Inferventis MCP Server

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

No annotations provided, so description carries the burden. It discloses the credential requirement and default behavior (returns all accounts when no ID). However, it does not mention read-only nature, rate limits, or potential destruction, leaving gaps in transparency.

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

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

Five sentences, each adding value. No redundant information. Front-loaded with purpose, then usage, then alternative tools, then requirements. Highly efficient.

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

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

No output schema, so description must explain return values. It mentions 'list of transactions' and 'bank account details' but lacks specifics on fields like transaction IDs, dates, amounts. Adequate for a simple list tool but could be more 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?

Schema descriptions cover both parameters (limit, account_id) with clear defaults and behavior. The tool description adds context about omitting account_id returning all accounts, reinforcing 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 the tool retrieves bank account details and transaction history. It distinguishes from siblings by naming open_banking_transactions and stripe_payments as alternatives, specifying when to use each.

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 bank_accounts (to inspect balances, review spending, etc.) and when to prefer alternatives (open_banking_transactions for PSD2 Open Banking, stripe_payments for Stripe data). Provides clear context and exclusions.

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 the data source (CoinAPI), real-time nature, and supported pair types. It implicitly indicates quota consumption by comparing to alternatives. However, it does not detail error cases, rate limits, or failure behavior, but the key requirement is stated.

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 (4 sentences) and well-structured. It front-loads the core action, follows with supported conversions, then usage guidance, and ends with prerequisites. No unnecessary words.

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

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

Given moderate complexity and no output schema, the description covers tool purpose, usage guidelines, and parameter context well. It lacks return format details, but the tool's behavior is adequately described for selection and invocation. The sibling differentiators are thorough.

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

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

Schema coverage is 100% with clear parameter descriptions. The description adds overarching context (case-insensitivity, accepted code types) but primarily reiterates schema information. It does not add significant new meaning beyond what the schema already provides, 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 explicitly states it converts amounts between crypto/fiat or crypto/crypto using CoinAPI real-time rates. It names the supported conversion types and clearly differentiates from sibling tools like crypto_price and currency_convert, 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 when-to-use (conversion with at least one crypto, live CoinAPI needed) and when-not-to-use (both fiat -> prefer currency_convert or fx_convert_lite). It also mentions the prerequisite of a configured CoinAPI key, guiding the agent effectively.

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

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

With no annotations, the description carries full burden. It discloses the source (CoinGecko) and that data is real-time, but does not mention rate limits, authentication needs, caching behavior, or potential errors. This is adequate for a read-only data tool but lacks detail.

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 of four sentences, front-loaded with the main purpose. Each sentence adds information about inputs, outputs, supported coins, and usage context. It is concise without being vague, though slightly verbose in listing examples.

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 compensates by listing the returned fields. It covers inputs, outputs, supported coins, and use cases. Missing details like error handling or data freshness are minor; overall it is complete for a straightforward price retrieval tool.

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

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

Schema coverage is 100%, providing a baseline of 3. The description adds value by listing example coins, explaining case-insensitivity, and detailing the output fields (price, 24h change, market cap, volume), which are not in the schema. This helps agents understand parameter effects on results.

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

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

The description clearly states it retrieves real-time price data for cryptocurrencies from CoinGecko, specifying inputs (coin, currency, include_market_data) and outputs (price, change, market cap, volume). It lists examples but does not explicitly differentiate from the sibling tool 'crypto_price_basic', though the mention of market data implies a more comprehensive tool.

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 advises using the tool when an agent needs current price, valuations, or market condition reports. It provides clear context but does not explicitly exclude scenarios or mention alternatives like the basic version, leaving some 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?

With no annotations, the description carries full burden. It clearly communicates the tool is read-only, uses a public API, and returns specific data. It transparently states what it omits (extended market data) and the lightweight nature. However, it could mention potential rate limits or data freshness for full transparency.

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

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

Description is concise with no wasted words. It front-loads the main action and returns, then adds usage guidance and examples. Every sentence serves a purpose, making it 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?

Given the tool's simplicity (2 params, no output schema), the description fully covers purpose, parameter usage, sibling differentiation, supported assets, and response fields. It is complete and leaves no ambiguity about what the tool does and when to use it.

