Inferventis MCP Server

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 the credential requirement, the read-only nature (retrieves/returns), and the default vs. specific account behavior. It lacks rate limits or error handling details, but the key behavioral aspects are covered.

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 efficiently structured in three sentences, covering purpose, behavior, and usage guidance. The alternatives guidance adds length but is valuable, and 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 the tool's simplicity (two optional params, no output schema), the description covers the essential information: what it does, when to use it, sibling alternatives, and a credential prerequisite. It lacks exact return field details but is sufficient for an agent to select and invoke the tool correctly.

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

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

The schema already describes both parameters comprehensively (limit default/max, account_id behavior), so the description adds no new parameter-specific information beyond restating what the schema says. Since schema coverage is 100%, the baseline of 3 is appropriate.

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

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

The description clearly states it retrieves bank account details and recent transaction history via a bank API, using a specific verb and resource. It also distinguishes itself from siblings like open_banking_transactions and stripe_payments, 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?

The description explicitly states when to use bank_accounts (inspect balances, review spending, categorize transactions, reconcile records) and when to prefer alternatives (open_banking_transactions for PSD2 Open Banking, stripe_payments for Stripe merchant accounts). This provides clear decision guidance with specific scenarios.

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

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

With no annotations, the description carries the full burden. It discloses the data source (CoinAPI), the authentication requirement (CoinAPI key configured on the server), and quota consumption. It does not detail output format or error behavior, but key operational context is provided.

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 primary purpose. Each sentence adds distinct value: functionality, pair coverage, usage guidance, alternatives, and requirements. No fluff or redundancy.

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

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

The tool has no output schema, but the description covers purpose, usage, alternatives, authentication, and data source. It is complete for a conversion tool, though it omits explicit return value description and error handling, which would make it fully complete.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds no extra semantics beyond the schema, only reaffirming examples. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

Clearly states the tool converts an amount between cryptocurrency and fiat pairs or two cryptocurrencies, with explicit examples (BTC/USD, ETH/EUR, USD/BTC, BTC/ETH). This distinguishes it from sibling tools by focusing on conversion with a specific amount.

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

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

Provides explicit when-to-use guidance: 'Use crypto_fx_rates when the conversion involves at least one cryptocurrency and a specific amount must be converted.' It also names alternatives (crypto_price, crypto_price_lite, currency_convert, currency_fx_lite) and conditions for preferring them, including rationale like avoiding CoinAPI quota.

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 transparency burden. It discloses the live spot price nature, the CoinGecko listing scope, and the exact return fields, which is meaningful behavioral context. However, it does not mention error conditions, rate limits, or data freshness, which keeps it a 4 rather than a 5.

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

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

The description is a well-structured paragraph with no filler. Every sentence contributes either the core function, the return payload, supported asset examples, or usage guidance with alternatives. The length is justified by the need to differentiate from several closely related sibling tools.

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 3 parameters and no output schema, the description fully covers what the tool returns, the accepted input scope, and the conditions for use versus alternatives. It even warns against historical use. The context is sufficiently complete for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces the purpose of include_market_data ('full market picture') and gives examples of accepted coin symbols, but the schema already thoroughly describes all parameters. The description adds marginal 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 begins with a specific verb and resource: 'Retrieves real-time price data for any cryptocurrency listed on CoinGecko.' It clearly enumerates the returned data (price, 24h change, market cap, volume) and distinguishes itself from sibling tools by explicitly referencing crypto_price_lite 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?

Provides explicit when-to-use guidance ('Use crypto_price when an agent needs the full market picture'), when to prefer alternatives ('Prefer crypto_price_lite', 'Use crypto_fx_rates'), and exclusions ('Do not use for fiat-to-fiat', 'Do not use when historical data required'). This is textbook-level 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?

With no annotations provided, the description carries the burden and does disclose the API source, the specific return fields, and the omission of extended market data. It also notes support for 10,000+ assets and input flexibility, but lacks details on rate limits, error behavior, or timestamp format which would be useful.

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, then proceeds to variants and alternatives, and ends with input details. 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?

