Inferventis — Financial Data, News & Web MCP

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

Without annotations, the description discloses that the tool requires a valid bank API credential and uses a connected bank API integration. It does not detail error behavior or rate limits, but the read-only nature is implied by the retrieval purpose.

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

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

The description is concise, well-structured, and contains no extraneous information. It front-loads the primary purpose and efficiently integrates usage guidelines.

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 optional parameters and no output schema, the description covers purpose, usage with alternatives, and prerequisites. It could mention the output format (list of transactions) but that is evident from the description.

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 that omitting account_id returns transactions for all linked accounts and noting the default and maximum for limit, which goes beyond the schema descriptions.

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

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

The description clearly states the tool retrieves bank account details and transaction history, lists specific use cases (inspect balances, review spending, categorize transactions, reconcile records), and explicitly distinguishes itself from sibling tools (open_banking_transactions, stripe_payments).

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 versus when to prefer open_banking_transactions (PSD2 Open Banking) or stripe_payments (Stripe merchant accounts), including specific context about integration types.

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

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

No annotations are provided, so the description bears the full transparency burden. It discloses the data source (CoinAPI) and requirement ('Requires a CoinAPI key'), but does not explicitly state whether the operation is read-only or has side effects. While the context implies a safe query, the lack of explicit behavioral traits (e.g., 'does not modify any data') leaves some ambiguity.

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

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

The description is a single paragraph that front-loads the primary purpose and efficiently covers scope, usage guidelines, and alternatives. Every sentence adds value, though it could be slightly more terse. It is well-structured for quick scanning.

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, usage, and sibling differentiation, but omits details about the output format (e.g., whether it returns just the converted amount or also the rate). There is no mention of error handling or edge cases. Given the tool's moderate complexity, this gap prevents full completeness.

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

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

Schema coverage is 100%, so each parameter has a description. The tool description adds usage context (e.g., examples of pairs, case-insensitivity) but does not significantly enhance parameter meaning beyond what the schema already provides. 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 uses specific verbs ('Converts an amount') and clearly identifies the resource ('cryptocurrency and fiat currency pair'). It distinguishes itself from siblings by specifying when to use crypto_price, crypto_price_lite, currency_convert, and currency_fx_lite instead, making the tool's unique 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 this tool ('when the conversion involves at least one cryptocurrency and a specific amount must be converted') and provides clear alternatives for other scenarios (e.g., 'prefer crypto_price when only the spot price is needed'). This gives the agent actionable decision criteria.

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

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

No annotations are provided, so the description carries full burden. It discloses the data source (CoinGecko), the returned metrics (price, 24h change, market cap, volume), and supports 10,000+ coins. It also clarifies case-insensitivity and default currency. While it does not mention rate limits or authentication, the read-only nature is clear, and the description is sufficiently transparent for this use case.

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

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

The description is relatively long but front-loaded with the core purpose. Every sentence adds value, covering supported data, examples, alternatives, and exclusions. It could be slightly shorter, but it is well-organized and efficient.

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

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

Given the tool has 3 parameters and no output schema, the description is fairly complete. It explains what is returned, when to use it, when not to, and parameter behavior. It does not cover error handling or pagination, but those are not critical for this simple lookup. Overall, it provides sufficient context for an agent to use the tool correctly.

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

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

Input schema coverage is 100% with each parameter having a schema description. The description adds marginal value by listing supported coin examples, clarifying case-insensitivity, and explaining the effect of include_market_data. However, the schema already provides good descriptions, so baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves real-time price data for cryptocurrencies from CoinGecko, listing specific data fields. It distinguishes from siblings by explicitly naming crypto_price_lite and crypto_fx_rates, and notes exclusions like historical data and fiat-to-fiat conversion.

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 versus alternatives: 'Use crypto_price when an agent needs the full market picture... Prefer crypto_price_lite when only spot price... Use crypto_fx_rates when converting... Do not use for fiat-to-fiat... Do not use when historical data required.' This is thorough and leaves no ambiguity.

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 behavioral disclosure burden. It implies a read-only operation via CoinGecko public API and lists output fields, but does not mention rate limits, error handling, or side effects. Adequate but not thorough.

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 7 sentences, front-loaded with the core purpose. Each sentence adds distinct value: outputs, sibling differentiation, parameter details. No wasted words.

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

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

Given no output schema, the description mentions returned fields (price, percentage change, timestamp). It covers inputs, usage context, and sibling tools. However, it could be improved by specifying the response format (e.g., JSON keys) or error behavior, but is sufficient for a lightweight 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% with descriptions for both parameters. The description adds value by noting case-insensitivity, examples (BTC, ETH, bitcoin), default currency (USD), and ISO 4217 support. These details 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 the current spot price and 24-hour change, specifying the verb 'retrieves' and the resource. It distinguishes from sibling crypto_price (lightweight variant omitting extended data) and crypto_fx_rates (for conversions).