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

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

Schema coverage is 100% with adequate descriptions. The description adds value by clarifying that the coin parameter accepts both ticker symbols and full names, giving examples, and stating the default currency. This enhances understanding beyond the schema's base 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 explicitly states the tool retrieves current spot price and 24-hour change for any cryptocurrency, specifying the source (CoinGecko) and returned data (price, percentage change, timestamp). It clearly differentiates from the sibling crypto_price by calling itself a lightweight variant that omits extended market 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?

Provides explicit guidance on when to use this tool ('when only the raw price and direction are needed') and when to prefer the sibling crypto_price ('when the agent also needs market capitalisation, trading volume, or richer structured output'). It also lists supported coins and currency defaults.

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 live exchange rates are used and specifies the return values (converted amount, rate, timestamp). It does not mention rate limits, error handling, or data freshness, but for a simple conversion tool, the transparency is adequate.

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

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

The description is two sentences long, front-loaded with the primary action, and contains no extraneous information. Every sentence adds value.

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

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

Given the tool's simplicity and the absence of an output schema, the description covers the key inputs, action, and outputs. It omits potential edge cases (e.g., unsupported currencies) but is largely complete 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?

The input schema already covers 100% of parameters with descriptions. The description reinforces that currency codes must be ISO 4217 and amount must be positive. This adds no new meaning beyond the schema, earning the baseline score of 3.

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

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

The description clearly states the tool converts monetary amounts between currencies using live exchange rates. It specifies ISO 4217 currency codes and mentions the specific outputs. However, it does not explicitly differentiate from sibling tools like currency_convert_basic or fx_converter, which may have similar functionality.

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 a clear use case: 'when an agent needs to convert prices, invoices, payments, or financial figures'. However, it does not mention when not to use this tool or suggest alternative siblings for different scenarios (e.g., basic vs. live rates).

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

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

No annotations provided, so description carries full burden. Discloses use of Frankfurter API (ECB data) and live exchange rates. Returns converted amount and rate. Lacks details on error handling, rate limits, or currency validation, but overall transparent enough for a lite 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?

Four sentences, each purposeful. Front-loaded with core action and return values. Compares to sibling, then excludes crypto. 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?

With no output schema, description explains return values (converted amount and rate) but omits potential error behaviors or default handling. For a lite tool, it is reasonably complete given the context signals of low parameter count and no nesting.

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 context by listing examples of accepted ISO codes and reiterates positive amount requirement, but does not significantly enhance the schema's own descriptions.

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

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

Explicitly states it converts monetary amounts between fiat currencies using live rates. Distinguishes from sibling currency_convert by noting it's a lightweight variant with minimal response. Also clarifies it does not support cryptocurrency, pointing to alternatives.

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 clear guidance on when to use this tool vs the full currency_convert (when only converted value and rate are needed). Also advises on alternative tools for crypto conversions (crypto_price, crypto_fx_rates). 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 source (open public API), informational use case, and no cryptocurrency support. With no annotations, description carries full burden; lacks details on rate limits or caching but is otherwise transparent.

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

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

Concise, front-loaded with purpose, then usage guidance and limitations. Every sentence adds value; no redundancy or filler.

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

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

With no output schema, description covers return value (converted amount and rate applied). Missing details on error handling or edge cases, but adequate for a conversion tool of this complexity.

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

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

Schema coverage is 100% with clear descriptions (ISO codes, case-insensitivity, positive number). Description adds no extra semantics beyond what the schema provides, meeting baseline expectation.

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?

Explicitly states 'converts a monetary amount between two fiat currencies using live exchange rates', specifies return values, and distinguishes from siblings like currency_convert (auditable rates) and currency_fx_lite (same source, lightweight).

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 clear when-to-use (fallback for unavailable/rate-limited converters), when-not-to-use (cryptocurrency not supported), and explicit alternatives (prefer currency_convert for compliance, currency_fx_lite for lightweight).

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 describes live mid-market rates and crypto exclusion but doesn't disclose whether operation is read-only or any side effects (though implied). Adequately transparent for a 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?

Four sentences, no redundancy. Front-loaded with main action and output. 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?