Since there is no output schema, the description appropriately specifies return values (price, percentage change, timestamp). It also covers input variety and default currency. While it doesn't mention error or edge cases, the description is sufficiently complete for the tool's simplicity.

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 both parameters are well-documented. The description reiterates the input formats and defaults, adding minor clarity (e.g., case-insensitive, ISO 4217) but does not meaningfully expand 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 current spot price and 24-hour change for any cryptocurrency via CoinGecko. It explicitly distinguishes itself as a lightweight variant of crypto_price, omitting extended market data, and names the exact output fields.

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 versus alternatives: use when only raw price and 24h direction are needed, prefer crypto_price for richer data, and use crypto_fx_rates for currency conversion. This is a textbook example of usage 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?

With no annotations, the description carries the behavioral burden. It discloses the data source (Frankfurter API/ECB), the response components (converted amount, exact rate, timestamp), and the currency scope. It does not mention rate limits, error conditions, or side effects, but the main behavioral aspects are covered.

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 appropriately sized, with each sentence serving a distinct purpose: function, output, supported currencies, when to use, alternatives, and exclusions. It is front-loaded with the core purpose and avoids extraneous 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?

With no output schema, the description explicitly names the return values: converted amount, exact exchange rate, and timestamp. It also covers data source, supported currencies, and alternatives. For a simple three-parameter tool, the description provides complete context for an agent.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by listing example currencies and clarifying that the amount is in the source currency. This complements the schema's parameter descriptions and provides additional context beyond field names.

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

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

The description opens with a specific verb and resource: 'Converts a monetary amount from one currency to another using live exchange rates.' It clearly distinguishes from siblings by naming currency_convert_lite, currency_fx_lite, currency_rates, and crypto tools with their specific purposes.

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

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

The description explicitly states when to use the tool: 'Use currency_convert when an agent needs to convert prices, invoices, salaries, payments...' It also provides direct alternatives: 'Prefer currency_convert_lite or currency_fx_lite when only the numeric converted amount...' and warns against crypto use, redirecting to appropriate tools.

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

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

With no annotations, the description carries the full burden and it does well by disclosing the data source (Frankfurter/ECB), return contents (converted amount and exchange rate), and the minimal response nature (no rate timestamp or source attribution). It also notes the lack of crypto support. It stops short of discussing error behavior or rate limits, but the key behavioral traits are covered.

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 output, followed by sibling comparisons and exclusions. While longer than average, each sentence contributes meaningful selection guidance. It could be condensed slightly, but the structure is logical and efficient.

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

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

For a simple conversion tool with no output schema and no annotations, the description provides sufficient context for an agent to select and invoke it correctly. It specifies return values, alternative tools, non-supported use cases (crypto), and gives currency code examples, making the tool's behavior and boundaries clear.

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 each parameter (amount, from, to) already described with format and constraints. The description adds only marginal context, such as 'any two fiat currencies' and example ISO codes, but does not materially enhance parameter understanding beyond the schema.

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

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

The description opens with a specific verb and resource: 'Converts a monetary amount between any two fiat currencies using live exchange rates from the Frankfurter API (European Central Bank data).' It clearly distinguishes itself from sibling tools by identifying as a 'lightweight variant of currency_convert' and contrasting with currency_convert, currency_fx_lite, currency_rates, and currency_convert_open.

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

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

Usage guidance is explicit and actionable: 'Use currency_convert_lite when only the converted value and rate are needed,' with specific instructions to prefer currency_convert for richer output, currency_fx_lite when ECB sourcing isn't required, currency_rates for historical data, and currency_convert_open as a fallback. It also excludes crypto pairs, directing users to crypto_price or crypto_fx_rates.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds context about the data source being 'an open public exchange rate feed suitable for informational use,' implying limitations for auditable purposes. It also explicitly states it does not support cryptocurrency pairs. However, it does not detail error behavior, rate limits, or the exact rate type (mid-market), but the core behavioral traits are 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 well-structured and front-loaded with the main action, followed by usage alternatives and exclusions. It is slightly verbose with multiple 'Prefer' statements, but each sentence carries meaningful information and justifies its place. It is appropriately sized for the complexity of the tool and its sibling context.

