Dexra

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

Annotations already cover the safety profile (readOnly, idempotent, open-world), yet the description adds material context they don't: per-call cost (0.020 USDC), subscription inclusion, and the specific metric set returned. It doesn't discuss rate limits, latency, or failure modes for an invalid pool, so it stops short of a 5.

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?

A single dense sentence front-loads the supported venues and the output set, with pricing placed last where it belongs. The enumerated metric list is long but every item earns its place; 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?

With no output schema, the description carries the burden of telling the agent what comes back, and it does list the returned metrics, venues, and cost. Only return format/shape and error behavior remain unspecified, which is a minor gap for a read-only analytical call.

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 both parameters are already documented with type, bounds, and default. The description ties width to the analytical outputs (time-in-range, rebalances) but adds no format or syntactic detail beyond the schema, matching the baseline for full-coverage schemas.

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?

Names the exact domain (Raydium CLMM, Orca Whirlpool, Meteora DLMM) and enumerates the specific outputs: liquidity by price, time-in-range over 7/30 days, fee yield, impermanent loss, and rebalance count. This distinguishes it cleanly from siblings like pool_health, market_snapshot, and token_deep_check.

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 title and description frame the use case ("which range to hold"), which implies a range-selection decision context, but no explicit when-to-use, when-not-to-use, or named alternative (e.g. pool_health) is given. Usage is inferable but not stated.

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?

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint), but the description adds genuinely useful non-annotated context: this is free while the pointed-to tools are paid. The main gap is that it doesn't describe response shape or rate limits, but cost transparency beyond annotations is a real contribution.

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?

Front-loaded and tight, with the 'Call first' guidance leading. However, the cost is stated twice ('Free.' at the start and 'Price: free.' at the end), which is redundant padding in an otherwise efficient description.

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 usefully previews what the return contains (address type, age, activity, basic stats) and provides clear routing. For a single-parameter read tool whose annotations already cover safety, this is close to complete; only pagination/response-format detail is absent.

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?

There is one parameter with 100% schema description coverage ('Any Solana address'), so the schema already carries the meaning. The description adds no syntax or format detail beyond what the schema provides, which is the expected baseline when coverage is 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?

States a specific verb and resource ('describe any Solana address') and enumerates what the caller learns: wallet/program/pool/token classification, age, activity, and basic stats. This is enough to distinguish it from siblings like wallet_check, pool_health, and token_deep_check, which it explicitly names as the follow-on 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?

Gives an explicit sequencing instruction ('Call first with any Solana address') and routes the agent to the alternatives that answer the next question (wallet_check, pool_health, token_deep_check). When-to-use and which-tool-next are both stated rather than inferred.

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?

Annotations already establish readOnly/openWorld/idempotent, so the bar is lower; the description adds genuinely new context by disclosing the external venue coverage and the cost model (0.010 USDC per call, included in subscriptions), which is important pre-invocation information. It stops short of describing determinism of 'cheapest venue', latency, or failure modes when a venue is unreachable.

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?

Front-loaded with the returned data and scope, then a short pricing sentence; every clause carries information. The venue/data enumeration is long but not redundant, 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?

With no output schema, the description carries the burden of describing returns and does so thoroughly (per-venue metrics, divergence, all-in cost, cheapest venue). It omits response shape, handling of unsupported tickers, and the meaning of the size default/maximum, which keeps it from a 5.

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%: symbol is documented ('Ticker such as SOL') but size_usd has no schema description, leaving its unit and semantics partly to the name. The description partly compensates by framing the tool around 'the all-in cost of an order of the given size on each venue', but never states that size_usd is a USD notional, so the 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 names concrete resources (funding, open interest, liquidations, order book depth, execution cost) and the exact scope (Binance, Bybit, OKX, Hyperliquid, Drift, plus Jupiter), so an agent knows this is a cross-venue perp market snapshot. It distinguishes itself from siblings like pool_health or swap_preflight by content, though it never references them by name.

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?

Usage is implied by the data enumerated — you call this when you need cross-venue funding/OI/execution-cost comparison for a ticker at a given order size. There is no explicit when-to-use, when-not-to-use, or named alternative (e.g. swap_preflight for a single swap), so the agent must infer the routing itself.

