Inferventis MCP Server

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

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

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

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

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

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

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

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

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

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

Input schema has 100% coverage with clear descriptions. Description adds value by explaining the effect of omitting account_id (returns all accounts), going beyond the schema.

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

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

Description clearly states it retrieves bank account details and transaction history. It explicitly distinguishes from sibling tools open_banking_transactions and stripe_payments by highlighting different use cases.

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

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

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

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

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

No annotations exist, so description carries full burden. It discloses real-time rates from CoinAPI, the need for a CoinAPI key, and contrasts data sources with sibling tools. However, it omits error behavior (e.g., if key is missing or rate unavailable) and rate limits.

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 purpose statement, supported pairs, usage guidelines, and alternatives. It is slightly verbose but every sentence adds value. Front-loaded with main function.

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 could clarify the return format (e.g., includes converted amount, rate, timestamp). However, it covers prerequisites, data source, and sibling differentiation, making it largely complete for a conversion tool with three required parameters.

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

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

Schema coverage is 100% with adequate descriptions for all three parameters. The description adds minor value by re-emphasizing that amount is denominated in the 'from' currency and noting case-insensitivity, but does not significantly expand beyond the schema.

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

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

The description specifies the verb 'converts', the resource 'amount between cryptocurrency and fiat', and the real-time source 'CoinAPI'. It explicitly distinguishes from siblings like crypto_price, crypto_price_lite, currency_convert, and currency_fx_lite, 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?

Provides clear when-to-use: 'when conversion involves at least one cryptocurrency and a specific amount must be converted.' Explicitly calls out alternatives: prefer crypto_price for spot price without amount, prefer currency_convert for fiat-only. Also mentions CoinAPI quota implications.

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?

Annotations are absent, so the description carries the burden. It discloses the data source (CoinGecko) and real-time nature, but does not mention rate limits, authentication requirements, or error handling. The tool is non-destructive, but behavioral constraints are minimal.

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 is front-loaded with the main purpose. It includes a list of examples and usage guidelines, which adds length but is well-organized. Could be slightly more concise by trimming redundant 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 the lack of output schema, the description adequately explains the return data (price, change, market cap, volume). It covers sibling differentiation and usage constraints. Slight gaps: no mention of error cases or response format, but sufficient for the tool's complexity.

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

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

Schema coverage is 100% with detailed descriptions for each parameter. The description adds examples of coins and currencies but does not enhance understanding beyond what the schema already provides. Baseline score of 3 is appropriate.

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

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

The description clearly states it retrieves real-time price data from CoinGecko, listing specific outputs (price, change, market cap, volume) and supporting 10,000+ coins. It distinguishes from siblings by mentioning alternatives like crypto_price_lite and crypto_fx_rates.

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

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

Provides explicit guidance: when to use this tool (full market picture), when to prefer crypto_price_lite (lighter response), when to use crypto_fx_rates (conversions), and when not to use it (fiat-to-fiat, historical 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?

With no annotations, the description carries full burden for behavioral traits. It mentions using CoinGecko public API, returning price, percentage change, and timestamp. It implicitly indicates a read-only operation. However, it does not discuss rate limits, latency, or error handling, which would enhance transparency further.

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 (about 100 words) and front-loaded: it opens with the core purpose, then provides sibling comparisons, then input support details. Every sentence is informative without redundancy, making it efficient for an agent 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 parameters, no output schema), the description adequately covers the functionality, input formats, and return fields. It lacks mention of edge cases or response format details, but overall it is sufficient for an agent to use correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by listing supported major coins (BTC, ETH, etc.) and explicitly noting that both tickers and full names are accepted. It also reinforces the default currency behavior, going beyond the schema's basic descriptions.

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

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

The description clearly states the tool retrieves the current spot price and 24-hour change for any cryptocurrency. It specifies the resource (CoinGecko public API) and distinguishes itself from siblings by positioning as a lightweight variant that omits extended market data, which is mentioned in the sibling crypto_price.

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

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

The description explicitly advises when to use this tool over alternatives: use crypto_price for richer data (market cap, volume), and use crypto_fx_rates for amount conversions. It also clarifies the input format and the default currency, providing clear context for choosing this 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 live exchange rates, ECB data source, and return fields (converted amount, rate, timestamp). It does not mention error handling or rate limits, but given the tool's simplicity, this is adequate. Slight deduction for not explicitly stating that unsupported currencies will cause failure (though implied by saying 'Supports 30+ currencies').

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

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

The description is four sentences long, front-loaded with the main action, then details, then usage guidance. Every sentence serves a purpose. Slight redundancy with schema examples (e.g., currency codes in both schema and description) but overall 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?