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

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

Given the tool's simple nature (3 params, no output schema, no annotations), the description is highly complete. It states what the tool returns (converted amount and rate), when to use it, when not to use it, and the underlying data source limitations. It also covers the sibling-tool landscape, making the decision context clear.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add additional parameter-specific details beyond what the schema already provides (e.g., positive number, ISO codes, case-insensitivity). No extra meaning is needed, but the description also doesn't enhance the semantics further.

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

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

The description clearly states the tool's purpose: 'Converts a monetary amount between two fiat currencies using live exchange rates from an open currency exchange API. Returns the converted amount and the rate applied.' It uses a specific verb (converts) and resource (monetary amount between fiat currencies) and distinguishes from siblings by describing itself as an alternative live-rate source.

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 and when-not-to-use guidance. It says 'Use currency_convert_open as an alternative live-rate source when currency_convert (Frankfurter/ECB) or currency_fx_lite are unavailable or rate-limited.' It also names preferred alternatives for different scenarios (accounting/compliance, minimal output, lightweight conversions) and excludes crypto pairs, pointing to crypto_fx_rates.

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

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

With no annotations, the description carries the transparency burden. It discloses return values (converted amount and exchange rate), data source behavior (live mid-market rates, non-ECB/Frankfurter by default), unsupported crypto pairs, and provider differences between lite tools. It does not disclose error handling or rate freshness details, but given the simplicity of the tool, this is a minor gap.

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

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

Although the description is longer than typical, every sentence earns its place. It is front-loaded with the core purpose and then systematically addresses usage guidance, alternatives, and exclusions. The length is justified by the complex sibling landscape, and there is no redundant or filler content.

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

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

The description is complete for a simple conversion tool: it states what it returns, when to use it, and how it differs from siblings. Since there is no output schema, the description clarifies that both the converted amount and rate are returned. It stops short of naming exact return fields, but this is not critical for a 'lite' tool.

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

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

All three parameters have detailed schema descriptions with examples and case-insensitivity, so schema coverage is 100%. The description adds some context by noting 'fiat currencies' and accepting 'all major ISO 4217 currency codes', which clarifies the parameter domain but does not substantially extend the schema's semantics.

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

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

The description clearly states the tool's function with a specific verb and resource: 'Converts a foreign exchange (FX) amount between two fiat currencies using live mid-market rates.' It also differentiates from siblings by naming alternative tools and their specific use cases, 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?

Explicit when-to-use guidance is provided: 'Use currency_fx_lite when only the numeric result is required and the ECB/Frankfurter data source is not specifically needed.' It also gives alternatives for richer metadata (currency_convert), ECB-specific rates (currency_convert_lite), historical rates (currency_rates), and open-source rates (currency_convert_open), along with crypto exclusions. This is exemplary 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?

With no annotations, the description carries the full burden of disclosing behavior. It clearly states the tool retrieves data (implying read-only), supports live/historical rates, is ECB-sourced, and explicitly excludes cryptocurrency pairs. It also notes a fallback source ('Frankfurter is unavailable'), which adds context. However, it does not mention rate limits, error behavior, or response format, so it is not maximally 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 longer than ideal but every sentence earns its place by providing essential guidance on when to choose this tool over alternatives. It is front-loaded with the core functionality, and the additional sentences are structured as clear comparisons. It is concise for the amount of useful information delivered.

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 thoroughly addresses the tool's purpose and sibling differentiation, but it omits a critical detail: the schema does not include a date parameter, yet the description heavily promotes historical lookup. This gap undermines completeness for an agent trying to use the tool correctly. Additionally, with no annotations or output schema, the description should clarify return behavior and any limitations, which it does not.

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?

While the schema has 100% coverage for the three parameters (amount, from, to), the description introduces a significant mismatch by claiming support for 'historical rate lookup for any past date' without including a date parameter in the schema. This could mislead an agent into attempting to pass a non-existent date parameter. The description adds no value beyond the schema for the actual parameters and actively creates confusion, so it falls below the baseline.

