0xrhXBT — Robinhood Chain Intelligence

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

Despite no annotations, the description fully discloses behavioral traits: returns 'observed data only, never a buy/sell recommendation', 'unknown values are null, never fabricated'. This is highly transparent for a risk-assessment 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 well-structured and front-loaded with purpose, but slightly verbose with enumeration of return fields. Every sentence adds value, so it earns a high score, though minor trimming could improve conciseness.

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 no output schema, the description provides a complete picture: it lists all return components (liquidity, volume, holders, rug-guard, launch-radar assessment, exit price impact) and clarifies data handling. For a 2-param tool, this is fully adequate.

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?

Adds significant meaning beyond the input schema: clarifies 'symbol_or_address' can be symbol or 0x address, and explains 'trade_size_usd' is for exit-impact estimate with default 2000. Schema covers only 50% of param descriptions, but the description compensates fully.

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 'pre-trade risk readout' for a Robinhood Chain token, using a specific verb ('check risk') and resource ('token'). It distinguishes itself from sibling tools like get_chain_stats or get_token by focusing on risk assessment before trading.

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 it is 'designed to be called BEFORE placing a trade elsewhere' and mentions placement via 'robinhood agentic trading'. This provides clear when-to-use guidance and differentiates from other tools.

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 goes beyond a simple read statement by explaining valuation methodology ('valuation-clocked'), exclusion of unpriced rows with a flag, and the null-with-dataNote behavior instead of fabricated zeros. This is exemplary transparency for a read tool with no annotations.

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 and front-loaded with the core purpose. However, the second sentence is dense and contains somewhat cryptic phrasing ('valuation-clocked', 'The one read no L2-only view can produce') that might reduce clarity. It is appropriately sized but could be streamlined 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 no output schema and no annotations, the description adequately explains return behavior for the tool's complexity. It mentions key output fields (netUsd, valuationCoverageComplete, dataNote) and handles edge cases. For a zero-parameter, read-only tool, this is complete and will guide an agent effectively.

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 per the rubric baseline is 4. The schema is empty and additionalProperties is false, so there are no parameter semantics to explain. The description correctly omits parameter details, as none exist, and does not need to compensate.

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: 'Net capital flow across the canonical L1 bridge over the last 24h'. It distinguishes from sibling tools by focusing on bridge flows specifically and adds a unique qualifier ('The one read no L2-only view can produce'). This is a precise, non-tautological statement.

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 provides clear context for when to use (to get bridge flow data) and hints at uniqueness, but it does not explicitly exclude alternatives or name specific sibling tools. The phrase 'The one read no L2-only view can produce' gives some differentiation but is not a direct usage guideline. No explicit when-not or alternative names are given.

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 mentions 'Latest' but lacks details on data freshness, refresh rate, safety, or side effects. The tool is likely read-only, but this is not confirmed.

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?

One sentence, front-loaded with key information, zero waste. Perfectly concise for the simplicity of the tool.

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, description lists exact metrics returned. For a zero-parameter tool, this is reasonably complete, though a hint about data structure or units would enhance usability.

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 exist (baseline 4). Description adds meaning by listing the returned stats, which compensates for the lack of output 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 provides 'Latest Robinhood Chain stats' and lists specific metrics (TVL, 24h DEX volume, stablecoin mcap, transactions, total addresses), distinguishing it from sibling tools that focus on individual tokens or risk 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?

No explicit guidance on when to use this tool versus alternatives. For a zero-parameter tool, context is simple, but exclusions or comparisons to siblings like get_token or get_intel would improve clarity.

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 discloses meaningful behavioral traits: data is 'computed from chain-timestamped logs' and 'tokens with no settlement in the window are omitted, never zero-filled'. These details go beyond a simple data summary and help set expectations for data completeness and source, which is valuable for a read-only 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 dense sentence but every clause contributes specific information about the metrics and behavior. It is not as tightly structured as a two-sentence summary, and the opening phrase 'the layer that exists nowhere else' is slightly promotional, but it avoids redundancy and stays focused on content.

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 no output schema, the description covers the key return aspects: what metrics are included (offHoursPct, minted/burned shares, dvpVwapUsd), how they are computed, and the omission policy. It implicitly indicates a per-stock breakdown through 'per-stock settlement activity'. For a tool with a single parameter and relatively clear scope, 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?

The input schema provides 100% coverage with a single parameter, `window`, including an enum and a description ('settlement window (default 24h)'). The tool description does not mention the parameter or add any additional nuance beyond what the schema already states, so 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 identifies the resource as 'per-stock settlement activity' and enumerates specific metrics (offHoursPct, minted/burned shares, dvpVwapUsd), making the tool's scope clear. However, it lacks an explicit verb like 'retrieves' or 'lists', instead using a noun phrase, which slightly reduces clarity. It does differentiate from siblings by emphasizing 'the layer that exists nowhere else'.

