate limit info, but for a simple price tool, it is sufficiently complete.

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

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

The input schema already has 100% description coverage, so the description adds value by listing example coins, stating case-insensitivity, and explaining the 'include_market_data' parameter's effect. This goes beyond the schema's 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 it retrieves real-time price data for any cryptocurrency on CoinGecko, specifying the returned fields (price, 24h change, market cap, volume). It also lists examples, differentiating from the sibling 'crypto_price_basic' by implying comprehensive 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?

Explicitly states when to use the tool: when an agent needs current price, valuation, portfolio monitoring, or market reports. However, it does not mention when not to use or explicitly differentiate from 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?

Since no annotations are provided, the description carries the full burden. It states the data source (CoinGecko public API), the returned fields (price, percentage change, timestamp), and mentions it is a lightweight variant. However, it does not disclose potential rate limits or authentication requirements, but for a simple read query this is adequate.

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

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

The description is concise at four sentences, all essential. It is front-loaded with the core purpose, and every sentence adds distinct value: purpose, output, guidance vs sibling, input flexibility, and default currency.

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 describes the return fields. It covers usage context, alternative tool, supported inputs, and default behavior. For a simple price retrieval tool, this provides sufficient completeness for an agent to use it correctly.

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

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

Schema coverage is 100%, and the description adds useful context beyond the schema: it specifies the default target currency (USD), acceptable ISO 4217 codes, and that coin accepts both ticker symbols and full names in a case-insensitive manner. This enhances understanding beyond the structured 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 current spot price and 24-hour change for any cryptocurrency. It distinguishes itself from the sibling tool crypto_price by being a lightweight variant that omits extended market data, making its purpose specific and unambiguous.

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

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

The description explicitly tells when to use this tool ('when only the raw price and direction are needed') and when to prefer the alternative ('when the agent also needs market capitalisation, trading volume, or richer structured output'). It also specifies supported assets and input formats.

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 bears full burden. It discloses that it uses live exchange rates and returns the converted amount, rate, and timestamp. However, it lacks details on potential limitations, such as rate update frequency, data source reliability, or what happens on network failure.

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: the first defines the core action, the second provides usage guidance and output details. No redundant words, perfectly front-loaded with the most critical 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 covers input (currency codes, amount), output (converted amount, rate, timestamp), and use cases. It does not mention error handling, rate limits, or how often rates update. For a simple conversion tool with full schema coverage and no output schema, this is nearly complete.

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

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

Schema coverage is 100%, so the description adds modest value beyond the schema. It confirms that any ISO 4217 pair is accepted and gives examples, but this is already implied by the format description in the schema. The schema already states amount must be positive, so no new semantic information.

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 'converts', the resource 'monetary amount', and the scope 'from one currency to another using live exchange rates', effectively distinguishing it from siblings like currency_convert_basic and fx_converter which may have different features or data sources.

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 use cases ('convert prices, invoices, payments, or financial figures between currencies in real time'), giving clear context. However, it does not mention when not to use this tool or suggest alternatives like currency_convert_basic for simpler needs.

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

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

With no annotations, the description fully bears the transparency burden. It discloses the use of the Frankfurter API, the minimal response schema (no rate timestamp, base currency metadata, or attribution), and unsupported currency types. However, it does not explicitly state that the tool is read-only or safe, which is implied but not explicit.

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

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

The description is adequately sized and front-loaded with the core action. It contains multiple sentences, each adding value (purpose, comparison, limitations). While slightly verbose, it remains efficient and well-structured, earning a 4.

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 explains the return values (converted amount and exchange rate). It also covers the data source, limitations (no crypto), and use cases. This provides complete context for an agent to understand 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?

Since schema description coverage is 100%, the baseline is 3. The description adds that amount must be positive (already in schema) and lists major currency codes, but does not provide additional semantic depth beyond what the schema already conveys.

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

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

The description clearly states the tool converts monetary amounts between fiat currencies using live exchange rates, specifying the data source (Frankfurter API/ECB). It distinguishes itself from sibling 'currency_convert' as a lightweight variant that returns minimal response, thus meeting the 'specific verb+resource' and sibling differentiation criteria.

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 this tool (when only converted value and rate are needed) and when to use the alternative 'currency_convert' (for richer output or historical context). It also clarifies the tool does not support cryptocurrency and directs to 'crypto_price' or 'crypto_fx_rates' for such cases, providing excellent usage 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?

Without annotations, the description fully discloses the data source (open public exchange rate feed for informational use), return values (converted amount and rate), and scope (fiat only, no crypto). It lacks mention of idempotency but conversion tools are inherently 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 four sentences, each serving a distinct purpose: main function, return info, usage guidance, and crypto exclusion. No redundant text, front-loaded with core 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?

