Munchausen Crypto Analytics

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

Given no annotations, the description carries the full behavioral burden. It exposes that this is an LLM operation, enumerates output dimensions, and importantly discloses the $0.02 USDC payment via x402, which is a meaningful behavioral trait. However, it doesn't clarify the confidence scale or any potential payment-failure/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 a single sentence that front-loads the core purpose, then enumerates the output content, and ends with the key billing caveat. Every phrase earns its place with no redundant or boilerplate language.

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 single-input tool with no output schema, the description supplies the essential output fields (verdict, confidence, reasoning) and a critical cost/payment detail. It is missing only minor specifics like the confidence scale or format of reasoning, but overall it is sufficient for an agent to understand the interaction.

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 documents the single parameter, 'claim', with a sufficient description ('The claim to verify'), giving 100% schema coverage. The tool description adds context about the fact-check output but no extra parameter syntax, format, or constraints, so the baseline of 3 applies.

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

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

The description clearly identifies the tool as an LLM fact-checker for a claim, with an explicit list of possible verdicts, confidence, and reasoning. This is distinct from the sibling tools, but it doesn't explicitly differentiate or name alternatives, so it receives a 4 rather than a 5.

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?

From the description, an agent can infer that this tool is appropriate when a claim needs verification and wants verdict/confidence/reasoning. However, there is no explicit guidance on which sibling tools are alternatives, or when depending on market research or token safety instead.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does this well by stating the data is real-time, the service is free with no signup, and requests are rate-limited to 5 per minute. It does not detail error behavior or response format, but for a simple no-parameter tool this is solid coverage.

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 concise sentence that front-loads the core purpose and then adds two high-value usage constraints. Every clause earns its place with 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?

Given the tool's minimal complexity—no parameters, no output schema, and no annotations—the description is largely complete. It covers what data is returned, the real-time nature, access requirements, and rate limits. It could mention output format or rate-limit failure behavior, but these are not critical for basic invocation.

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

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

The input schema has zero parameters, so the baseline is 4. The description adds no parameter details, but none are needed since the tool takes no inputs and the schema coverage is effectively complete.

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 identifies the resource as real-time BTC/ETH/SOL prices, which distinguishes it from sibling tools focused on analysis, quotes, or research reports. It lacks an explicit verb like 'retrieves' or 'returns,' but the tool name and phrasing make the purpose unambiguous.

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

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

The description implies when to use this tool—when real-time prices for BTC, ETH, or SOL are needed—and provides useful context like free access and rate limits. However, it does not explicitly state when to prefer this over sibling tools like get_market_quote or get_market_analysis.

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?

Mentions the tool is paid ($0.05 USDC via x402), which is a transparent behavior. Also notes 'AI-generated', implying possible non-determinism, but does not elaborate on rate limits, side effects, or failure modes. Given no annotations, the description carries the burden but only partially covers it.

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, concise sentence that directly states the tool's purpose and key output components. No fluff or redundant wording.

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?

Provides sufficient context for a simple tool with no parameters and no output schema: it lists what the analysis includes (sentiment, key levels, risk factors) and notes the cost. Could specify the market type (e.g., crypto) but the sibling names imply it, so it is reasonably 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?

The tool has zero parameters, so the schema is fully covered (100%). Baseline for 0 params is 4; the description does not need to explain any parameters, and it avoids adding irrelevant details.

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 provides AI-generated market analysis with sentiment, key levels, and risk factors, distinguishing it from price/quote/report tools. Could be more specific about the market type or analysis scope, but the core purpose is clear.

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

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

No explicit guidance on when to use this tool over the sibling tools (e.g., get_research_report). The only usage hint is the cost, but it does not help with selection. Lacks comparative context.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool is paid ($0.01 USDC via x402) and provides real-time data, which are useful behavioral traits. However, it does not mention potential rate limits, failure modes, or whether the quote is delayed, which could be relevant for an AI agent.

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

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

The description is a single sentence that efficiently conveys the core purpose and the key differentiator (cost). It is front-loaded with the primary task, and there is zero waste or redundancy.

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

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

For a simple tool with one parameter fully described by the schema and no output schema, the description covers the essential purpose and cost. However, it lacks context on expected output format (e.g., price, 24h change) and any usage constraints like symbol availability, which could be needed for an agent to invoke it correctly in diverse scenarios.