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

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

The description uses a specific verb ('Retrieves') and clearly identifies the resource ('fiat currency exchange rates') and source ('Frankfurter API, sourced from the European Central Bank'). It explicitly distinguishes from siblings by highlighting historical lookup and ECB sourcing, making the tool's unique role 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 when-to-use guidance ('Use currency_rates when a rate from a specific past date is required... or when the ECB source must be documented') and names specific alternatives with their appropriate contexts (currency_convert, currency_convert_lite, currency_fx_lite, currency_convert_open, crypto_fx_rates). This exceeds the typical level of usage direction.

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

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

With no annotations, the description carries the full burden. It discloses that calculations run 'entirely locally with no external API dependency' and details what each calculation type returns (e.g., compound_interest computes final value and total interest; loan_repayment calculates monthly payment, total repayable amount, and total interest). It lacks explicit return format or error behavior, but the output descriptions are substantive. Not contradicting any 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 comprehensive yet efficiently structured: one overview sentence, six detailed type sentences, and explicit usage guidance. No fluff or redundant statements. The length is justified by the tool's six calculation types. Everything 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 the tool's complexity (six calculation types, 11 parameters, no output schema, no annotations), the description is remarkably complete. It explains each type's computation and key outputs, provides usage context, and distinguishes from siblings. The only missing element is an explicit output structure, but the description already tells the agent what to expect for each calculation.

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

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

Schema description coverage is 100%, with each parameter already well-described (units, examples, which calculations use it). The description adds no new parameter-level meaning beyond the schema; it simply recaps the parameter-to-calculation mapping. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states the tool performs precise financial calculations across six defined types, listing each type and its specific outputs. It distinguishes from siblings by naming alternatives like financial_calculator_lite and market-data tools. The verb 'performs' and resource 'financial calculations' are specific and unambiguous.

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

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

Explicit guidance is provided: 'Use this tool when an agent needs to perform any structured financial calculation', with specific examples. It also states when to prefer financial_calculator_lite and explicitly says not to use this tool for live prices, directing to stock_quote, crypto_price, and currency_convert. This fully covers when/when-not and alternatives.

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

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

With no annotations provided, the description carries the burden. It discloses that calculations happen locally with no external API dependency, that it returns only a single numeric result, and that it does not fetch live market data. This provides useful behavioral context beyond the schema, though it omits edge-case behaviors or detailed return formatting.

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 about six sentences, each adding distinct value: what it does, supported types, return format, differentiation from sibling, usage guidance, and market data caveat. No filler or repetition; it is dense yet concise.

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

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

Given the tool has 6 parameters, multiple calculator types, no output schema, and no annotations, the description covers purpose, usage, alternatives, and key behavioral constraints. It could mention how to handle invalid calculator types or result precision, but the schema fills parameter needs, making it fairly complete for decision-making.

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

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

The schema already describes all 6 parameters with 100% coverage, including accepted calculator_type values and per-parameter required context. The description adds nothing about parameter syntax or units beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

The description clearly states it performs common financial calculations with a specific list of supported types, and explicitly distinguishes itself from financial_calculator as a lighter variant returning only a single numeric result. This makes it unique among siblings and gives a clear verb+resource+scope.

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

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

Explicitly states when to use lite vs full calculator: 'Use financial_calculator_lite when only the headline figure is needed. Prefer financial_calculator when the agent needs a full breakdown...' Also directs to stock_quote, crypto_price, or currency_convert for live market data, making alternatives clear.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the required prerequisite ('Requires a Finnhub API key to be configured on the server'), the real-time nature, and the specific return fields. It doesn't mention rate limits or error handling, but for a read-only quote tool, this is reasonably transparent. The description adds context about what data is returned (current price, intraday high/low, etc.) beyond what the schema covers.

