LION - Trend Intent MCP + Marketplace Visibility Audit

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

With no annotations provided, the description carries full burden. It discloses payment (0.005 USDC), return format (typed JSON rows + freshness fields), public keyless access, the tools/call vs paid route distinction, and a no-advice disclaimer. This is thorough and exceeds the minimum needed.

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 dense but front-loaded with the core purpose, followed by parameter breakdown and payment details. Every sentence carries useful information, though length is substantial; it is organized logically.

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 8 parameters, no output schema, and no annotations, the description is remarkably complete. It covers purpose, parameter semantics for all three verticals, return format, payment flow, and safety disclaimers, leaving little ambiguity for an agent.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining each source's exact output semantics (e.g., base_dex via DexScreener, token_risk via GoPlus) and clarifies token requirements per source. However, it does not elaborate on every parameter's syntax 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 is a single typed x402 query endpoint for live Base on-chain data, covering multiple verticals (base_dex, token_risk, defi_yields) via one call. This specific verb+resource+scope distinguishes it from siblings like lion_base_dex_signals_json and lion_trend_intent_signal_csv.

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 explains when to use the tool (any agent request over live Base data, multiple verticals) and describes each source's output. It also notes payment requirements, but does not explicitly name alternatives or provide exclusion criteria, so it falls short of a 5.

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 and does so excellently: discloses public DexScreener source, no API key required, paid route payment amount and token, that tools/call returns only payment-required metadata, and explicitly disclaims financial advice.

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 dense but efficient: every sentence adds value—scope, metrics, source, payment workflow, and disclaimer. Information is front-loaded and well-structured, avoiding fluff.

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

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

Given no output schema and no parameters, the description fully compensates by listing expected output fields, explaining the scoring mechanism, payment steps, and caveats. It is complete for an agent to understand what the tool returns and what actions are required.

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 baseline is 4. Although description focuses on output fields (price_usd, liquidity_usd, etc.), it adds semantic context about the 'mechanical activity_score' and raw on-chain nature, which is useful for interpretation.

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 a specific verb+resource: 'Live Base (eip155:8453) DEX market-data feed' that 'Returns a fresh ranked snapshot of active Base tokens'. It lists exact metrics, distinguishing this tool from sibling tools like trend-intent signals or adaptive queries.

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?

Context is clear: this is for trading/research agents needing live Base DEX data, with explicit payment workflow and source info. However, it does not name alternatives or state when not to use this tool, so it lacks explicit 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 provided, the description carries the full burden and delivers rich behavioral context. It reveals the 0.05 USDC cost, the JSON-RPC error for unpaid calls, the auto-construction of EIP-3009 signatures by wallet-capable clients, and explicit limitations (no payment, no settlement, no fund movement). This goes beyond a simple 'checks terms' statement.

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

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

The description is moderately long but densely informative, front-loading the purpose and then methodically covering behavior, cost, errors, and limitations. Every sentence contributes value, though it could be slightly tightened by removing redundant phrasing such as 'LION does NOT perform payment inside MCP' and 'no fund movement' which overlap.

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 having an output schema, the description lists all major return fields (blockers, policy fit, recipe, funnel stage, readiness score, risk flags), explains the error condition, states the cost, and clarifies the tool's scope limitations. For a tool with one parameter and no annotations, this is a comprehensively complete description.

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

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

The sole parameter `url` is fully described in the schema (pattern, format, description). The description only mentions it as a 'target x402/MCP service URL' without adding new semantic details beyond the schema. With 100% schema coverage, the baseline of 3 is appropriate.

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

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

The description opens with a specific verb+resource: "Pre-spend payment-term trust check for x402 endpoints." It clearly states the tool audits payment terms before signing a Payment-Signature, distinguishing it from sibling data-query tools. The purpose is further clarified by enumerating the exact outputs (blockers, policy fit, recipe, funnel stage, readiness score, risk flags).

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 guidance is provided: "Before your agent signs a Payment-Signature, call this." It also gives when-not-to-use caveats: LION does not perform payment, is not a smart-contract security audit, and provides no settlement guarantee. While no alternative tool is named, the exclusions and context are 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, the description carries the full transparency burden. It clearly discloses the payment requirement ($0.01 USDC via x402), that tools/call returns only payment-required metadata, and that settling the invoice fetches the CSV. It does not mention potential rate limits or errors, but for this type of data tool the disclosed traits are sufficient.

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

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

The description is moderately long but each sentence delivers useful information: purpose, content, use cases, payment, and call behavior. It is front-loaded with the core purpose and avoids fluff. Slight redundancy in mentioning 'flat CSV' twice, but overall efficient.

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

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

Given no output schema or annotations, the description is fairly complete. It covers what data is included, the CSV format, the intended workflows, and the exact payment/fetch flow. It does not provide an example of the CSV rows or headers, but the column names listed give a clear picture. This is adequate for invoking 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?

The tool has zero parameters, so the baseline is 4. The description adds detail about the column set (Ledger vs Trezor, Bitcoin-only wallet research, etc.), which informs what the returned data will contain, even though it is not about parameters per se. This adds value beyond the empty 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 that the tool provides hardware-wallet buyer-intent and self-custody onboarding-friction intelligence as a flat CSV. It distinguishes from the sibling JSON variant by explicitly mentioning 'Same column set as the JSON variant' and the CSV format, leaving no ambiguity about the resource and output type.

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

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

The description gives context on when to use it: 'Optimised for spreadsheet / pipeline ingestion and affiliate-routing or comparison-research workflows.' It also references the JSON variant as an alternative, but does not explicitly say 'use JSON instead' or list exclusions, so it stops short of fully explicit when-not guidance.

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

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

With no annotations, the description fully discloses the critical behavioral aspects: it charges $0.01 USDC via x402, returns payment-required metadata rather than the actual dataset, and requires an x402-capable client to settle and fetch the JSON. It also notes the feed is Non-PII, offering strong 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?

The description uses five focused sentences, each adding distinct value: content taxonomy, use cases, payment mechanism, and return behavior. It is front-loaded and contains no filler or redundant statements.

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 parameters or output schema, the description fully explains the dataset scope, intended workflows, payment/settlement process, and the fact that the MCP call returns metadata rather than data. This is sufficient for an agent to correctly invoke and handle 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?

The tool has zero parameters and the input schema is empty, so the schema provides complete coverage. The description adds no parameter details, but none are needed; the baseline of 4 for zero-parameter tools 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 a hardware-wallet buyer-intent signal feed in JSON format and lists specific covered topics, distinguishing it from the CSV sibling by format and content. However, it lacks an explicit action verb like 'retrieve' or 'provide,' making the purpose slightly implicit rather than directly stated.

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 states clear use contexts: agent routing, affiliate/comparison-content workflows, and research. It does not explicitly name when not to use this tool versus the CSV alternative, but the provided use cases give enough guidance for selection.

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