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 provided, so description must carry behavioral info. It reveals dependency on CoinAPI and requirement for an API key. Implies quota consumption by contrasting with fiat tools. Could mention response format or error handling, but the safety profile (read-only) is clear enough.

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

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

Three sentences: first states core function, second lists supported pairs, third gives usage guidance and alternatives. No fluff, front-loaded with key information.

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

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

Given no output schema or annotations, the description covers purpose, usage, prerequisites, and alternatives. Lacks details on error conditions or output format but is sufficient for the agent to decide to invoke. Acceptable completeness for the complexity level.

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 does not add significant parameter-level detail beyond what the schema provides. It briefly mentions allowed code types but no new 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 verb (converts), resource (amount between currency pairs), and source (CoinAPI). It distinguishes from siblings like crypto_price and currency_convert by specifying the scope (at least one 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?

Explicitly provides when-to-use: when conversion involves crypto and live CoinAPI rates are needed. Offers alternatives: crypto_price for spot prices without conversion amount, currency_convert for fiat-only conversions. Also notes that fiat-only tools do not consume 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 full burden. It states the data source (CoinGecko) and return fields, but does not disclose potential rate limits, API dependencies, or latency considerations. The description is adequate but could be more transparent about operational constraints.

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

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

The description is a single well-structured paragraph with clear sections: purpose, return data, examples, and usage scenarios. Every sentence contributes meaning 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 3 parameters, no output schema, and no annotations, the description covers purpose, parameters, return values, and use cases comprehensively. It lacks only minor details like error handling or explicit read-only indication.

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 some value with examples (coin names, currency codes) and clarifies defaults (USD for currency, true for include_market_data). The improvement over the schema is marginal, meeting the baseline for high 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?

The description clearly specifies the tool's function: retrieving real-time price data for cryptocurrencies from CoinGecko. It lists the exact data returned (price, 24h change, market cap, volume) and provides a comprehensive list of supported coins, distinguishing it from siblings like crypto_price_basic.

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 use cases such as getting current price, valuing holdings, monitoring portfolios, and reporting on market conditions. However, it lacks guidance on when not to use the tool or explicit comparison to alternatives like crypto_price_basic or stock_tool.

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

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

With no annotations provided, the description carries full burden. It discloses using the CoinGecko public API and the return fields (price, percentage change, timestamp). However, it does not mention rate limits, potential errors, or any authentication requirements, leaving some uncertainty for the agent.

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 five sentences, front-loading the core function and then providing distinctions, supported assets, and parameter details. Every sentence adds value with no redundancy or irrelevant information.

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

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

Given no output schema, the description adequately explains the response includes price, 24-hour change, and timestamp. It covers the essential return structure for a lightweight price tool but could have included an example or note on data format for 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% with both parameters described. The description adds context by explaining that 'coin' accepts ticker or full name (case-insensitive) and 'currency' defaults to USD with ISO 4217 codes. While helpful, this largely reinforces schema details rather than adding significant new meaning, warranting a 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?

The description clearly states it retrieves current spot price and 24-hour change for cryptocurrencies. It distinguishes itself from the sibling tool 'crypto_price' by noting it omits extended market data and has a simpler response schema, making its specific use case very clear.

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 this tool versus 'crypto_price': use this when only raw price and direction are needed, and prefer the sibling for market cap, volume, or richer output. Also lists supported assets and acceptable input formats, providing comprehensive 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. It discloses live exchange rates and return fields, but lacks details on limitations (e.g., unsupported currencies, precision, rate update frequency). This is adequate but not comprehensive.

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

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

The description is two sentences, efficiently stating the core function and usage context with no redundant information. Front-loaded 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?

Despite no output schema, the description adequately explains return values (converted amount, rate, timestamp). For a simple conversion tool, this covers what an agent needs to know and is complete.

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

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

Schema coverage is 100% with good descriptions for each parameter. The description only adds that any ISO 4217 code pair is accepted, which reinforces but does not significantly extend the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool converts monetary amounts between currencies using live rates, accepts ISO 4217 codes, and specifies the return values. This distinguishes it from siblings like currency_convert_basic which may not use live 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?

The description explicitly states when to use the tool (converting prices, invoices, etc., in real time). However, it does not mention when not to use it or provide explicit alternatives among the many sibling tools (e.g., fx_converter, real_coinapi_fx).

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 fully covers behavioral traits: it names the data source (Frankfurter API/ECB), specifies what is returned (converted amount and rate), and what is omitted (timestamp, metadata). No contradictions.

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

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

The description is well-structured with the core action first, but it is slightly verbose with multiple examples and comparisons. Most sentences add unique value, though some could be condensed.