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

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

The description is structured with a clear lead sentence, then return fields, usage scenarios, exclusions, and prerequisites. It's somewhat verbose (4-5 sentences) but every sentence adds useful information. It front-loads the primary purpose and then provides actionable guidance. No unnecessary 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 2 parameters and no output schema, the description is remarkably complete. It covers purpose, return data, use cases, alternative tools, exclusions, and a prerequisite. It gives the agent enough context to decide when to use this tool and what to expect from it.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the 'symbol' parameter with concrete examples (AAPL, MSFT, etc.) and clarifies the 'include_company_info' parameter's default behavior ('Defaults to true') and when to set it false. It also contrasts with stock_price_lite to help understand parameter usage.

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

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

The description clearly states the tool's function: 'Retrieves real-time stock price quotes and company information for any publicly traded company via the Finnhub API.' It specifies the verb (retrieves), resource (stock quotes and company information), and scope (any publicly traded company). It also distinguishes itself from sibling tools like stock_price_lite and crypto_price by outlining its unique value.

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 usage guidance: 'Use stock_quote when an agent needs to look up a stock price...' and 'Prefer stock_quote over stock_price_lite when the agent needs price change...' It also gives negative guidance: 'Do not use for cryptocurrency prices — use crypto_price...' and 'Do not use for fiat currency conversion — use currency_convert...' This clearly delineates when to use this tool versus 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?

With no annotations, the description fully discloses that the tool is read-only, requires a TrueLayer access token, and returns mock data if no token is configured. It also details the return content, offering strong behavioral transparency.

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

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

The description is compact yet informative, with each sentence serving a purpose: action, output, use cases, exclusions, and access token requirement. 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?

The description covers all necessary context: authentication via token, the read-only nature, the bank coverage, the exact output fields, transaction limits, and mock data fallback. Since no output schema exists, the description fully compensates by explaining return values.

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

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

Schema coverage is 100%, so schema already documents all parameters fully. The description adds minimal extra meaning (e.g., 'up to 100 transactions' aligns with the limit parameter), but does not meaningfully go 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 bank balances and transactions via PSD2 Open Banking, covering 300+ banks. It explicitly differentiates from siblings like stripe_payments, making its 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?

Provides explicit use cases ('Use this tool when...'), negative guidance ('Do not use for payment initiation', 'Do not use for Stripe-specific records'), and names the alternative tool (stripe_payments). This is exemplary 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?

With no annotations provided, the description carries the full burden. It discloses the search technique (MiniLM-L6-v2 embeddings), input format, output contents (ranked tools, schemas, pricing tier, call instructions), and result limit. It does not mention edge cases like no matches, but overall it is transparent about behavior.

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

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

The description is appropriately sized at four sentences, front-loaded with the core purpose, then usage guidance, alternatives, and output details. Every sentence adds value 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?

Despite no output schema, the description explains exactly what is returned (best matching tools, ranked, with schemas, pricing tier, and call instructions) and states result limit. It covers intent, usage, and output, making it complete for a tool-finder with 3 straightforward 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 description coverage is 100%, so baseline is 3. The description adds no parameter-level detail beyond the schema. The schema already explains intent, top_k, and model_hint with examples and defaults, so the description does not need to compensate.

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 uses a specific verb ('Discovers') with a clear resource ('most relevant tools available on this MCP server') and mechanism ('local semantic search'). It explicitly distinguishes itself from the domain-specific sibling tools by positioning itself as a meta-tool for discovering those 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?

Description provides explicit when-to-use guidance ('Use this tool first when you are connected to this server but do not know which specific tool to call') and when-not-to-use ('Do not use this tool if you already know the tool name — call that tool directly instead'). This fully clarifies 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 present, the description carries the burden and does well by stating it returns a single numeric value, omits certain metadata, and does not support crypto. It could mention error behavior or data delays, but for a simple lookup, this is sufficient disclosure.

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

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

The description is well-structured with three sentences: the core function, the comparison to stock_quote, and the crypto exclusion. Every sentence provides value without redundant fluff, making it concise and front-loaded.

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

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

Given the tool's simple nature (one required parameter, no output schema), the description is complete: it defines the return type, clarifies scope, and points to alternatives. No critical information is missing for an agent to select and invoke it correctly.