For a tool with no output schema and multiple siblings, the description covers: what it does, data source, supported currencies, return format (implicitly described), alternative tools for specific use cases, and exclusions (no crypto). This is complete for an agent to understand when and how 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 clear parameter descriptions. The description adds context by explaining the data source and return format, which helps the agent understand the tool's behavior beyond the schema. However, it does not add new meaning to the parameters themselves beyond the schema examples. Baseline 3 is exceeded due to additional context.

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

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

The description uses a specific verb ('converts') and resource ('monetary amount from one currency to another'), mentions the data source (Frankfurter API/ECB), and lists supported currencies. It clearly distinguishes from siblings like currency_convert_lite and crypto_fx_rates.

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

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

Explicitly states when to use this tool versus alternatives: 'Prefer currency_convert_lite or currency_fx_lite when only the numeric converted amount and rate are needed without metadata. Use currency_rates when... Do not use this tool for cryptocurrency conversion — use crypto_fx_rates or crypto_price.' This provides clear guidance 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, so description carries full burden. Discloses data source (Frankfurter/ECB), live rates, minimal response without timestamp/source, and supported currencies. Could mention API availability but not required.

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?

All sentences are informative; main function is front-loaded. Slightly lengthy due to sibling comparisons but each 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?

Covers purpose, return values (converted amount and rate), constraints (no crypto), and alternative tools. No output schema needed given description clarity.

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%, but description adds case-insensitivity, positive amount constraint, and example currency codes, providing value 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?

The description clearly states it converts fiat currencies using live Frankfurter API rates, and explicitly distinguishes itself as a lightweight variant without timestamp/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?

Provides explicit when-to-use and when-not-to-use guidance: names sibling tools (currency_convert, currency_fx_lite, currency_rates, currency_convert_open) and specifies exact use cases for each, plus clarifies crypto exclusion.

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 the description discloses that the source is an open public feed suitable for informational use, and explicitly states it does not support cryptocurrency pairs. This adds useful behavioral context beyond the raw schema.

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

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

The description is a bit lengthy but every sentence adds value by distinguishing from siblings and setting expectations. It is front-loaded with the core purpose and then provides usage guidance.

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, but the description mentions the tool returns 'the converted amount and the rate applied,' which is sufficient for a conversion tool. For a relatively straightforward tool, the description 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 description coverage is 100%, so the schema already adequately describes the three parameters (amount, from, to). The description adds no additional detail beyond the schema, but baseline 3 is appropriate.

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

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

The description clearly states the tool converts monetary amounts between fiat currencies using live exchange rates. It distinguishes itself from siblings by naming alternatives (currency_convert, currency_fx_lite, etc.) and specifying its niche.

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 this tool (when others are unavailable/rate-limited) and when to prefer alternatives (e.g., for ECB-auditable rates use currency_convert; for crypto use crypto_fx_rates).

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

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

No annotations are provided, so the description bears full responsibility. It discloses the use of live mid-market rates, the return of converted amount and rate, and that it does not support cryptocurrency. However, it does not mention the rate source provider, latency, or any potential rate limits, which would be helpful.

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

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

The description is relatively long but well-structured and front-loaded with the core purpose. Every sentence adds value, though minor trimming could improve conciseness without losing necessary detail.

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

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

Given no output schema, the description adequately describes return values (converted amount and rate). It covers purpose, usage guidelines, parameter requirements, and limitations. It is fairly complete for a conversion tool with well-defined parameters.

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

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

Schema coverage is 100%, so the descriptions in the schema already document all three parameters (amount, from, to) adequately. The tool description adds little new information about parameters beyond what is in the schema, such as reinforcing ISO 4217 codes and positivity of amount. This meets the 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?

The description clearly states the tool converts FX amounts between two fiat currencies using live mid-market rates, returns the converted amount and rate. It also distinguishes from siblings by specifying when to use alternatives like currency_convert or 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?

The description explicitly provides guidance on when to use this tool versus alternatives (currency_convert, currency_convert_lite, currency_rates, currency_convert_open, crypto_fx_rates, crypto_price_lite), including specific conditions for each. It also states that cryptocurrency pairs are not supported.

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 data source (ECB via Frankfurter API), support for live and historical rates, and crypto exclusion. Lacks details on rate limits, error handling, or authentication, but overall transparent for a read-only data 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 well-structured and front-loaded with core purpose, followed by usage guidelines. Slightly verbose but every sentence adds value; could be tightened without losing clarity.

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

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

Given the complexity (historical vs live, many siblings) and lack of output schema, the description adequately covers use cases, data source, and alternatives. Does not detail return format but this is acceptable for a simple rates API.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds no extra meaning beyond parameter names and types; it just restates the general functionality.