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 by claiming its content 'exists nowhere else', suggesting it is the sole source for these settlement metrics. It does not name alternative tools or provide explicit when-to-use/when-not-to-use guidance, leaving the user to infer usage from the unique data claims.

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. The description mentions 'Recent' and 'severity 1-5' but does not disclose caching, rate limits, real-time nature, or what happens with empty results. The parameter 'limit' implies pagination but is not discussed.

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 a single sentence with a list. It is front-loaded with the main purpose. However, it could benefit from slight restructuring (e.g., separating parameters).

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 2 simple parameters and no output schema, the description covers the core functionality adequately but lacks details on categories, output format, and pagination. It is minimally 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?

The description adds minimal semantics: 'Optionally filter by category' indicates the category parameter is optional but does not list valid categories or explain the limit parameter beyond schema constraints. Schema coverage is 0%, so the description should compensate but does not.

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 retrieves recent intel signals, listing specific types (volume spikes, whale transfers, etc.) and mentioning severity and provenance. It distinguishes from sibling tools like check_token_risk or get_narratives by focusing on actionable signals.

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 guidance on when to use this tool versus alternatives. It does not specify use cases, prerequisites, or situations where other tools would be more appropriate.

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?

Without annotations, the description carries the full burden. It explains ranking methodology (entry pricing, win condition, penalties for rug-shills, minimum calls) and includes the on-watch bench. Covers behavioral traits beyond a simple list.

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 dense sentence packs significant detail without redundancy. Could be split for readability but is not overly long. Every part is informative.

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, yet the description does not specify return fields or structure. It mentions 'on-watch bench' but lacks detail on what each row contains. Incomplete for a data retrieval 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?

Only one parameter 'limit' with full schema coverage. The description adds no extra meaning beyond the schema's 'max rows (default 20)'. 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?

Explicitly states the tool returns a KOL leaderboard ranked by verified onchain call performance. Details like ranking criteria, win condition, penalties, and bench distinguish it from sibling tools like get_chain_stats or get_token.

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 (for leaderboard data) but does not explicitly state when not to use or mention alternatives. No comparison with siblings or exclusion 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?

Despite no annotations, the description fully discloses behavioral traits: three-output assessment, safety score, liquidity, paid-promotion flag, and a critical caveat that a high score does not guarantee legitimacy. This adds significant transparency beyond structured fields.

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 key information. The first sentence covers purpose and output, the second adds an important caveat. 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?

Despite no output schema, the description explains the output structure and limitations. It covers the main aspects but lacks details on pagination or interpretation of the three-output gates. Adequate for a radar 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 50% (limit has description; status has enum but no description). The description explains the output assessment but does not clarify the meaning of the status filter values (watching, clean, flagged) in input context. It adds some value but is not fully compensatory.

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 explicitly states what the tool does: lists early-stage Robinhood Chain candidates from X chatter and onchain scans, with a three-output assessment. It distinguishes itself from siblings like check_token_risk and get_trending_tokens by focusing on early-stage candidates with specific evaluation criteria.

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 discovering early-stage candidates, and the sibling tools provide alternatives for detailed risk or trending. However, it does not explicitly state when not to use or provide direct comparisons.

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 a non-obvious behavioral trait: freshness-gating, where outdated rows are withheld and the response says 'stale' rather than serving stale data. It also explains the meaning of stockCollateral, adding context beyond obvious read 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?

Two sentences, each dense with useful information. The first defines scope and data, the second explains an important behavioral constraint. No filler or repetition of schema details.

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 covers the tool's purpose, key data fields, unique feature, and freshness behavior. With a single optional parameter and no output schema, this is more than sufficient for an agent to understand and invoke the tool 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?

The schema already covers 100% of the parameter with a clear description. The tool description adds meaningful domain context by explaining that stockCollateral refers to registered tokenized equity, i.e., leverage against real stock, which is unique to this chain. This goes beyond the schema's phrasing.

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 Morpho lending markets on Robinhood Chain with specific data fields (supply/borrow USD, utilization, APYs) and highlights the unique stockCollateral feature. This distinguishes it from all sibling tools, none of which mention lending markets.

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 strong context for when to use this tool (when needing lending market data) and implicitly signals its unique value via stockCollateral. It doesn't explicitly name alternatives or exclusions, but the domain context is clear enough for an agent to select it appropriately.