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

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

The input schema already fully documents the symbol parameter with examples and validation (100% coverage), so the description adds little beyond restating 'ticker symbol'. Baseline 3 is appropriate since the schema does the heavy lifting and the description does not conflict or add meaningful new detail.

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 the current trading price for a stock by ticker, and explicitly differentiates itself from stock_quote as a lightweight variant. It specifies the return value as a single numeric price, leaving no ambiguity about its function.

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 usage guidance: use when only raw price is needed, prefer stock_quote for additional fields, and use crypto_price/crypto_price_lite for cryptocurrencies. This directly addresses when and when not to use the tool versus 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?

With no annotations, the description carries the full burden. It discloses a prerequisite ('Requires a Stripe API key to be configured on the server') and clarifies the nature of the response ('returns a simple records array rather than the full structured Stripe response'). The verb 'retrieves' implies a safe read operation, and the lightweight variant distinction adds useful context beyond a generic retrieval 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 appropriately sized and well-structured: it opens with the core purpose, then provides usage guidance, sibling comparisons, and a prerequisite. Every sentence adds value, and the layout makes it easy to scan.

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 only two parameters, no annotations, and no output schema, the description covers all essential context: what it does, when to use it, how it differs from alternatives, data source, and API key requirement. It fully specifies the query types and introduces the lightweight nature of the response, making it complete for a simple list-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 description coverage is 100%, so the baseline is 3. Both 'limit' and 'query_type' are already fully described in the schema, including accepted values for query_type. The description reinforces the query_type meaning by mentioning review of charges, refunds, disputes, and subscriptions, but this is contextual rather than adding new parameter-specific semantics.

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

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

The description clearly states the tool retrieves payment and charge records from a Stripe merchant account, using the specific verb 'Retrieves' and identifying the resource. It also distinguishes itself from the sibling tool stripe_payments by noting this is a lightweight variant that returns a simple records array, making its 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?

Explicit usage guidance is provided: 'Use stripe_payment_records when an agent needs to review recent charges, refunds, disputes, or subscription payments from a Stripe account.' It also names alternatives: 'Prefer stripe_payments when the agent needs complete Stripe charge objects...' and 'Prefer open_banking_transactions or bank_accounts when the payment data source is a bank account.' This gives clear when-to-use and when-not-to-use instructions.

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

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

No annotations are provided, so the description must carry the transparency burden. It does disclose that the operation retrieves/returns data, describes the output structure (full Stripe charge objects with customer IDs, metadata, processing details), and notes the requirement for a Stripe secret key. However, it does not explicitly state that it is read-only or describe error/rate-limit behavior, so some behavioral disclosure is implicit rather than fully 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 front-loaded with the main purpose and each sentence earns its place. It covers the four query types, usage context, exclusions, and the authentication requirement. While longer than two sentences, the added length is justified by the tool's multi-mode complexity and the need to clearly warn against incorrect usage.

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 only two parameters and no output schema or annotations, the description provides thorough contextual coverage: it explains all query modes, the shape of returned data, applicable use cases, constraints (bank transactions, generic bank history), and the server-side key requirement. An agent would have sufficient information to select and invoke the tool correctly.

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

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

The schema already covers both parameters (query_type and limit) with descriptions, and schema coverage is 100%, so the baseline is 3. The description adds value by elaborating what each query_type returns (e.g., 'recent_payments returns the latest payment intents with status, amount, and currency'), which goes beyond the schema's simple enum listing.

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 a Stripe account via the Stripe API' and explicitly lists four query types. It also distinguishes the tool from the sibling 'stripe_payment_records' by noting that this one returns full Stripe objects versus a lighter-weight array.

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

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

The description gives explicit when-to-use guidance: 'use stripe_payments when an agent needs to investigate payment failures, audit recent transaction activity, retrieve customer billing records, or check subscription status.' It also provides clear when-not-to-use guidance and names alternative tools: 'Do not use for bank account transactions or PSD2 Open Banking data — use open_banking_transactions instead' and 'Do not use for generic bank account history — use bank_accounts instead.'