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 lack of an output schema, the description adequately explains what the tool returns (converted amount and rate) and what it does not (timestamp, metadata). It also covers usage context, alternatives, and exclusions, making it complete for an agent.

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

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

The input schema has 100% coverage with descriptions for all three parameters. The description adds marginal value by listing example currency codes and stating case-insensitivity, but does not significantly enhance 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 clearly states the tool converts monetary amounts between fiat currencies using live exchange rates, and distinguishes it from the sibling 'currency_convert' as a lightweight variant. It also specifies it does not support crypto pairs, directing agents to 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?

It explicitly recommends using this variant when only the converted value and rate are needed and a smaller payload is preferred, and directs to 'currency_convert' when richer output is required. It also warns against using for cryptocurrency, providing alternatives.

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

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

No annotations are provided, so the description must carry the full behavioral burden. It discloses the source (open public feed), return values (converted amount and rate), and constraints (no crypto, informational use). However, it does not explicitly state this is a read-only operation with no side effects, which would be expected given the lack of annotations.

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

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

The description is concise with no filler sentences. It front-loads the core purpose, then immediately provides usage guidelines and exclusions. Every sentence adds value.

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

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

For a 3-param conversion tool with no output schema, the description covers purpose, usage guidelines, behavioral constraints, and exclusions. It is fully adequate for correct agent selection and invocation.

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

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

Schema coverage is 100% with adequate property descriptions. The description adds context about live rates and the open source, but does not provide additional semantic detail beyond what the schema already offers. Baseline 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', 'returns') and identifies the resource ('monetary amount between two fiat currencies using live exchange rates'). It clearly distinguishes from siblings by naming currency_convert and currency_fx_lite, and specifying different use cases.

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

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

Explicitly states when to use this tool (alternative when others are unavailable/rate-limited) and when to prefer siblings (currency_convert for compliance, currency_fx_lite for lightweight, crypto_fx_rates for crypto). 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?

Discloses use of live mid-market rates, returns converted amount and rate, and excludes crypto. Lacks details on rate freshness or limits, but adequate with no annotations.

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

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

Three sentences, front-loaded with purpose, then usage guidelines. 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?

Describes output and constraints, but lacks explicit output schema structure. However, the description is adequate for a lightweight conversion tool with simple return.

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 parameters with descriptions. Description adds that currency codes are ISO 4217 and case-insensitive, but schema already conveys meaning with 100% 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 the tool converts FX amounts between fiat currencies using live mid-market rates. Distinguishes from siblings by naming alternatives for richer metadata or crypto.

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

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

Explicitly says when to use this vs currency_convert, currency_convert_lite, and crypto_fx_rates. Provides clear context for selection.

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

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

No annotations provided, but description discloses data source and capabilities (live and historical). As a read-only tool, it is adequately transparent. Does not mention rate limits or authentication, but that is not required for transparency trust.

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?

Single paragraph of about 100 words, front-loaded with main purpose. Efficiently includes sibling differentiation. However, the misleading historical claim detracts from structural integrity.

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 present, and description does not describe return structure. Missing date parameter for historical lookup is a significant gap. Completeness is adequate for a simple read tool but not 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?

Input schema covers 100% of actual parameters with descriptions. However, description claims 'historical rate lookup for any past date' but schema has no date parameter, creating misleading expectation. This contradiction reduces clarity.

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 'Retrieves live and historical fiat currency exchange rates' with specific source (ECB via Frankfurter API). Distinguishes from siblings by naming currency_convert, currency_convert_lite, currency_fx_lite, and crypto_fx_rates for different use cases.

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

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

Explicitly states when to use (auditable, historical, ECB source) and when to prefer alternatives (live rich response, lightweight, crypto). Also states 'Does not support cryptocurrency pairs' with alternative tool.

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

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

No annotations exist, so the description must disclose behavioral traits. It states calculations are 'local with no external API dependency' but fails to mention auth needs, rate limits, or potential side effects. As a calculator, safety is assumed, but transparency is thin.

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, starting with the general purpose, then detailing each calculation type in a clear, front-loaded paragraph. Every sentence provides unique information without repetition 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 the lack of an output schema and annotations, the description should fully explain return values and constraints. It names the outputs for each calculation type (e.g., 'final value and total interest') but does not specify the exact JSON keys or structure, which may confuse an agent parsing the response.

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

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

The input schema has 100% description coverage with detailed parameter meanings (e.g., 'rate: Annual interest or discount rate...'). The tool description adds no additional parameter insight beyond listing which calculation uses which parameters, which is already in the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool performs six specific financial calculations, listing each with a precise verb and resource (e.g., 'compound_interest computes the final value and total interest'). It distinguishes itself from sibling 'financial_calculator_basic' by implying a more comprehensive offering.