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 retrieves live and historical fiat currency exchange rates from the Frankfurter API (ECB source). Explicitly distinguishes from sibling tools like currency_convert, currency_convert_lite, currency_fx_lite, currency_convert_open, and crypto_fx_rates.

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

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

Provides explicit when-to-use guidance: use for historical/ECB-sourced rates; prefer alternatives for live conversions without historical requirements. Also states what it does not support (cryptocurrency).

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 calculations are performed 'entirely locally with no external API dependency'. Describes each calculation type's inputs and outputs (e.g., 'final value and total interest earned'). Lacks details on error handling or edge cases, but is generally transparent for a local calculator.

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: overview sentence, then per-type details, then usage guidance. Front-loads core purpose. Slightly verbose in detailing each calculation type, but still clear and scannable.

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 11 parameters, no output schema, and no annotations, the description covers calculation inputs, outputs, and usage context comprehensively. Missing details on return format or error behavior, but adequate for the tool's complexity.

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

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

Schema coverage is 100% with good descriptions, but the description adds significant value by grouping parameters by calculation type (e.g., 'Used for compound_interest, loan_repayment...') and providing context beyond the schema (e.g., 'Annual interest or discount rate expressed as a percentage').

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

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

The description clearly states it performs precise financial calculations across six types. It distinguishes from sibling 'financial_calculator_lite' by noting the full structured breakdown versus a single headline result, and from live price tools like 'stock_quote' and 'crypto_price'.

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

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

Explicit when-to-use: 'when an agent needs to perform any structured financial calculation'. Explicit when-not-to-use: 'Do not use to fetch live market prices' with named alternatives (stock_quote, crypto_price, currency_convert). Also advises preferring financial_calculator_lite for single headline result.

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

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

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

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

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

The description is concise, well-structured, and front-loaded: it states purpose, return value, differentiation from sibling, and usage guidance in a logical flow. Every sentence adds value.

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

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

Given the complexity (6 parameters, multiple calculator types, no output schema, no annotations), the description covers purpose, parameter dependencies, and sibling differentiation. It lacks explicit return format details or error handling, but is otherwise complete.

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

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

Schema coverage is 100% (baseline 3). The description adds value by associating specific parameters with calculator types (e.g., 'Required for compound_interest, loan_repayment...'), and by clarifying rate format (decimal). This goes beyond the schema's basic descriptions.

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

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

The description clearly states it performs common financial calculations locally, enumerates the supported types (compound interest, loan repayment, etc.), and specifies that it returns a single numeric result. It effectively distinguishes itself from the sibling financial_calculator.

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

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

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

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

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

No annotations present, so description carries burden. It discloses reliance on Finnhub API key and lists returned data (current price, high, low, change, sector, exchange). Does not mention rate limits, data freshness, or error behavior, but covers essential behavioral context.

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 with clear, front-loaded sentences. Each sentence serves a purpose: purpose, usage, comparisons, exclusions, requirements. 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?

No output schema exists, but description covers key return fields (price, high, low, change, sector, exchange). Mentions API key requirement. For a simple stock quote tool, this provides sufficient context for an agent to decide and invoke correctly.

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

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

Schema coverage is 100%, so baseline 3. Description adds value by explaining symbol parameter with examples and uppercase requirement, and include_company_info with default and effect. This goes beyond schema documentation.

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 retrieves real-time stock price quotes and company information via Finnhub API. It distinguishes itself from sibling tools like stock_price_lite, crypto_price, and currency_convert, making its purpose unambiguous.

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

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

Explicitly states when to use (stock price lookups, intraday performance, portfolio monitoring) and when not to (cryptocurrency, fiat conversion). Provides clear alternatives: crypto_price, currency_convert. Contrasts with stock_price_lite based on data richness.

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 declares read-only behavior, mock data fallback when no token configured, and token requirement. Minor gap: does not describe error handling or rate limits.

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 with a distinct purpose: scope, return fields, filters, usage guidance. Front-loaded with core action, 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 3 optional params, no output schema, and no annotations, the description covers purpose, return values, usage rules, and token dependency. Lacks details on error states or authentication flow, but adequate for the 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%, so baseline is 3. Description adds context by mentioning optional date filtering, limiting transactions to 100, and explaining the account_id source. This enhances understanding 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?

The description clearly states the tool retrieves bank account balances and transaction history via PSD2 Open Banking, covering 300+ banks. It specifies the return fields (balance, currency, up to 100 transactions with details) and distinguishes itself from siblings like 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?

Explicitly lists use cases (inspecting spending, verifying payments, etc.) and provides clear when-not-to-use guidance (payment initiation, Stripe-specific records). It names the alternative tool (stripe_payments) for excluded scenarios.