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 responsibility for behavioral disclosure. It states the tool accepts time, from_tz, to_tz, date and returns the converted local time, but does not discuss edge cases like DST transitions, invalid timezone names, or error behavior. This is adequate for a simple conversion tool but leaves some gaps.

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 overly long, with five similar examples of conversion scenarios that could be condensed to one or two. The initial statement and parameter/return summary are useful, but the repeated examples inflate the length without adding significant value.

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

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

The tool is simple, the schema fully documents parameters, and the description clearly states the return value. No output schema exists, so this is unnecessary. Context is sufficient for a user to understand when and how to use the tool, though it could mention handling of timezone edge cases.

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

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

The input schema already provides detailed descriptions for all four parameters (100% coverage), and the description merely lists the parameter names without adding new semantic information. Therefore the description adds no value beyond the schema, meriting the baseline score.

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 opens with a clear verb+resource ('Converts time between timezones') and provides concrete examples of conversion scenarios. It is immediately distinguishable from sibling tools, which are all financial/financial data tools.

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

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

The description explicitly states 'Use when you need to convert...' followed by a list of common scenarios (EST to Pacific, Tokyo from NY, etc.). While no alternatives are mentioned, there are no competing timezone tools among siblings, so this is sufficient 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?

With no annotations provided, the description carries the full transparency burden. It discloses key behavioral traits: real-time data, source restriction, sorted output, and no API key requirement. It stops short of covering error/rate-limit details but adds valuable context beyond the schema.

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

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

The description is concise yet comprehensive, opening with the core function, then detailing usage guidance and exclusions. Every sentence contributes to the description without redundancy, achieving strong front-loading.

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?

Even without an output schema, the description explains return fields, sorting order, and source constraints, complementing the rich input schema. It provides sufficient context for an agent to select and invoke the tool, though it omits minor details like empty-result behavior.

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

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

Schema description coverage is 100%, with each parameter (limit, topic, keyword) already thoroughly documented. The description adds no additional parameter-specific semantics beyond what the schema provides, so the baseline score of 3 applies.

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

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

The description clearly states the tool retrieves real-time news headlines and article summaries from BBC News and The Guardian, specifying the verb 'retrieves' and the resource (headlines and summaries). It also lists output fields and sources, distinguishing it from siblings like web_url_reader.

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 provides when-to-use scenarios (summarize headlines, research events, monitor topics) and when-not-to-use, directing users to web_url_reader for full article content and noting the source limitation to BBC and Guardian.

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

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

No annotations are provided, so the description carries full burden of behavioral disclosure. It discloses that the tool does not execute JavaScript, will not work with paywalled, login-protected, or JavaScript-rendered single-page applications, and describes the return format (title, meta description, word count, body text). This is comprehensive for a read-only public web fetcher.

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

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

The description is moderately long but every sentence contributes useful information—purpose, output, usage examples, exclusions, and limitations. It is front-loaded with the core function and progressively adds context, maintaining focus without clutter. Slightly verbose but efficient.

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

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

Given the tool's simple structure (3 parameters, no output schema), the description adequately explains the input, output, and limitations. It provides examples of appropriate URLs, mentions truncation behavior, and clarifies the non-JavaScript constraint, making it self-sufficient for an agent to use correctly. No major gaps remain.

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 100% of parameters with descriptions, so the description does not need to re-explain them. However, it adds valuable context beyond the schema: max_chars defaults to 8000 with a range up to 50000 and indicates whether content was truncated, and include_links defaults to false with a use case for crawling. This complements the schema effectively, though it doesn't fully replace the need for the schema's own 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 fetches any public web page and returns clean, readable plain text stripped of HTML, navigation, scripts, advertisements, and boilerplate. It specifies the output (page title, meta description, word count, main body text) and explicitly distinguishes itself from web_news_headlines, making its purpose and scope unambiguous.

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

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

Explicitly states when to use the tool (summarizing an article, extracting facts, verifying claims, converting a web page to plain text) and when not to (discovering current news headlines, which is web_news_headlines's job). It also gives a concrete example of passing article URLs from web_news_headlines, and notes limitations with JavaScript-rendered or paywalled pages, providing clear guidance for an agent.

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