Given no output schema, the description adequately covers input, output, rate source, and alternatives. With many siblings, it provides sufficient information for correct tool selection and 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% with clear parameter descriptions. The description adds context about live rates and fiat currencies but does not improve upon the schema's definition of the three parameters.

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

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

The description clearly states it converts monetary amounts between fiat currencies using live exchange rates from an open API, and explicitly differentiates from sibling tools like currency_convert and currency_fx_lite by specifying use cases.

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 guidance on when to use this tool (as alternative when others are unavailable), when to prefer other tools (for ECB-auditable rates use currency_convert, for lightweight use currency_fx_lite), and when not to use (crypto).

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 return of converted amount and rate, and use of live mid-market rates. Lacks details on rate freshness or error handling, but sufficient for a lightweight tool.

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

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

Front-loaded with core action, followed by usage guidance and exclusions. No redundant sentences, very 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?

Adequately describes return shape (converted amount and rate) and covers alternatives and exclusions. No output schema, but sufficient for simple 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 already covers all three parameters with descriptions. Description reiterates 'major ISO 4217' and positivity, adding little new 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?

Clear verb 'Converts' and specific resource 'FX amount between two fiat currencies using live mid-market rates'. Distinctly differentiates from siblings like currency_convert and crypto_fx_rates.

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 (lightweight, ad-hoc, numeric result) and when to prefer alternatives (richer metadata, Frankfurter rates). Also excludes crypto pairs with clear redirection.

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 the tool supports historical rate lookup and sources from ECB, but it does not detail potential limitations (e.g., date range, rate availability, error handling). Still, it adds meaningful behavioral context beyond a bare-bones description.

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

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

The description is well-structured and front-loaded with the core purpose, followed by usage scenarios and comparisons. It is somewhat lengthy but every sentence adds value; a minor reduction could improve conciseness 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 the presence of multiple sibling tools and the requirement to handle both live and historical rates with a specific data source, the description covers the key contextual points. The lack of an output schema is compensated by the clear purpose and usage guidance. It could mention return format or error behavior for full completeness, but it is adequate.

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

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

The input schema already covers all three parameters (amount, from, to) with 100% description coverage. The description does not add additional meaning or usage details for these parameters beyond what is in the schema, so the 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 that the tool retrieves live and historical fiat currency exchange rates from the ECB-sourced Frankfurter API. It distinguishes itself from siblings by emphasizing historical and auditable rate retrieval, making its purpose specific and unambiguous.

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

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

The description provides explicit guidance on when to use this tool (historical rates for accounting/tax/audit, ECB source requirement) and explicitly names alternatives for other scenarios: currency_convert for rich live conversions, currency_convert_lite or currency_fx_lite for lightweight live conversions, and crypto_fx_rates for cryptocurrency pairs. This is exemplary.

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 clearly states calculations are local and deterministic, but with no annotations, the burden is on the description to disclose all behavioral aspects. It does not mention potential error conditions (e.g., division by zero, negative rates) or output format specifics, which is a gap for a tool with multiple calculation types.

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

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

The description is a single dense paragraph with 10 sentences. It front-loads the main purpose effectively. While it could be more succinct with bullet points, every sentence serves a clear purpose, earning a high score without being wordy.

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 (6 calculation types, 11 parameters, no output schema), the description provides thorough coverage: each calculation type's inputs and outputs are explained, and the local computation nature is stated. An agent can fully understand when and how to 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?

Schema description coverage is 100%, and the description adds significant value by explaining which parameters apply to which calculation types, e.g., 'rate: Annual interest... Used for compound_interest, loan_repayment, present_value, and future_value.' This surpasses a baseline of 3.

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

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

The description explicitly states it performs 'precise financial calculations across six calculation types' and enumerates each type with its specific use, such as 'compound_interest computes the final value and total interest earned'. This clearly differentiates it from sibling data-fetching tools like crypto_price or finnhub_stock_quote.

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

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

The description implicitly guides usage by detailing each calculation type's purpose (e.g., 'loan_repayment calculates the monthly payment... for a mortgage or loan') and states 'All calculations are performed locally with no external API dependency', which implies it should be used for computations rather than data retrieval. However, it stops short of explicitly stating when to avoid this tool in favor 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?

The description discloses local execution and no external API dependency. It notes that the tool does not fetch live market data. No annotations are present, so the description carries the burden; it lacks details on error handling or parameter validation, but is otherwise transparent.