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

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

Explicitly says 'use it when only the raw price and 24h direction are needed' and provides clear alternatives: prefer crypto_price for richer data, use crypto_fx_rates for amount conversions. Also mentions default USD currency and supported 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 are provided, so the description carries full burden. It discloses the data source (ECB live rates), return format (converted amount, rate, timestamp), and currency support. It lacks details on error handling, rate limits, or authentication, but for a conversion tool these are less critical.

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 (5 sentences) but each sentence adds value: core function, return info, supported currencies, and usage distinctions. It is front-loaded with the primary action. Could be slightly more concise but achieves clarity.

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

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

Given no output schema, the description adequately explains return fields (converted amount, rate, timestamp). It also covers usage context and alternatives. The tool is complex with multiple siblings, and the description provides all necessary information for correct invocation.

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

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

Schema coverage is 100% with clear descriptions for each parameter (amount, from, to). The description reinforces the use of ISO 4217 codes and examples but does not add significant new semantic information beyond the schema. Baseline for high coverage is 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 clearly states the tool converts monetary amounts using live exchange rates from the Frankfurter API (ECB data). It specifies the output includes converted amount, rate, and timestamp, and lists supported currencies. It distinguishes itself from sibling tools by explicitly contrasting with lite versions and crypto converters.

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 versus siblings: use for full metadata, prefer lite versions for simpler needs, use currency_rates for historical rates, and avoid for crypto. This is comprehensive and clear.

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

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

With no annotations, description fairly discloses behavior: uses Frankfurter API/ECB data, returns converted amount and rate, minimal response without timestamp or source attribution, no crypto support. Could mention error handling or idempotency but is adequate for a straightforward conversion 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?

Description is detailed and well-structured, front-loading the main purpose and listing alternatives. Slightly verbose but each sentence adds value given many 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?

Completes the tool's purpose, output (converted amount and rate), data source, usage context, and alternatives. No output schema needed as description covers return values adequately.

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

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

Schema coverage is 100% with descriptions for amount (positive number), from/to (ISO 4217, case-insensitive). Description adds context about live rates and data source but does not significantly enhance parameter semantics 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?

Description clearly states 'Converts a monetary amount between any two fiat currencies using live exchange rates' and distinguishes from siblings like currency_convert, currency_fx_lite, currency_rates, and currency_convert_open. It also specifies unsupported cases (cryptocurrency).

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 and when-not-to-use guidance: 'Use currency_convert_lite when only the converted value and rate are needed... Prefer currency_convert when... Use currency_fx_lite for same minimal output... Use currency_rates when historical rate... Use currency_convert_open as fallback... Does not support cryptocurrency pairs.'

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 disclosure burden. It states the tool uses live rates, returns amount and rate, is for informational use, and does not support crypto. This is good, but it could mention potential issues like rate limits or error handling for invalid currencies.

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 front-loads the core functionality and then provides usage guidelines. Every sentence adds value, with no wasted words.

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

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

Given no output schema, the description clearly states what is returned (converted amount and rate). It adequately covers purpose, inputs, and usage context. Missing details on error handling or output format do not detract significantly for a simple conversion tool.

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

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

Schema coverage is 100%, so the description adds minimal value beyond the schema. It mentions that 'amount' is monetary and tags are case-insensitive, but these are implied. 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 converts monetary amounts between fiat currencies using live exchange rates from an open API, and returns the converted amount and rate. It distinguishes itself from siblings like currency_convert and currency_fx_lite by specifying the data 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 explicitly provides when to use this tool (as an alternative when other sources are unavailable) and when not to use it (prefer ECB-backed tools for compliance, and avoid crypto pairs). It names specific sibling tools for different use cases.

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

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

With no annotations, the description carries the full burden. It discloses that the tool uses 'live mid-market rates' and returns 'converted amount and exchange rate applied,' indicating read-only behavior. However, it does not explicitly state that it is non-destructive or provide details on rate source latency or data reliability, which would raise the score to 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 longer than ideal but every sentence serves a purpose: defining the operation, return value, usage guidance, and exclusions. It is front-loaded with the main purpose and well-structured. Could be slightly more concise, but not overly verbose.

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

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

Given no output schema, the description minimally explains the return ('converted amount and exchange rate applied'). With low complexity (3 simple parameters) and many sibling tools, this is sufficient. However, lacking details on output format or potential errors leaves room for improvement.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema, only noting that it 'Accepts all major ISO 4217 currency codes,' which is already implied by the schema examples. The schema itself already describes each parameter well, so the description adds little extra meaning.