Despite no output schema, description covers return values (converted amount and rate). Includes limitations (crypto, sibling comparisons). Complete for its simplicity.

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

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

Schema coverage is 100% with descriptions. Description adds 'case-insensitive' for currency codes and 'positive number' for amount, which are minor but valuable extras 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?

Clearly states it converts FX between fiat currencies using live mid-market rates. Distinguishes from sibling tools like currency_convert and crypto_fx_rates by specifying lightweight ad-hoc use.

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 (lightweight, ad-hoc) and when-to-use-alternatives (currency_convert for richer metadata, crypto_fx_rates for crypto pairs). Names specific sibling 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?

Despite no annotations, the description fully discloses the data source (ECB), capabilities (live and historical), and limitations (no crypto). It sets appropriate context for a read-only retrieval 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 front-loaded with key purpose and source. It is somewhat lengthy due to multiple sibling references, but each sentence adds value. Efficient use of space.

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?

Comprehensive coverage of use cases, source, and alternatives. However, the description implies a historical date parameter that does not exist in the schema, creating confusion. No output schema, but description does not explain response format.

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 case-insensitivity and positive number hints, but also claims historical rate lookup without a date parameter in the schema, which could mislead agents. No improvement over schema.

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

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

Clear verb 'retrieves' with specific resource 'live and historical fiat currency exchange rates from Frankfurter API'. Distinguishes from siblings like currency_convert (for live only) and crypto_fx_rates.

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

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

Explicitly states when to use this tool (historical rates, ECB source) and when to use alternatives (currency_convert, currency_convert_lite, crypto_fx_rates). Provides clear criteria 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?

With no annotations provided, the description carries the full burden. It discloses that all calculations are performed locally with no external API dependency, which is important behavioral information. It also outlines what each calculation type returns (e.g., 'monthly payment, total repayable amount, total interest'). No contradictory or missing safety information.

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 of about seven sentences covering all six types. It is front-loaded with the overall purpose and then expands each type. While efficient, a more structured format (e.g., bullet points) could improve readability for an agent, but it remains concise and informative.

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 11 parameters, no output schema, and no annotations, the description covers inputs and purpose well but lacks explicit details about the output format (e.g., JSON structure, data types returned). The implied returns (e.g., 'final value and total interest') are somewhat vague. For a tool with many calculation types, a brief note on common output structure would improve 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 description coverage is 100%, so baseline 3. The description adds significant value by grouping parameters by calculation type (e.g., 'compound_interest uses principal, rate, periods'), helping the agent understand which parameters are relevant for each calculator_type. This is beyond what the schema provides.

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

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

The description explicitly states the tool performs precise financial calculations across six calculation types, and for each type it specifies what it computes (e.g., 'compound_interest computes the final value and total interest earned'). This clearly differentiates from siblings like financial_calculator_basic, implying this is the full-featured version.

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 lists the six calculation types and their purposes, which implicitly tells the user when to use each type. However, it does not provide explicit guidance on when to use this tool versus the sibling 'financial_calculator_basic' or other financial tools, missing an opportunity to set usage boundaries.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It clearly states the tool is local (no external API dependency), returns a single numeric result, and lists all supported calculation types. It also explicitly says it does not fetch live market data. No contradictions exist.

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 approximately 4 sentences, front-loaded with the core purpose, followed by supported types, result format, and explicit sibling comparison. Every sentence adds value; there is 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?

The description covers the tool's functionality, supported calculations, and differentiation from siblings. However, it does not specify the exact output format (e.g., a JSON object with a 'result' field, or a plain number). Given that there is no output schema, a brief note on the output structure would improve completeness. Nonetheless, for a lightweight tool, the description is mostly 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?

The input schema has 100% description coverage for all 6 parameters, so the description adds minimal additional meaning. It does clarify that the result is a single numeric value, which differentiates from the sibling, but the schema already explains parameter requirements and types adequately. 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's purpose: performing common financial calculations locally. It lists supported calculation types (compound interest, loan repayment, ROI, etc.) and explicitly distinguishes itself from the sibling tool financial_calculator by stating it returns only a single numeric result, not 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?