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

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

The description is concise, well-structured, and front-loaded: it states purpose, return value, differentiation from sibling, and usage guidance in a logical flow. 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 the complexity (6 parameters, multiple calculator types, no output schema, no annotations), the description covers purpose, parameter dependencies, and sibling differentiation. It lacks explicit return format details or error handling, but is otherwise complete.

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

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

Schema coverage is 100% (baseline 3). The description adds value by associating specific parameters with calculator types (e.g., 'Required for compound_interest, loan_repayment...'), and by clarifying rate format (decimal). This goes 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 clearly states it performs common financial calculations locally, enumerates the supported types (compound interest, loan repayment, etc.), and specifies that it returns a single numeric result. It effectively distinguishes itself from the sibling financial_calculator.

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 advises when to use this lite variant versus the full financial_calculator, and also directs the agent to other tools (stock_quote, crypto_price, currency_convert) for live data, providing comprehensive selection 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?

No annotations provided, so description bears full burden. It mentions real-time, PSD2 compliance, but lacks details on rate limits, side effects, or response structure. Adequate but not comprehensive.

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

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

Three concise sentences with front-loaded purpose and key details. 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?

For a tool with all optional parameters and no output schema, the description sufficiently covers purpose, data returned, and use cases. Could be improved by noting output structure, but return fields are mentioned.

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; description adds value by explaining default behavior for omitted account_id and lists returned fields (categorised transactions, merchant names, etc.).

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 real-time bank account transaction history and balance data, specifying supported banks, compliance, and returned data fields. It distinguishes from sibling tools like stock_tool and crypto_price.

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

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

Explicitly lists use cases: analyzing spending patterns, verifying balances, assessing affordability, etc. Does not mention when not to use or alternatives, but context from sibling tools implies differentiation.

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

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

No annotations provided, so description carries full burden. It discloses return format (ranked results with schemas, pricing, call instructions), up to 10 results, and the embedding model. Could mention non-destructive nature, but still transparent.

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

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

Four sentences, well-structured, no fluff. First sentence states purpose, second describes input/output, third gives usage guidance, fourth specifies result count.

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

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

No output schema, but description explains returns adequately. Covers input, behavior, and usage. No missing critical information for task completion.

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 description adds meaning: top_k default and when to increase, intent should be goal not tool name with examples, model_hint optional for model-specific ranking. Exceeds schema information.

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 discovers relevant tools using semantic search. It distinguishes from sibling tools by being a meta-tool for finding which specific tool to call.

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 (when you don't know the tool) and when not to use (if you already know the tool name). Provides examples and advises using higher top_k when ambiguous.

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?

Although no annotations are provided, the description clearly states it returns 'the current price as a single numeric value' and explains it is a lightweight variant omitting intraday high/low, percentage change, etc. It does not discuss latency or data source, but for a simple read operation, the behavioral description is adequate.

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 is purposeful. The description is front-loaded with the primary purpose, then details the lightweight nature, usage contexts, and exclusions. It is concise and well-structured 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?

Given the tool's simplicity (one parameter, no output schema), the description is fully complete. It covers what the tool does, what it omits, when to use it versus alternatives, and what it does not support. No critical information is missing.

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

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

The input schema has 100% description coverage for its single parameter 'symbol', with examples and validation details. The description reinforces the parameter's purpose by explaining it is a stock ticker and provides usage context. This adds value beyond the schema.

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

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

The description explicitly states 'Retrieves the current trading price for a publicly listed stock by ticker symbol.' It uses a specific verb ('Retrieves') and resource ('current trading price'), and distinguishes itself from the sibling tool 'stock_quote' by highlighting it is a lightweight variant that omits extra 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 provides clear guidance on when to use this tool ('when only the raw current price is needed') and when to prefer alternatives ('Prefer stock_quote when...'). It also explicitly states that it does not support cryptocurrency prices, directing to 'crypto_price' for digital assets.

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 tool returns real-time data and requires a configured API key. While it does not detail error handling or rate limits, it provides essential behavioral context beyond what is structured.

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 a well-structured single paragraph of about 110 words. Every sentence provides value: purpose, return data, usage guidance, exclusions, and prerequisites. No redundancy or filler.

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

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

Despite no output schema, description lists key return fields (price, high/low, change, previous close, sector, exchange). It covers prerequisite (API key) and distinguishes from siblings. For a 2-parameter tool, this is thorough and enables correct usage.

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

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

Schema covers both parameters with descriptions, and the description adds meaning: it explains that include_company_info controls inclusion of sector/exchange, and notes performance benefit when set to false. This goes beyond schema to clarify parameter behavior.