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

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

The description clearly states the tool's purpose: 'Converts a foreign exchange (FX) amount between two fiat currencies using live mid-market rates.' It specifies the verb (converts), the resource (FX amount between fiat currencies), and the method (live mid-market rates). It also distinguishes itself from numerous sibling tools by naming them and their specific 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?

The description provides explicit guidance on when to use this tool (numeric result only, no specific data source needed) and when to prefer alternatives (e.g., currency_convert for richer metadata, currency_rates for historical data, crypto_fx_rates for crypto). It also states exclusions ('Does not support cryptocurrency pairs'). This is comprehensive and actionable.

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 must disclose behavior. It mentions data source (ECB), live/historical, and exclusion of crypto. However, it does not explain output format, pagination, or that historical querying requires a date parameter missing from 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?

Multi-sentence description is well-structured and front-loaded: first sentence defines purpose, second details usage context, third lists alternatives. No fluff or repetition.

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 is provided, yet description does not explain the response structure (e.g., rates object, timestamp). More critically, it promises historical lookup without a date parameter in input schema, making the tool definition incomplete for that advertised 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 covers all 3 parameters with descriptions (amount, from, to). Description adds context on case-insensitivity and ISO codes, but this is largely redundant with schema descriptions. Baseline score of 3 applies due to full schema coverage.

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 retrieval of live and historical fiat exchange rates from Frankfurter API. However, it claims historical support but the input schema does not include a date parameter, which may mislead agents into thinking they can query past dates.

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 specifies when to use this tool (auditable, ECB-sourced, specific past date) and names four alternatives for different contexts. The historical use case is emphasized but not supported by parameters, reducing clarity.

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

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

With no annotations, the description carries full burden. It states the tool is entirely local with no external dependency, which is important behavioral info. However, it does not detail error handling, output format, or potential precision issues, leaving 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?

Description is concise and well-structured, with a clear front-loaded purpose followed by bullet-like explanations of each calculation type. 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 the complexity (11 params, 6 calculation types) and no output schema, the description provides sufficient context: it explains what each calculation returns, which parameters are needed, and when to use the tool. The usage guidelines and sibling differentiation complete the picture.

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 explaining which calculation type uses which parameters, and clarifies unit conventions (e.g., periods in years vs months) that are not fully covered in the schema.

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

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

Clearly states that the tool performs precise financial calculations across six types locally, and distinguishes from sibling tools like financial_calculator_lite by specifying when to use each. The description includes specific verbs and resources for each calculation type.

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 the tool (structured financial calculations) and when not to (live market prices). Names alternative tools (stock_quote, crypto_price, currency_convert) for other tasks, and suggests financial_calculator_lite 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 provided, the description carries full burden. It discloses local computation, no external API dependency, and return of a single number. It could be more explicit about non-destructive nature, but the calculator context implies safety.

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 efficient paragraph. It is front-loaded with the core purpose and immediately distinguishes from siblings. Slightly verbose with listing all calculation types, but overall concise and well-structured.

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

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

Given 6 parameters, no output schema, and no annotations, the description covers purpose, parameter usage by calculation type, sibling differentiation, and external tool references. It does not explain the output format beyond 'single numeric result', but that is sufficient for a lightweight calculator.

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 3. The description adds value by specifying which parameters are required for each calculator_type, e.g., 'rate required for compound_interest, loan_repayment...' This goes beyond the schema's per-parameter descriptions.

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

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

The description clearly states the tool performs common financial calculations locally, lists specific calculation types (compound interest, loan repayment, etc.), and distinguishes itself from the sibling financial_calculator by noting it returns only a single numeric result instead of a full breakdown.

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

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

Explicitly tells when to use this tool (headline figure only) versus financial_calculator (full breakdown). Also advises using stock_quote, crypto_price, or currency_convert for live market data, providing clear alternatives.

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

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

Discloses requirement of a Finnhub API key and describes output fields. However, lacks details on error behavior (e.g., invalid symbol, missing API key) and potential rate limits, which would enhance transparency for an unannotated tool.

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

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

The description is well-structured, front-loading the purpose and logically progressing through returned data, usage guidance, and exclusions. It is slightly lengthy but every sentence contributes 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 (2 parameters, no output schema, no annotations), the description provides a comprehensive overview including output fields, usage context, and API key requirement. Missing error handling and rate limits, but sufficient for typical use.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description adds minimal new semantic information beyond the schema, such as listing symbol examples, but does not significantly extend 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?