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

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

No explicit guidance is provided on when to use this tool versus alternatives like 'financial_calculator_basic' or when not to use it. The description only implies usage for financial calculations, lacking exclusion criteria or alternative references.

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 real-time nature, PSD2 compliance, and that it returns categorized transactions, merchant names, amounts, and current balance. No annotations exist, so description carries full burden. Does not mention rate limits or authentication, but provides key behavioral traits.

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

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

Three sentences, front-loaded with action. First sentence states purpose, second adds scope, third lists use cases and outputs. 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?

No output schema, but description explains return includes categorized transactions, amounts, balance. Parameter descriptions are complete. Lack of error handling or pagination info is minor for this complexity level.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value: default and max for limit, ISO format for from_date, and behavior when account_id omitted (returns first available account). This goes beyond schema descriptions.

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

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

Clearly states it retrieves real-time bank account transaction history and balance data via Open Banking APIs. Specifies the resource (bank transactions) and action (retrieves) with scope (300+ UK/European banks). Though it does not explicitly distinguish from siblings like stock_tool or crypto_price, the domain is distinct enough.

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?

Lists specific use cases: analyze spending patterns, verify account balances, assess affordability, retrieve financial history. Explicitly says when to use. Does not mention when not to use or alternatives, but given sibling tools are in different domains, this is acceptable.

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, but description details the limited output (single numeric value) and what is omitted. Does not specify if data is real-time or delayed, but overall transparent about its lightweight nature.

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

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

Well-structured: purpose first, then usage differentiation, then crypto exclusion. Could be slightly shorter, but no fluff.

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

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

Given low complexity (one param, simple output), description is adequate. Explains return value and usage boundaries (no crypto). No output schema, so description suffices.

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?

Only one parameter (symbol) with schema coverage 100%. Description adds value by providing examples, uppercase requirement, and valid ticker examples, though schema already clearly defines it.

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

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

Clearly states it retrieves current trading price for a stock by ticker symbol. Distinguishes itself from sibling tools stock_quote and crypto_price, and explicitly lists what it omits compared to stock_quote.

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

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

Explicitly instructs when to use this tool (only need raw price) versus stock_quote (need more data) and crypto_price (for crypto). Provides 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?

No annotations, so description bears full burden. It mentions real-time nature, intraday data, and dependency on a configured Finnhub API key. However, it does not address rate limits, data freshness guarantees, or error handling for invalid symbols, which are relevant behavioral aspects.

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: first sentence states core purpose and return values, following sentences give usage guidance and exclusions. Each sentence earns its place; no redundant info. Appropriate length for a tool with moderate complexity and multiple 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?

No output schema, but description lists all major return fields (current price, intraday high/low, percent change, previous close, sector, exchange). Covers input details, usage context, and exclusions. Adequately complete for a stock quote tool with a straightforward purpose.

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?

100% schema coverage, but description adds value beyond schema: provides stock symbol examples, explains default for include_company_info (true), and gives a performance hint (set to false for faster response). This goes beyond the schema's minimal 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?

Description starts with a clear verb ('Retrieves real-time stock price quotes and company information'), specifies the resource (publicly traded companies via Finnhub API), and lists return fields. It distinguishes from sibling tool stock_price_lite and other domain tools for crypto and currency.

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

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

Explicitly states when to use ('when an agent needs to look up a stock price...') and when not to use (cryptocurrency, fiat conversion), providing alternative tool names. Also distinguishes from stock_price_lite based on needs for additional data.

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

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

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?

With no annotations, the description bears full burden. It indicates read-only behavior ('retrieves', 'returns') but does not disclose authentication needs, rate limits, or potential side effects. The description is adequate but lacks depth on operational constraints.

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

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

The description is two concise sentences, front-loading the main purpose. Every sentence adds value without redundancy. The structure is efficient and easy to parse.

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 2 parameters, no output schema, and no annotations. The description covers main use cases and data types (failure reasons, customer details, billing info). It could hint at response format, but the listed scenarios provide sufficient context for an agent to decide 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?

Input schema coverage is 100% with clear descriptions for limit and query_type. The description summarizes query_type options but adds no additional syntax or format details beyond the schema. Baseline 3 is appropriate; the description provides marginal extra value.

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 Stripe payment data, enumerating specific data types (payment intents, failed charges, customers, subscriptions) and use cases. It uses specific verbs like 'retrieves', 'investigate', 'audit', distinguishing it from sibling tools like bank_data or crypto_price which cover other domains.

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 lists when to use the tool: 'investigate payment failures... audit recent transactions...' providing clear context. It does not mention when not to use it or alternative tools, but the scenarios are specific enough to guide selection.

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

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

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.