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?

Annotations already declare readOnly/idempotent/openWorld, so the bar is lower, yet the description adds real context beyond them: per-call pricing (0.005 USDC), that longer windows cost more, and the concrete payload contents (authority flags, liquidity/lock, creator history, first-buyer funding graph, sniper detection). It does not disclose rate limits, pagination, or what happens when no launches match.

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?

Purpose and data sources are front-loaded, the returned-field list is dense but information-bearing, and the pricing sentence carries non-obvious cost information. No filler or redundant restatement of the name or title.

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 correctly carries the return-value burden by enumerating the fields an agent will receive. It is nearly complete for a read tool, with the only gap being unexplained semantics for the three undocumented input parameters.

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 only 40%: minutes and platforms are documented in the schema, while limit, include_collapsed, and min_liquidity_usd have no descriptions. The description mirrors the platform list and the time window but adds no meaning for the three undocumented parameters, so it only marginally compensates.

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?

States a specific resource (new tokens/pools on Solana), enumerates the exact data sources (pump.fun, LetsBonk, Raydium LaunchLab, Meteora DBC, etc.), and specifies the look-back scope. An agent can distinguish this from token_deep_check or new_launches_batch by the 'just launched, last N minutes' framing plus the returned data set.

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?

Usage is implied by the 'new launches' framing and the cost hint ('Windows over 60 minutes cost more') steers window selection, but there is no explicit when-to-use/when-not guidance or routing to siblings such as new_launches_batch, token_deep_check, or pool_health.

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?

Annotations already cover the safety profile (readOnly, idempotent, openWorld), so the description correctly focuses on what annotations cannot express: per-call pricing (0.015 USDC, subscription-included) and the 50-mint batch ceiling. It omits partial-failure or per-mint error behavior, which is a real gap for a batch tool but a minor one.

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 that both earn their place: the first defines scope and the sibling relationship, the second delivers cost. Front-loaded and waste-free.

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 read-only batch tool with annotations carrying safety, a named list of the checks performed, pricing, and the batch limit, the description is close to complete. Only batch-level failure semantics are unaddressed, and with no output schema present that is a modest omission.

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 0%, so the description must compensate, and it does: it clarifies that mints are Solana token mints the caller already holds and that up to 50 may be passed in one call. It adds no format or ordering detail beyond that, but for a single param it is sufficient.

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?

States a specific verb and resource ('launch checks ... for up to 50 Solana token mints') and explicitly anchors to the sibling new_launches, with the batch distinction made clear by 'in one call'. An agent can differentiate it from new_launches and other siblings without opening a schema.

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?

'Mints you already know about' implicitly signals the selection condition versus new_launches, which is presumably for discovery, and the 50-mint cap frames batch use. It gives clear context but stops short of an explicit when-to-use-this-instead-of-new_launches 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?

Annotations already declare readOnly/idempotent/openWorld, so the bar is low; the description goes beyond them by disclosing the pricing model (0.030 USDC per call, included in subscriptions), which is materially useful for deciding whether to invoke. It still omits data freshness/latency and any rate-limit behavior, keeping it short of a 5.

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 dense sentence front-loads the venue coverage then the metric list, and the pricing clause is appended last where it belongs. It is a run-on enumeration with no separators for scanability, but every clause 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?

With no output schema, the description must describe the return payload, and it does so with explicit metric names and time windows (24h flow, 7/30-day IL-adjusted returns). Missing only freshness/timestamp and result-shape details, which are minor for a read-only single-pool lookup.

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?

Single parameter with 100% schema description coverage ('Pool address' plus a base58 pattern), so the schema carries the semantics and a 3 baseline applies. The venue list (Raydium AMM/CPMM/CLMM, Orca Whirlpool, Meteora DLMM, PumpSwap) adds adjacent context about which pools are valid but no extra meaning for the pool argument itself.