The description clearly states it retrieves real-time stock quotes and company info via Finnhub API, lists returned data (price, intraday high/low, change, sector, exchange), and distinguishes from sibling tool stock_price_lite by detailing what extra information it provides.

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 (stock price lookup, intraday performance, sector data, portfolio monitoring), contrasts with stock_price_lite, and advises against use for crypto or fiat conversion, pointing to specific alternative tools.

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

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

With no annotations, the description carries full burden. It discloses it is strictly read-only and returns mock data if no token configured. However, it does not mention rate limits or behavior if no data is found, which would be helpful.

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 coherent paragraph of 5 sentences, front-loaded with main purpose, followed by details and usage guidance. No wasted words.

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

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

Given no output schema, the description explains return values (balance, currency, transactions with fields) and handles the edge case of missing token. The tool has 3 optional parameters, and the description covers all needed context.

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

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

Schema coverage is 100% with good parameter descriptions. The tool description adds extra context: ISO 8601 date format with an example, default limit, and behavior when account_id is omitted. This exceeds 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 clearly states it retrieves bank account balances and transaction history via PSD2 Open Banking, covering 300+ UK and European banks. It specifies the output: account balance, ISO 4217 currency code, and up to 100 recent transactions with details. This effectively distinguishes from sibling tools like stripe_payments and crypto tools.

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

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

Explicitly provides use cases (e.g., inspect spending history, verify payment cleared) and when-not-to-use (payment initiation, Stripe records), even naming the alternative tool (stripe_payments). This is exemplary 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 are provided, so the description carries full burden. It explains the behavior: local semantic search with MiniLM-L6-v2, returning up to 10 results ranked by relevance, along with schemas, pricing, and call instructions. It does not mention rate limits or latency, but the core behavior is well-described. A minor gap is the lack of detail on what 'exact call instructions' entails.

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 serves a purpose. It starts with the core function, then usage advice, and ends with output details. It is well-structured and front-loaded. Slightly verbose in the middle ('Returns up to 10 results...') but not overly so; still earns a 4 for efficiency.

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 (a meta-discovery tool) and the absence of an output schema, the description is comprehensive. It explains inputs, how it works, and what the output contains (tools, schemas, pricing, instructions). It does not detail the output format (e.g., is it JSON or text?), but the mention of 'ranked by relevance' and 'exact call instructions' provides sufficient context for an agent to understand what to expect.

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

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

Schema coverage is 100% (all three parameters described). The description adds significant value: for 'intent' it provides concrete examples, for 'top_k' it explains when to use higher values, and for 'model_hint' it clarifies an optional role. This goes beyond the schema's basic descriptions, enhancing semantic 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?

The description clearly states the tool's purpose: discovering relevant tools via semantic search on this MCP server. It specifies the embedding model (MiniLM-L6-v2), input (plain-English description), and output (ranked tools with schemas). It distinguishes itself from sibling tools by advising use when the agent does not know which tool to call, making the purpose unmistakable.

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 this tool first when you are connected to this server but do not know which specific tool to call' and 'Do not use this tool if you already know the tool name — call that tool directly instead.' It also advises on adjusting top_k for ambiguous cases, offering 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 provided, so description carries full burden. It discloses return type (single numeric value) and scope limitations. Could mention error handling or behavior on invalid symbols, but for a simple read tool it is sufficient.

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

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

Well-structured with core function first, then details. Slightly verbose but each sentence adds value. Could be more concise without losing meaning.

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

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

Given no output schema, description adequately explains return value. Covers usage, limitations, and comparisons. Complete for a simple single-parameter 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% with a good description. Description adds value by providing examples and requiring uppercase, which aids correct invocation.

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

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

The description uses specific verbs ('Retrieves the current trading price') and resource ('publicly listed stock by ticker symbol'). It clearly distinguishes from sibling tool 'stock_quote' by noting it's a lightweight variant omitting additional 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 ('only raw price needed') and when to prefer alternatives ('stock_quote'). Also clarifies unsupported use case (cryptocurrency) and directs 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?

No annotations provided, so description carries full burden. States return type (simple records array vs full structured response) and API key requirement. Lacks details on errors, exact response format, or mutability, but basic behavioral context is present.

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

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

Front-loaded with purpose; each sentence adds value. Slightly verbose in usage guidance section, but overall efficient.

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

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

Given no output schema, description explains return type contrast with stripe_payments. Adequate for a simple tool with 2 parameters, though record structure is not detailed.

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 only reiterates 'filtered by query type' without adding new meaning. 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?

Clear verb+resource: 'Retrieves payment and charge records'. Distinguishes from sibling stripe_payments (lightweight variant) and bank account tools (open_banking_transactions, bank_accounts).