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

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

The schema already fully covers the single parameter 'symbols' with a description ('Comma-separated: btc,eth,sol'). The description does not add further meaning beyond label and format, but since schema coverage is 100%, the baseline of 3 is appropriate. The description's mention of 'specified symbols' aligns with the parameter but does not enrich it.

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

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

Clearly states it provides real-time market quotes for specified symbols, using a specific verb ('get') and resource ('market quote'), and differentiates from siblings by noting the cost and real-time nature. The mention of 'specified symbols' and the comma-separated format implies a simple quote retrieval, distinguishing it from deeper analysis or reports.

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 implies usage for obtaining real-time quotes for a list of symbols, but does not explicitly state when to use this tool versus alternatives like 'get_crypto_prices' or 'get_market_analysis'. No exclusions or alternative recommendations are provided; the cost and real-time aspect hint at differentiators, but explicit guidance is missing.

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

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

With no annotations, the description carries the transparency burden. It discloses the paid nature ($0.25 USDC via x402) and the report's contents, which is useful. However, it does not explain payment flow, authentication needs, error behavior, or data freshness—key behavioral details for a paid external 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 sentence that front-loads the core purpose, then compactly lists report sections and the payment detail. Every word contributes value and there is no 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?

For a zero-parameter tool with no output schema, the description is reasonably complete: it defines the report scope and the cost. The main gap is lack of differentiation from sibling analysis tools, but otherwise an agent can understand what this tool will provide.

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 tool has zero parameters, so there is no parameter documentation burden. The description also adds helpful context about the report contents, exceeding the minimal 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 returns a full structured market research report covering technical, on-chain, and outlook content. It uses a specific resource noun and conveys scope, but it does not explicitly distinguish itself from the sibling tools like get_market_analysis.

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 no explicit guidance on when to use this tool versus alternatives such as get_market_analysis or get_market_quote. It implies it is for comprehensive research, but gives no exclusions, prerequisites, or alternative recommendations.

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 and does useful work by disclosing that the tool is paid and through which mechanism ($0.005 USDC via x402). It also flags the output shape. It does not cover edge cases like URL failure or rate limits, but the paid aspect is a critical hidden 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 a single, tight sentence that front-loads the core action, names the return value, and includes the payment requirement without any 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?

For a one-parameter, no-output-schema tool, the description is largely sufficient: it states the operation, the return content, and the payment condition. It could mention error/non-HTML handling, but that is a minor gap given the simplicity and 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?

The input schema already documents the sole parameter fully, including the format 'Full URL starting with http(s)://'. The description adds no extra parameter-specific information beyond saying 'any URL', so the schema carries the weight and the score stays at the baseline.

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

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

The description uses a specific verb ('Fetch') and resource ('any URL') while explicitly naming the return result ('clean extracted text with title'). It clearly differs from its crypto/market-focused sibling tools, so an agent can understand it as the generic web-content 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 provides a clear context: use it when fetching any URL for extracted text. It does not explicitly name alternatives or say when not to use it, but the general-purpose scope and the cost warning make the usage fairly clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently states that the tool uses live DEX data, produces a heuristic 0-100 risk score, and costs $0.01 USDC via x402. This is meaningful behavioral context beyond a simple 'screens tokens' statement, though it omits failure modes or invalid-address 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 entire description is one compact sentence that front-loads the core purpose, then lists key inputs and output, and ends with the payment detail. Every clause earns its place with no redundancy.

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

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

For a single-parameter tool with no output schema, the description covers the essential context: what it screens, what data it uses, what score it produces, and that it costs money. It does not explicitly state the risk-score direction (higher = riskier), but 'risk score' makes that reasonably inferable.

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

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

Schema description coverage is 100% and the only parameter, address, is already described as 'EVM token contract address 0x...'. The tool description adds no new parameter-level meaning beyond restating 'EVM token', so the schema carries the weight.

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 identifies the tool's function: EVM token rug-pull risk screening. It specifies the resource (EVM token), the inputs (liquidity, volume, pair age), and the output (0-100 heuristic risk score), which distinguishes it from the sibling market analysis and price tools.

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

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

The description implies when to use the tool: when an EVM token needs rug-pull risk screening. However, it does not explicitly state when not to use it or compare it to alternatives like get_market_analysis or fact_check, leaving usage guidance to inference.

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