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

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

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

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

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

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

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

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

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

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

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

Schema coverage is 100% and description adds meaning: top_k default and when to increase, intent should be goal not tool name with examples, model_hint optional for model-specific ranking. Exceeds schema information.

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

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

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

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

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

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

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

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

With no annotations provided, the description carries the full burden. It discloses what the tool returns (current price as a single numeric value) and what it omits (intraday high/low, percentage change, etc.). It does not mention latency, rate limits, or authentication, but for a simple data retrieval tool, this is adequate.

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

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

The description is concise, using three sentences that are front-loaded with the core purpose. Every sentence adds value: first states what it does, second clarifies what it omits, third gives usage guidance and alternatives.

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 simplicity of the tool (one required parameter, no output schema), the description is complete. It specifies the return value, explains the limitations and omissions, and provides clear alternatives. No additional information is needed for an agent to use it correctly.

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

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

Schema coverage is 100% with the single 'symbol' parameter well-described. The description adds value by specifying that the ticker should be uppercase and providing examples like AAPL, MSFT, etc., which helps the agent understand the expected format.

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

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

The description clearly states it retrieves the current trading price for a publicly listed stock by ticker symbol, and explicitly distinguishes itself from the more comprehensive stock_quote tool, calling itself a 'lightweight variant'.

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 ('when only the raw current price is needed') and when to prefer stock_quote ('when the agent also needs price change, intraday range...'). It also directs users to crypto_price/crypto_price_lite for cryptocurrencies.

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

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

With no annotations, the description must cover behavioral traits. It explains that results are filtered by query_type and are a lightweight variant. However, it does not explicitly state that the operation is read-only or mention any rate limits. The return format is described as a 'simple records array', but details of record fields are omitted.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The description discloses return data (full Stripe charge objects with customer IDs, metadata, processing details) and a dependency (requires Stripe secret key configured on server). However, it does not mention authentication needs beyond the key, any destructive potential, rate limits, or side effects. Given no annotations, 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 well-structured: begins with purpose, then enumerates query types, followed by usage guidelines and exclusions. It is somewhat lengthy but each sentence provides value. Could be slightly more concise without losing clarity.

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

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

Despite lacking an output schema, the description adequately explains what each query type returns, including fields like status, amount, currency, failure reasons, customer details, etc. It also notes the dependency on a Stripe secret key. For a 2-parameter tool with medium complexity, this is sufficiently complete.

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

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

Both parameters (query_type and limit) are fully described in the input schema with clear descriptions. The description adds context by explaining what each query_type returns, but does not add significant new meaning beyond what the schema already provides. Schema coverage is 100%, so baseline is 3.

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

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

The description clearly states it retrieves live payment data from a Stripe account via the Stripe API. It enumerates four specific query types (recent_payments, failed_charges, customers, subscriptions) and what each returns, distinguishing it from sibling tools like stripe_payment_records.

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

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

Explicitly states when to use (investigate payment failures, audit transaction activity, etc.) and when not to use (bank account transactions, PSD2 Open Banking data, generic bank history), providing alternative tools (open_banking_transactions, bank_accounts) and differentiating from stripe_payment_records.

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 return value ('Returns the converted local time') and parameter inputs. While it doesn't mention edge cases like DST handling or error behavior, the tool is simple and the description adequately conveys its behavior.

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

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

The description is front-loaded with the purpose, but the numerous repetitive examples make it longer than necessary. It could be trimmed while retaining clarity.

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

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

The description explains the return value and lists all parameters, though it doesn't specify that the 'date' field is optional. Given no output schema, it provides enough context for a simple conversion tool. Minor gaps in optionality and error handling prevent a perfect score.

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 detailed descriptions. The description repeats parameter names in examples but adds little new semantic meaning beyond what the schema already provides, so baseline score of 3 is appropriate.

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

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

The description clearly states 'Converts time between timezones' and provides multiple concrete examples. It distinguishes itself from sibling tools like currency converters and stock quotes by explicitly focusing on timezone conversion tasks.

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 includes numerous specific use cases (e.g., converting 3:45 PM EST to Pacific Time), explicitly telling the agent when to use this tool. It also lists the accepted parameters, providing clear guidance on invocation.

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

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

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

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

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

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

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

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

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

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

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

Schema coverage is 100% with well-described parameters. Description adds minimal extra value beyond schema, such as 'no API key required' but does not deepen parameter understanding.

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

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

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

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

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

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

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

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

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

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

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

The description is well-structured with core functionality first, then use cases, then limitations. While comprehensive, it is not overly verbose; every sentence adds value. Minor redundancy could be trimmed, but overall efficient.

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

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

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

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

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

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

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

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

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

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

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

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

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