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 fully discloses the output format (rankings, categories, clusters, labels, symbols) and implies no side effects or destructive actions. Since no annotations are provided, the description adequately informs the agent of the read-only nature.

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, well-structured sentence that conveys all necessary information without redundancy. Every element (ranked mindshare, static/dynamic, stability labels, member symbols) 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?

Given no parameters, no output schema, and no annotations, the description provides comprehensive information about the tool's purpose and return values, leaving no gaps for the 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?

With zero parameters and 100% schema coverage, the description adds no additional parameter info, which is appropriate. The baseline of 4 applies as no further semantic clarification 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 a verb ('ranked'), resource ('narratives'), and distinct output details ('static categories', 'dynamic clusters', 'stability labels', 'member symbols'), clearly distinguishing it from sibling tools like get_trending_tokens or get_token.

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 narrative analysis but does not explicitly state when to use this tool over alternatives such as check_token_risk or get_intel. The context is clear but lacks direct comparative 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 carries the full transparency burden and excels: it discloses the 10-minute precomputation cycle, the presence of computedAt, validUntil and a stale flag, and the critical behavior that stale rows are withheld rather than served. It also clarifies semantic nuances about counterparties and components, which are easily misinterpreted.

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, information-dense sentence that front-loads the main purpose ('stock-settlement counterparty graph') before filling in specifics. Every clause adds value—aggregation fields, staleness handling, and semantic disambiguation—without redundant or filler 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 the complexity of the graph tool, the description covers key aspects: what data is included, how it is aggregated, freshness, and semantic interpretation. It lacks explicit permission or rate-limit info (no annotations), but the absence of an output schema makes the detailed row-field listing more valuable. Slightly incomplete in operational details but strong overall.

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 there is no parameter semantics to explain. The baseline for 0 parameters is 4, and the description adds no unnecessary parameter details, instead focusing on the response structure, which is appropriate given the lack of an output 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 identifies the tool as a stock-settlement counterparty graph, specifying the exact action (which wallets move tokenized equities to whom), the context (off-pool), and the aggregation details (per wallet with counterparty addresses, notional, concentration, etc.). This distinguishes it from sibling tools like get_equity_flows or get_bridge_flow, which focus on different data dimensions.

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 analyzing off-pool tokenized equity settlement relationships, but it does not explicitly state when to prefer this tool over alternatives or provide exclusions. Sibling tools are not referenced, leaving the agent to infer the niche from the detailed purpose statement.

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 explains the underlying detection mechanism (onchain transfer ratio at a specific block), clarifies that a change signifies a corporate action, and warns that it is not from an announcement feed. This goes beyond the tool name but does not cover potential edge cases like rate limits or data availability.

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, each adding essential information. It front-loads the primary purpose and uses no filler words. It also earns its length by explaining the tool's unique relevance.

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 (current value, previous value, and change time) and its significance. It could add details on data format or units, but the description is complete enough for an agent to invoke and interpret the result.

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 meaningful context about what data is returned (current, previous, time of change) without needing to explain any parameters.

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 ERC-8056 ui-multiplier per tokenized stock along with previous value and change time. It distinguishes itself from siblings by focusing on corporate actions (splits/dividends) and explicitly mentions pairing with the robinhood-chain-kit npm package.

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 use case: 'Essential for interpreting historical prices and share counts across a split.' It also clarifies that the multiplier change is detected from onchain transfer ratio at a specific block, not from an announcement feed, which helps avoid misuse. However, it does not explicitly name alternative tools or when not to use this one.

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 excels. It discloses data-source behaviors (live market only during regular session, oracle frozen at close), the semantics of premiumPct versus driftPct, and the envelope's own clock for session verdict. This is rich, non-obvious 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?

The description is a compact paragraph of five sentences, each adding value: it introduces the three-way pricing, defines both metrics with caveats, and differentiates from siblings. No fluff; the most important facts are 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?

Given the absence of an output schema, the description effectively explains the return content: both metrics, the session verdict field, and the meaning of the values. It also provides enough context about data sources and timing to make the tool usable without further documentation.

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 per rubric. The description adds no parameter information because none exists, but the input schema already confirms this. No deduction 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 clearly states the tool returns pricing for tokenized stocks with three price sources (DEX, live market, oracle), and explains the key metrics premiumPct and driftPct. It explicitly differentiates from siblings by noting 'No other index on this chain has the live-market leg.'

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: premiumPct is 'only meaningful while the session is open' and driftPct represents 'expected off-session drift, never an arbitrage claim.' This tells users when each metric is appropriate and warns against misinterpretation, while the sibling differentiation clarifies when to choose 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?

