xsignal

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

With no annotations, the description fully discloses behavior: it is a paid, outcome-priced endpoint that returns 'gaining', 'fading', or 'abstain', provides a tamper-evidence receipt, and clarifies it's a heuristic, not financial advice. No hidden destructive or reading side effects are implied.

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 flagship label and key differentiator. Every sentence adds value, though the first sentence could be slightly tighter. Well-structured for an AI agent.

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 moderate complexity, the description covers return values ('gaining', 'fading', 'abstain'), receipt, pricing (3 free calls, $0.01), and confidence semantics. No obvious gaps.

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

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

Schema coverage is 100%, baseline 3. The description adds meaningful semantics: it explains the role of min_confidence (causes abstain if below threshold), defines 'addr' as base token address, and clarifies 'question' defaulting to symbol. This goes beyond the schema descriptions.

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

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

The description clearly states the verb ('get') and resource ('intent'), and distinguishes this tool from siblings as the only x402 signal that can abstain. It provides a specific purpose: yield a momentum verdict with confidence threshold.

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 explains when to use: to obtain a momentum verdict with a confidence bar, and gives an example. It omits explicit when-not-to-use or alternatives, but the abstain behavior implicitly differentiates from siblings.

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

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

No annotations are provided, so the description must disclose all behavioral traits. It covers cost, free tier, and the safety-first gating logic, but lacks details on authentication, rate limits, or side effects. The description is moderately 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 but packed with information, front-loading the core functionality. Some technical details (e.g., cost, endpoint) are included efficiently. Could be slightly more structured but is effective.

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 one parameter and no output schema, the description covers input format, output types, cost, and behavioral logic. It lacks output structure detail but is sufficient for agent understanding.

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 single parameter 'addr' is described as '0x Base token address' in both the description and schema, with no additional semantics added. Schema coverage is 100%, so baseline 3 is appropriate.

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

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

The description clearly states the tool fuses on-chain safety and momentum into a single recommendation, listing possible outputs (GO, CAUTION, etc.) and specifying it's for Base tokens. It answers 'is this token safe to touch AND moving?', providing a distinct purpose.

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 mentions cost and free tiers but does not explicitly state when to use this tool versus siblings like get_screen or get_signal. No alternatives or exclusions are given; usage context is implied but not directive.

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 covers behavioral traits: runs preflight (safety+momentum), returns GO/token verdicts/summary, payment model, and disclaims 'Not financial advice.' No contradictions or missing critical behaviors.

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

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

Description is front-loaded with purpose, then details. It is somewhat long but each sentence provides value. Could be tightened slightly but remains effective.

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 only one parameter and no output schema, the description adequately explains input, output, payment, and usage context. No missing information for a simple batch screening 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% and baseline is 3. Description adds value by noting that 'addrs' can be an array or comma-separated string, and restates the 'up to 10' limit, slightly enhancing schema info.

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

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

Description clearly states the tool runs batch preflight screening on up to 10 Base tokens, outputs GO verdicts, per-token verdicts, and a summary count. Identifies the target use case ('For an agent screening a watchlist'), and effectively distinguishes its batch behavior from sibling 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?

Provides clear context for when to use: screening a watchlist. Includes payment details (x402, cost, free calls) and input format. Does not explicitly list when not to use or compare to siblings, but the context is sufficient for an agent.

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

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

No annotations are provided, so the description carries the full burden. It discloses real-time nature, scoring criteria, deduping, ranking, and cost details. Missing output format but covers key behaviors.

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 and front-loaded with the main purpose. The structure is logical but slightly dense; could use bullet points for readability.

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 (5 params, no output schema, no annotations), the description covers purpose, input modes, cost, and provides an example. It lacks explicit output structure but is sufficient for agent invocation.

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

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

Schema coverage is 100%, baseline 3. The description adds value by explaining the two main modes ('query' vs 'candidates') and the pricing parameter via URL example, going beyond schema definitions.

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 provides real-time social signal for a topic with scoring, deduping, and ranking. It distinguishes from sibling tools like get_intent or get_token_brief by focusing on topic signal.

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 explains input modes (query or candidates) and gives an example. While it doesn't explicitly state when not to use or compare with siblings, the context is clear enough for basic usage.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool is a fused call with pricing details ($0.05, 3 free trials) and provides an example. It does not discuss rate limits or error behavior, but for a read-only info 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 a single paragraph that front-loads the core purpose effectively. However, it includes pricing details and an example, which slightly reduces conciseness. Still, every sentence adds value.

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

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

Given the tool has 2 parameters, no output schema, and no annotations, the description covers the output (market flags, social posts, summary) and includes pricing and an example. It is fairly complete for its intended use.

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

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

Schema coverage is 100%, and the parameter descriptions in the schema are already detailed. The tool description does not add new information about parameters beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states that this tool combines Base token market intel and real-time social signals into a single brief. It distinguishes itself from sibling tools by emphasizing it is a 'fused MEAL' that saves the agent the work of fetching and fusing separate data, implying siblings are more granular.

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 use when a combined overview is needed, mentioning it saves the agent fetch-and-fuse work. However, it does not explicitly state when not to use it or name alternative tools, leaving some ambiguity.

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

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

No annotations are provided, so description carries full burden. It correctly identifies the tool as market data (not a trust rating) and discloses API cost and free tier. However, it does not explicitly state read-only nature or potential side effects, though the data-focused description implies no mutations.

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?

Two sentences with no wasted words. Key information is front-loaded: what it does, what it returns, and how it should be used.

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 only one parameter and no output schema, the description provides a good overview of the data returned (liquidity, volume, price, etc.) and its use case. Could be slightly more explicit about the return format (e.g., JSON object), but sufficient for an AI 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 has 100% coverage with description for the single 'addr' parameter. The description adds context about 'public DEX pools' but adds minimal extra meaning beyond the schema. Baseline 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?

Description explicitly states it returns 'Base token MARKET data' with specific metrics (liquidity, volume, price, etc.) and clarifies it is not a trust/safety rating. It also distinguishes from sibling tools like get_token_brief by positioning itself as input to that 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?

Provides clear guidance: 'Best used as an input to get_token_brief' and mentions cost and free usage (3 free per wallet). While it doesn't explicitly state when not to use or list alternatives, the context is sufficient for an agent to decide.

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