Liquid State — Crypto Market Intelligence

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

With no annotations, the description carries the full burden. It lists return fields (headline, category, coins, summary, url, publishedAt) and content types (price action, liquidations, funding, macro). But it lacks details on ordering, freshness, or potential limitations, making it 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?

Two sentences with no wasted words. The purpose is front-loaded, and the structure is efficient and clear.

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 lists return fields. It mentions the source (Liquid State) and content categories. Minor missing details like ordering or coverage limits, but overall complete for a simple list 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% for the single parameter 'limit', which has a description in the schema. The tool description does not add extra meaning beyond what the schema 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 the tool returns 'recent crypto market-intelligence briefs from Liquid State' listing specific fields. It distinguishes from siblings like get_derivatives and get_market_overview by focusing on analyst write-ups on price action, liquidations, funding, and macro.

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 usage context: 'Use when the user wants recent crypto news/analysis or what's moving markets.' However, it does not mention when not to use or suggest alternatives, but the context is 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?

No annotations are provided, so the description carries the full burden. It mentions data is 'Live' but does not disclose any behavioral aspects like idempotency, rate limits, authentication needs, or potential side effects. The lack of such details makes it insufficient for an agent to understand operational 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 only two sentences, with no wasted words. The first sentence efficiently lists what the tool provides, and the second gives usage guidance. Every sentence earns its place.

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

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

Despite low complexity, the description covers the data source (OKX), data fields, and usage context. Since there is no output schema, listing the returned fields (funding rate, open interest, etc.) ensures the agent knows what to expect. Minor improvement: could mention data recency or format, but overall sufficient.

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

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

Schema description coverage is 100% for the single parameter 'symbol'. The description adds context (data from OKX, derivative type) but does not add meaning beyond what the schema already provides (ticker example). Baseline 3 is appropriate.

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

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

The description clearly states it provides live perpetual-futures derivatives for one crypto asset, listing specific data fields (funding rate, open interest, long-account %, last price). This gives a specific verb+resource, but does not explicitly differentiate from sibling tools like 'get_briefs' or 'get_market_overview', though the derivative focus implies distinction.

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 says 'Use when the user asks about funding, open interest, positioning, or the perp price of a specific coin.' This provides clear context for when to use. However, it does not mention when not to use or suggest alternatives, which would improve the 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 provided, so description carries burden. Discloses data content but omits behavioral traits like data freshness, rate limits, or cost implications. Adequate but not thorough.

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, front-loaded with purpose, no unnecessary words. Efficient and clear.

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 covers return contents. Could add details like data format or timeframe, but for a snapshot it's 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?

No parameters, schema coverage 100%. Description adds context beyond schema by specifying the returned assets and data types, meeting the baseline for zero-parameter tools.

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?

Specifies exactly the tool returns (funding rate + price snapshot) and its scope (BTC, ETH, SOL). Distinct from siblings like get_briefs or get_derivatives.

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 'quick market-wide read', guiding when to use. Does not explicitly mention when not to use or alternatives, but context is clear enough.

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 disclosure. It states the tool returns live data with a timestamp, source, and citation, but does not mention caching, rate limits, or authorization needs. Adequate but could be more explicit about 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 two sentences long, clearly front-loading the purpose and output details. It is efficient without being overly verbose, though the first sentence could be slightly shortened.

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 zero parameters and no output schema, the description provides sufficient context about the tool's return values (as_of, source, regime signal). It does not list possible errors, but for a simple snapshot tool this is acceptable completeness.

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

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

The tool has zero parameters, so the description adds value by detailing the output structure and use case. Schema coverage is 100% (no parameters), so no further parameter information is needed.

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 exactly which crypto assets and metrics are included (funding rates, prices, open interest, etc.), and explicitly states it is designed for AI citation with a derived regime signal. This clearly differentiates it from siblings like get_market_overview.

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 tells the agent to use the tool when the user asks about current crypto market conditions. While this provides clear context, it does not explicitly mention when not to use it or offer alternative sibling tools, which prevents a perfect score.

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