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 (reviewing charges, refunds, disputes, subscriptions) and when not to (prefer stripe_payments for full objects, prefer bank account tools for bank data). Mentions Stripe API key prerequisite.

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 a Stripe secret key must be configured and that it returns full Stripe charge objects. However, it does not explicitly state that the tool is read-only (no destructive side effects) or mention rate limits, error handling, or authentication failure behavior. This leaves behavioral 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?

Description is comprehensive and well-structured, grouping related information. However, it is somewhat lengthy and includes some redundancy (e.g., repeating alternatives at the end). Slightly more concise could improve it.

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

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

Given no output schema, the description explains return value (full Stripe charge objects with customer IDs, metadata, processing details). It covers when to use vs. siblings. Complete enough for a data retrieval tool with clear parameters.

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

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

Schema coverage is 100% and already describes both parameters (limit and query_type) adequately. The description adds value by explaining what each query_type returns in more detail (e.g., 'recent_payments returns the latest payment intents with status, amount, and currency'), which helps the agent choose the correct query type.

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 live payment data from Stripe and enumerates four distinct query types (recent_payments, failed_charges, customers, subscriptions) with specific details about each. It also distinguishes from the sibling tool stripe_payment_records by noting the latter returns lighter-weight records.

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 (investigate payment failures, audit transactions, retrieve billing records, check subscriptions) and when not to use (bank account transactions, PSD2 Open Banking data) with specific alternative tools named (open_banking_transactions, bank_accounts). Also mentions the sibling tool stripe_payment_records as a lighter 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?

No annotations exist, so the description carries the burden. It states 'Returns the converted local time', implying a safe read operation, but lacks explicit mention of side effects, required permissions, or edge cases like DST handling. No contradictions with annotations (none 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 somewhat repetitive with multiple similar examples (e.g., multiple time conversion scenarios). It could be more concise without losing clarity. However, it is well-structured 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 absence of an output schema, the description mentions the return value ('converted local time'), but lacks details on error handling, timezone validation, or input format constraints beyond examples. It covers basics but not fully for a conversion tool.

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

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

Schema coverage is 100% so the schema already defines all parameters well. The description adds usage context via examples but does not provide additional semantic detail beyond what the schema offers.

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 'Converts time between timezones' with specific examples, distinguishing it from unrelated sibling tools like financial calculators or stock quotes.

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?

Multiple explicit usage examples are provided (e.g., converting 3:45 PM EST to Pacific Time, 9 AM NY to Tokyo, etc.), but no when-not-to-use or alternative tool guidance is given; however, siblings are in different domains making differentiation less critical.

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. Discloses sources, sorting order, and that no API key is required. Could mention rate limits or error handling (e.g., if sources are unavailable) 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 concise with no wasted words. Front-loaded with purpose, followed by usage guidance, then exclusions. Each sentence contributes meaningfully.

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 fully explains return structure. Covers all 3 optional parameters with clear defaults. For a simple news retrieval tool with no required params and no nested objects, the description is sufficient.

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

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

Schema coverage is 100% with detailed parameter descriptions. Description adds value beyond schema by explaining sorting ('most recent first') and default behavior for omitted 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?

Description clearly states verb (retrieves), resource (news headlines from BBC and The Guardian), and scope (across nine topic categories, sorted recent first). It distinguishes from sibling web_url_reader by specifying that this tool does not read full articles.

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, summarize, research, monitor, briefing) and when not to use (full article content, other sources). Names alternative tool web_url_reader and explains how to use it.

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

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

Discloses that it does not execute JavaScript, is best for standard HTML, and will not work with paywalled, login-protected, or JS SPAs. Mentions truncation indication for max_chars. Lacks error handling details, but annotations are absent so description carries full burden.

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

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

Every sentence earns its place. The description is well-structured: core function, return values, use cases, differentiation from sibling, and limitations. No redundancy or fluff.

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

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

Given no output schema, the description sufficiently explains what is returned (title, meta description, word count, body text). Covers when to use and limitations. Could mention error handling but not critical for this simple 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 max_chars defaults (8000) and maximum (50000), and include_links usage ('set to true when crawling'). Also provides URL examples.

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 fetches any public web page and returns clean plain text. It lists specific use cases (summarize, extract facts, verify claims) and distinguishes itself from sibling web_news_headlines by explicitly stating not to use for news headline discovery.

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 (reading specific web pages, summarizing, extracting facts) and when-not-to-use (not for discovering news headlines, not for paywalled/JS-rendered pages). Also guides to pass article URLs from web_news_headlines.

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