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 retrieves real-time stock price quotes and company information, listing specific data points returned (current price, high/low, change, sector, exchange). It uses a specific verb 'Retrieves' and resource, and distinguishes itself from sibling tools like stock_price_lite.

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 instructs when to use stock_quote, including contrasts with stock_price_lite for price-only needs, crypto_price for cryptocurrencies, and currency_convert for fiat conversion. Also notes the requirement of a Finnhub API key, providing clear context for agent decision-making.

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 cover behavioral traits. It explains that results are filtered by query_type and are a lightweight variant. However, it does not explicitly state that the operation is read-only or mention any rate limits. The return format is described as a 'simple records array', but details of record fields are omitted.

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

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

The description is front-loaded with the core purpose, then provides usage guidelines and comparisons with siblings in a concise manner. Every sentence adds value without redundancy.

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

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

Given there is no output schema and no annotations, the description provides adequate context: purpose, usage, alternatives, and parameter details. It could be improved by specifying the structure of returned records, but it is sufficient for an 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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the accepted values for query_type ('recent_payments', 'refunds', 'disputes', 'subscriptions') and the default and maximum for limit (defaults to 10, max 50).

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 payment and charge records from a Stripe merchant account, filtered by query type. It distinguishes itself from sibling stripe_payments as a lightweight variant returning a simple array, and from open_banking_transactions and bank_accounts for bank data sources.

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 (review recent charges, refunds, disputes, subscriptions) and provides alternatives: prefer stripe_payments for full objects with customer details, prefer bank tools for bank data. Also notes the requirement for a configured Stripe API key.

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 disclose behavioral traits. It states 'real-time' but does not clarify read-only nature, authentication needs, rate limits, or error behavior. This lack of detail leaves the agent uninformed about side effects or constraints.

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

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

The description is three sentences, concise and front-loaded. The first sentence states the primary purpose, the second adds connection detail, and the third lists use cases. No extraneous information.

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

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

For a simple tool with two parameters and no output schema, the description covers main use cases and data types. However, it omits return format, error handling, and pagination, which would help completeness. It is adequate but not outstanding.

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

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

Schema description coverage is 100%, so the schema already documents both parameters. The description adds use-case context but no new parameter-specific details beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves live payment data from Stripe, listing specific resources (payment intents, failed charges, customer records, subscription status) and verbs (Retrieves, Connects). It distinguishes from siblings like crypto_price or stock_tool by being Stripe-specific.

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 when-to-use scenarios (investigate failures, audit transactions, check subscriptions, etc.). However, it does not mention when not to use or suggest alternative tools, which is acceptable given the distinct sibling names.

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, but description adequately covers data sources, no-auth requirement, sorted output, and returned fields. Lacks mention of potential rate limits or data freshness, but overall transparent.

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

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

Description is a single coherent paragraph with logical flow: function, output, usage guidance. Efficient though slightly lengthy; each sentence adds value.

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

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

No output schema, but description lists all returned fields. Covers input parameters fully, source limitations, and usage boundaries. Complete for a data retrieval tool with these optional params.

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 well-described parameters. Description adds minimal extra value beyond schema, such as 'no API key required' but does not deepen parameter understanding.

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

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

Description specifies the tool retrieves real-time headlines and summaries from BBC News and The Guardian across nine topics, listing returned fields and sorting order. It clearly distinguishes from sibling web_url_reader for full article reading.

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 (current news, summarising, research, monitoring, briefing) and when not to use (reading full article, needing other sources), with direct reference to web_url_reader as 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?

Given no annotations, the description fully covers behavioral traits: returns plain text stripped of HTML, navigation, ads; returns title, meta description, word count, main body; does not execute JavaScript; will not work with paywalled or JavaScript-rendered apps; indicates if content truncated.

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 core functionality first, then use cases, then limitations. While comprehensive, it is not overly verbose; every sentence adds value. 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?

No output schema, but description explains what is returned (title, meta description, word count, main body). It covers limitations (no JavaScript, paywalls) and optional parameters. Adequate for the tool's complexity.

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

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

Schema coverage is 100% (baseline 3). Description adds significant value: explains max_chars default and how to adjust (higher for long articles, lower for headlines), include_links for crawling, and provides URL examples. This goes well beyond schema descriptions.

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

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

The description clearly states that the tool fetches a public web page and returns clean readable plain text. It provides specific use cases (summarize article, extract facts, verify claim) and distinguishes itself from the sibling tool web_news_headlines.

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 tells when to use (reading content of a specific URL) and when not to use (not for discovering headlines; use web_news_headlines). It also warns about limitations like no JavaScript, paywalled, or login-protected pages.

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