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 enumerates a specific, distinctive metric set (depth within 2%, $1k/$10k slippage, IL-adjusted LP returns, bot/sandwich share) that unambiguously identifies this as a per-pool health/yield read for named AMM venues. It lacks an explicit verb and never contrasts itself with siblings such as market_snapshot or clmm_range_stats, so the agent must infer the split.

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?

There is no statement of when to call this versus the many sibling analytics tools, and no preconditions beyond the pool address already captured in the schema. The only situational hint is the cost line, which informs budget decisions rather than selection.

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?

Annotations already declare readOnlyHint, openWorldHint and idempotentHint, so safety and idempotency are covered. The description adds that the data is free and mentions 'daily drift', hinting at a time-series return, but discloses nothing about rate limits, auth, or how flags are defined. Adequate but thin beyond the 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 core sentence is dense and informative, but it opens with a wasted fragment ('Free.') and closes by repeating the same idea ('Price: free.'), so pricing bookends the description while the substantive content sits in the middle. Two redundant clauses cost it structure points.

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 carries the full burden of describing returns, and it does list the fields reported (rates, base rate, sample size, window, drift). It still leaves ambiguity about format, the scale/meaning of 'daily drift', and how the days parameter shapes output. Reasonably complete for a read-only stats tool but not fully so.

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 'days' parameter, whose bounds and default are already documented in the schema. The phrase 'test window' loosely corresponds to it but adds no format or behavior detail beyond what the schema provides. 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?

The description states a specific resource — reported accuracy of the service's published launch risk flags — and enumerates its content (collapse rate within 24h vs base rate, sample size, window, drift). This is far clearer than the vague name 'score_performance'. It does not, however, explicitly differentiate itself from the similarly-named sibling 'service_stats'.

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?

There is no statement of when to reach for this tool versus siblings such as service_stats or token_deep_check, nor any prerequisites or exclusions. The only operative guidance is pricing, which does not tell an agent when to invoke it.

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?

Annotations already declare readOnly, idempotent and openWorld, so the safety profile is covered. The description adds the fact that it is free and specifies the 24h/7d/30d aggregation windows, which is genuinely useful, but says nothing about latency, rate limits or freshness beyond the windows.

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?

Front-loads the metrics list, which is good, but wastes tokens by stating 'Free.' at the start and 'Price: free.' at the end — the same information twice. One of those clauses should be dropped.

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 parameters and no output schema, the description carries the return-value burden and does it reasonably well by enumerating the metrics and time windows. It is nearly complete for a zero-arg stats tool; only freshness/caching behavior is unstated.

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 takes zero parameters, so the baseline is 4. The description correctly describes the only meaningful 'input' dimension an agent needs to know — the fixed time windows the stats are aggregated over.

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?

States the resource (this service) and enumerates exactly what is returned: calls, unique payers, paid volume, uptime, latency. An agent can tell it apart from siblings like pool_health or token_deep_check, though the verb ('get/read') is implied rather than 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?

Gives a clear trigger condition — 'Check before paying' — which tells the agent when this read-only reputation check belongs in a workflow. It does not name alternatives or exclusions, so it stops 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?

Annotations already cover the safety profile (readOnly, idempotent, openWorld), so the bar is lower, and the description adds genuinely new context: per-call pricing (0.005 USDC, included in subscriptions) and the enumerated verdict values with reasons. It does not explain latency, freshness of "our history," or failure behavior for unknown mints.

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 the signal list, and the pricing sentence is short and useful. The opening is dense with a comma-heavy list but nothing is redundant or padding.

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 must convey return content, and it does: the four risk signals plus the verdict enum values and "with reasons." It omits how thresholds map to verdicts and what happens on unsupported tokens, but an agent has enough to invoke and interpret it.

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 75%, with mint, quote, and amount already documented in the schema, so the schema carries most of the load. The description adds no parameter-level detail — it never restates that quote defaults to SOL or clarifies buy-vs-sell unit conventions — so the 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?

The description names a specific operation (pre-swap safety preflight on Solana) and enumerates the exact signals returned: price impact, sandwich share, Token-2022 transfer fees/hooks, freeze authority, and a verdict. This clearly separates it from siblings like token_deep_check or pool_health, which do not bundle a swap verdict.

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?