No annotations provided, so description carries full burden. It discloses the collision resolution policy (canonical Robinhood token wins) and lists the data returned. Missing details on error handling or rate limits, but still transparent about core 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?

Two sentences, front-loaded with purpose and contents, then collision handling. No wasted 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?

Given no output schema and moderate complexity, the description lists all key return components (snapshot, trend, intel, whale transfers) and handles collision. Missing possible error responses, but adequate for agent 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?

With 0% schema description coverage, the description adds critical meaning: it explains the format (symbol or hex address) and provides examples (TSLA, CASHCAT). This fully compensates for the schema gap.

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 ('get full detail for one token') and resource ('by symbol or 0x address'), clearly stating the scope and content (snapshot, trend, intel, whale transfers). It differentiates from siblings by specifying it returns comprehensive data, unlike specialized tools like check_token_risk.

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 clearly indicates when to use (for comprehensive token detail), but does not explicitly exclude cases or mention alternatives like other sibling tools. However, the context of being a single-token lookup 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?

With no annotations provided, the description carries the transparency burden. It discloses key behavioral details: supported intervals (5m, 1h, 1d), lookback limitations (up to 48h for 5m, full history for 1d), and that each bar includes a source field (dex/oracle). These details are beyond what the schema provides.

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 compact and front-loaded, containing only two sentences. The first sentence defines the core purpose, the second covers intervals/limitations and output detail (source). Every word adds value, 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 modest complexity (3 parameters, no output schema) and no annotations, the description sufficiently covers the purpose, data scope, and a key output attribute. However, it could be more explicit about the return format (e.g., array of candles) or default behavior for 1d with hours set, leaving some ambiguity.

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 description adds crucial meaning to the symbol_or_address parameter by stating it accepts a symbol or 0x address, which the schema does not describe. It also explains the relationship between hours and interval (e.g., 5m limited to 48h), supplementing the schema's generic descriptions and covering the 67% schema coverage gap.

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 returns OHLCV candles for one token, identified by symbol or 0x address, and specifies the intervals (5m, 1h, 1d) and lookback windows. This specific verb+resource+scope distinguishes it from siblings like get_token or search_tokens, which serve different data purposes.

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 clear context for when to use the tool: when OHLCV candle data for a token is needed, matching the terminal charts. It does not explicitly mention alternatives or exclusions, but the purpose is unambiguous given the sibling set contains no other candle 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?

No annotations provided, so description must carry the burden. It explains the scoring mechanism (z-scores vs 7d baseline) and lists returned fields (price, volume, holders, trajectory). This adds meaningful behavioral context beyond the input 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?

Two concise sentences front-loading the main purpose with no wasted 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?

Given the tool's simplicity (one optional parameter, no output schema), the description adequately covers what it does and what it returns. Slightly incomplete in not explicitly stating descending ordering, but implied by 'ranked'.

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 only parameter (limit), so baseline is 3. Description does not add extra meaning for the parameter beyond what schema already provides, though it gives context about output fields.

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 returns ranked trending tokens on Robinhood Chain by trendScore, which is a specific verb and resource. It distinguishes from sibling tools like get_launch_radar or get_token by focusing on trending status and the Robinhood Chain.

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 versus alternatives. Sibling tools exist but no comparisons or conditions are given.

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 burden of behavioral disclosure. It does disclose the search types and expected return fields (price, change, holders, mcap, liquidity), but it omits details like whether it is strictly read-only, rate limits, or result sorting. The description is not misleading, but it leaves some behavioral aspects understated given the absence of annotations.

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, well-structured sentence that front-loads the action and resource. It includes the search scope and return fields without unnecessary fluff, making every word 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?

Given no output schema, the description appropriately lists the returned fields (live price, 24h change, holders, mcap, liquidity) and the search mechanism. It does not explain edge cases like zero results or exact matching behavior, but for a simple search tool with two parameters, it covers the essential contextual needs for an agent to decide 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 only 50%, with 'query' lacking a schema description. The description compensates by explaining that the query can be a symbol, name, or address fragment. The 'limit' parameter is already described in the schema, so the description adds the necessary semantic meaning for the otherwise undocumented parameter.

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 'Search' and the resource 'tokens on Robinhood Chain' with explicit search dimensions (symbol, name, 0x address fragment). It also distinguishes itself from sibling tools like get_token by indicating it returns search matches with multiple fields, 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?

The description gives clear context that this is the same search used in the terminal's popup, implying a general token search. However, it does not explicitly name alternatives or state when not to use it, so it lacks explicit exclusions but still provides enough situational context.

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