The description provides explicit guidance on when to use this tool vs. financial_calculator: 'when only the headline figure is needed' vs. 'when the agent needs a full breakdown'. It also warns that neither tool fetches live market data and directs to other tools (stock_quote, crypto_price, currency_convert) for live prices.

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 must handle behavioral disclosure. It states the tool 'retrieves' and 'returns' data, implying a read-only operation. However, it does not discuss authentication, rate limits, error handling, or latency, leaving some behavioral aspects undisclosed.

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-organized paragraph of four sentences. It efficiently conveys purpose, context, output details, and usage guidance without redundant or extraneous information.

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

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

Given the absence of an output schema and annotations, the description covers the essential aspects: purpose, data sources, return contents (categorised transactions, balance), and use cases. It could be improved by mentioning date format validation or error scenarios, but remains largely complete for the tool's simplicity.

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

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

The input schema has 100% coverage, describing three parameters (limit, from_date, account_id) with clear descriptions. The description adds minimal extra value, echoing that account_id is optional and defaults to the first account, which is already in the schema.

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

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

The description clearly states the tool retrieves real-time bank account transaction history and balance data via Open Banking APIs, specifying the scope (300+ UK and European banks). It uses a strong verb+resource combination and effectively differentiates from sibling tools focused on stocks 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?

The description explicitly lists use cases (analysing spending patterns, verifying balances, assessing affordability) and advises using the tool when such live bank data is needed. However, it does not mention when to avoid using it or provide alternatives, missing a chance to differentiate from the sibling 'bank_data' 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, the description carries full burden. It discloses the use of MiniLM-L6-v2 embeddings for local semantic search, explains the output (best matching tools with input schemas, pricing tier, call instructions), and mentions the result limit (up to 10). It does not cover limitations or potential errors, but the behavior is well-described for a discovery 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 a single paragraph that efficiently covers key aspects. It is front-loaded with the purpose and usage guidance. While it could be slightly more concise, it is well-structured and every sentence adds value. 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 the absence of an output schema, the description reasonably covers what is returned (ranked tools with schemas, pricing, call instructions). It addresses the main context needed for a discovery tool. For a server with many siblings, it orients the agent well. However, it could mention that results are based on semantic similarity and might not be perfect.

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 beyond the schema by providing usage advice for each parameter: for 'intent' it gives examples and advises specificity; for 'top_k' it explains the range and suggests when to use higher values; for 'model_hint' it clarifies it is optional and explains its purpose. This additional context justifies a 4.

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 discovers relevant tools on the MCP server for a given task using semantic search. It distinguishes itself from sibling tools by specifying it is for when the agent does not know which tool to call, and it returns ranked results with schemas and instructions.

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

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

Explicit guidance is provided: 'Use this tool 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.' This clearly defines when to use and when not to use.

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 accurately describes the tool's behavior as a lightweight price fetcher, noting what it omits and that it doesn't support crypto. However, it does not mention return format details or error handling, which would be beneficial.

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

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

Concise yet informative, with front-loaded purpose followed by clear usage guidance. Every sentence serves a purpose without redundant text.

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 one parameter and no output schema, the description covers purpose, usage guidelines, and limitations. It could be slightly improved by noting the return format (e.g., 'returns a number'), but is largely complete.

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

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

Schema coverage is 100%, baseline 3. The description adds value by explaining symbol examples and uppercase requirement, going beyond the schema's description.

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, returns a single numeric value, and distinguishes itself from sibling tools stock_quote and crypto_price with specific details about what it omits.

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 (for quick price lookup) and when to prefer alternatives (stock_quote for comprehensive data, crypto_price for crypto), providing clear 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 provided, so description carries full burden. It discloses API requirement (Finnhub), required API key, and return fields (price, intraday high/low, etc.). Could mention rate limits or error handling, but sufficiently 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?

Well-structured with clear sentences, but slightly verbose (e.g., listing examples in schema already). Front-loads core purpose and 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?

Given low complexity (2 params, no output schema), description is complete: covers return data, optional behavior, and usage boundaries. No gaps.

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?

Adds meaning beyond schema: explains effect of 'include_company_info' ('Set to false for a faster, price-only response') and describes return data. Schema coverage is 100%, but description enriches 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?