"Before a Solana swap" gives a clear, explicit trigger condition for when to call it. However, it never names an alternative tool or states when not to use this one (e.g., for non-swap token safety, use token_deep_check), so the routing guidance stops short of full coverage.

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?

Annotations already declare readOnlyHint, openWorldHint and idempotentHint, so safety profile is covered. The description adds real value beyond them: it discloses the pricing model ('0.030 USDC per call, included in subscriptions') and the analytical basis (snapshot history). It omits rate limits and any freshness/latency notes.

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?

Front-loaded with the use case and then the capability list, which is efficient. The comparative sentence about RugCheck is slightly promotional, but it still earns its place as differentiation; overall it is a single dense pass with little waste.

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 carries the return-value burden and does describe the deliverable (graph findings, concentration, dynamics, blacklist proximity, verdict). Combined with full annotation coverage of the read-only/idempotent profile, an agent has enough to invoke it correctly, though latency/rate context is absent.

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 required parameter (mint) with 100% schema description coverage, so the schema fully documents the input. The description adds no format or validation guidance for the mint beyond what the schema pattern provides, making the baseline 3 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?

Names a specific verb and resource ('see who really holds it' for a Solana token) and enumerates the concrete outputs: shared-funding linkage among top holders, merged-wallet concentration, holder/liquidity dynamics, blacklist proximity, and a verdict. This clearly separates it from siblings like token_holders_delta or market_snapshot.

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?

States explicit context for use: 'before buying or holding a Solana token'. It also positions itself against a known alternative ('Goes deeper than a single safety score such as RugCheck's'), though it does not name any sibling tool or state when NOT to use it.

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?

Annotations already declare readOnly, idempotent and openWorld, so the safety profile is covered. The description adds real value beyond them: the data source ('from our snapshots'), the time windows, and pricing (0.030 USDC per call, included in subscriptions). It omits any note on rate limits or freshness of snapshots.

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?

A single front-loaded sentence that leads with the usage condition and packs the return content efficiently. Slightly dense but nothing is wasted; the pricing clause is appended cleanly.

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 carries the return-shape burden and does so reasonably by enumerating new/exited holders, concentration shift and large-holder selling. Combined with read-only annotations and full param coverage, it is nearly complete, though exact metric definitions remain unspecified.

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 (mint) and schema description coverage is 100%, so the schema already documents it. The description implies the token context but adds no format or constraint detail 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?

States a specific resource (top holder changes for a Solana token) and scope (1h and 24h windows, new/exited holders, concentration shift, large holders selling). An agent knows exactly what analysis this produces, though it does not explicitly distinguish itself from sibling token_deep_check.

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?

Opens with an explicit use-case framing ('Use to see whether insiders are leaving a Solana token'), giving clear context for selection. It does not name alternatives or say when NOT to use it versus token_deep_check or market_snapshot, 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?

Annotations already cover safety (readOnly, idempotent, openWorld), so this is graded on added context. The description goes beyond them by disclosing cost mechanics (0.030 USDC per call, included in subscriptions) and analytical depth ('up to two hops' for OFAC/hack linkage), which materially affects agent decisions on batching and budget.

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, no filler, and the core deliverable is front-loaded before the cost caveat. Every clause carries information about either scope or price.

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 compensates by enumerating the verdict components (exchange status, age, activity, bot/human, PnL on launches, exchange funding, OFAC/hack links). It also discloses cost. Only the response packaging/shape is left unstated, which is minor for a single-address lookup.

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 and schema description coverage is 100%, so the schema fully documents the address input including its regex pattern. The description adds no format or sourcing nuance beyond that, making 3 the appropriate 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?

States a concrete output (a verdict with reasons) for a specific resource (a Solana address) and enumerates the dimensions checked, so the agent knows exactly what capability this is. It does not, however, differentiate itself from near-neighbors like describe_address or score_performance, which the agent must distinguish on its own.

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 title 'Should I deal with this address' plus the risk-signal list implies the use case (vetting an address before transacting), and the pricing line gives a cost consideration. But there is no explicit when-to-use/when-not guidance and no mention of when describe_address or score_performance would be the better pick.

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