Clear verb+resource: 'Retrieves real-time stock price quotes and company information'. Distinguishes from sibling 'stock_price_lite' by explicitly stating it returns more 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 ('when an agent needs to look up a stock price...') and when not to use (cryptocurrency, fiat currency). Names alternative tools: crypto_price, currency_convert.

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 cover behavioral aspects. It explains filtering by query type, the return format (records array), and its lightweight nature. However, it does not mention read-only/idempotent behavior, error handling, or rate limits. The description is adequate but lacks depth for a tool with zero 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 somewhat lengthy but front-loaded with the core purpose, then usage guidance, then comparisons and requirements. Each sentence serves a purpose, though some redundancy with the schema could be trimmed.

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, and the description only vaguely mentions returning a 'records array' without detailing the fields. For a retrieval tool, the agent would benefit from knowing what is in each record. The description covers usage well but lacks completeness in describing return structure.

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 descriptions for both parameters. The description adds context for `query_type` values and `limit` defaults/max, but these are also in the schema. Since schema coverage is high, the description adds minimal extra value, baseline 3 is appropriate.

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

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

The description clearly states it retrieves payment and charge records from a Stripe account, filtered by query type. It distinguishes itself from the sibling tool `stripe_payments` by noting it returns a simple array rather than full structured 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?

Explicit guidance on when to use this tool vs `stripe_payments` (lightweight vs full) and when to prefer alternatives like `open_banking_transactions` or `bank_accounts`. Also mentions the requirement of a configured Stripe API key.

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

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

No annotations are provided, but the description explicitly states 'retrieves' and 'return real-time data', indicating a read-only operation with no side effects. 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 three well-structured sentences with no fluff: first defines scope, second explains connectivity, third lists use cases. Every sentence earns its place.

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

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

With no output schema, the description vaguely describes return content ('real-time payment activity, failure reasons, customer details'), but lacks specifics on structure, pagination, or fields. Sufficient but not 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 detailed property descriptions. The tool description lists query types already covered in the schema, adding no new parameter-specific meaning beyond what the schema provides.

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

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

The description clearly states the tool retrieves live payment data from Stripe, enumerates specific data types (payment intents, failed charges, etc.), and distinguishes it from generic siblings like 'payment_tool' by specifying the Stripe source.

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

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

The description provides explicit use cases (investigate failures, audit transactions, etc.) but does not mention when not to use or name specific alternatives. Still, guidance is clear enough for an agent.

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 no API key is required and results are sorted most recent first. However, it lacks details on rate limits or potential delays, but is otherwise transparent.

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

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

Well-structured and concise, front-loaded with purpose, then details, usage guidance, and exclusions. 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 no output schema, the description explains return fields fully. It covers sources, topics, sorting, and optional parameters, making it complete for the tool's scope.

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

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

Schema coverage is 100%, and the description adds value with examples for keyword and explanations for limit (e.g., lower for quick briefing, higher for comprehensive coverage), going beyond what the schema provides.

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

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

The description clearly states the tool retrieves real-time news headlines and article summaries from BBC News and The Guardian, specifying the return fields and sorting. It distinguishes itself from sibling tools like web_url_reader.

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

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

Explicitly tells when to use (e.g., need current news, research recent events) and when not to use (not for full article content, not for non-BBC/Guardian sources). Provides an alternative: web_url_reader for full articles.

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

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

With no annotations, the description fully discloses behavioral traits: does not execute JavaScript, works with standard HTML, not for paywalled or JS-rendered pages. Also mentions stripping of navigation and advertisements.

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

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

Description is a single coherent paragraph, contains all necessary information without excessive verbosity. Could be slightly more structured but remains concise and readable.

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

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

Covers purpose, usage, limitations, output details (title, meta, word count, body). No output schema provided, but description explains 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%, so baseline 3. The description adds minimal extra meaning beyond schema descriptions (e.g., default max_chars, purpose of include_links). No contradictions or gaps.

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 public web pages and returns clean plain text, stripping HTML and boilerplate. It explicitly differentiates from siblings by stating not to use for headlines (use web_news_headlines).

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

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

Provides explicit use cases (summarize article, extract facts, verify claim) and when not to use (discover headlines). Names alternative sibling tool (web_news_headlines).

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