Per-transaction crypto trade validator for AI agents. Returns deterministic PROCEED / CAUTION / BLOCK verdicts using WaveGuard anomaly detection, history checks, and rug-pull risk analysis.
Stateless what-if sensitivity analysis. Submit baseline + counterfactual variants in one request and receive tipping-point guidance.
| Name | Required | Description | Default |
|---|---|---|---|
| training | Yes | Reference normal samples. | |
| base_test | Yes | Baseline sample under evaluation. | |
| field_level | No | 0=real field, 1=complex field (default: 1). | |
| sensitivity | No | Anomaly sensitivity (default: 1.0). | |
| counterfactual_tests | Yes | What-if variants of the baseline sample. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds 'Stateless' and the notion of submitting all variants in one request, which are behavioral traits not captured in the annotations. The annotations already provide readOnly, idempotent, and openWorld hints, so the description contributes extra context about statelessness and output behavior (tipping-point guidance). No contradiction.
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 concise sentences that front-load the core purpose ('Stateless what-if sensitivity analysis') and then detail the request pattern and outcome. Every word earns its place with no 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 partially compensates by stating it returns 'tipping-point guidance,' which gives an idea of the output's nature. However, it doesn't detail the structure of that guidance (e.g., numeric thresholds, text). For a 5-param tool with clear schema descriptions, this is reasonably complete but could be slightly more explicit about the response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description confirms the roles of base_test and counterfactual_tests by saying 'Submit baseline + counterfactual variants', but it does not add syntax, formatting, or additional parameter semantics beyond the schema. The schema descriptions are already clear.
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 ('Submit') and resource ('baseline + counterfactual variants') and explains the outcome ('tipping-point guidance'). It distinguishes this tool from siblings like cryptoguard_validate_trade by focusing on what-if sensitivity analysis rather than general validation.
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 sensitivity analysis when you have a baseline and counterfactual variants, but it does not explicitly state when to use it over alternatives or mention exclusions. The tool name and 'counterfactual' provide context, but there's no explicit guidance on choosing this over validate_trade or validate_trade_plus.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check CryptoGuard API health, version, and service status. No payment required. Use this to verify the service is running.
| Name | Required | Description | Default |
|---|---|---|---|
| verbose | No | Return detailed health info including uptime and version details (default: false). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds 'No payment required' and specifies what is checked (health, version, status), providing useful context 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 description is concise with two short sentences. The first states the core purpose, and the second adds usage guidance. No unnecessary words or repetition.
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 simple health check tool with one optional parameter, strong annotations, and no output schema, the description sufficiently covers purpose, usage, and constraints. Nothing essential is missing.
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 100% coverage, including a description of the 'verbose' parameter. The tool description adds no additional parameter guidance, so a 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 checks CryptoGuard API health, version, and service status, with a specific verb and resource. It is easily distinguished from sibling tools that handle trade validation, token scanning, and searches.
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 explicitly says to use this to verify the service is running, providing clear usage context. It does not mention alternatives, but the health check is unique among siblings, so this is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Assess rug pull risk for a specific DEX trading pair. Scores 6 risk factors (0-100): liquidity depth, pair age, volume/liquidity ratio, price action, buy/sell imbalance, and metadata.
Example: check if a new Solana pair is a potential rug pull.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | Yes | Blockchain (solana, ethereum, base, bsc). | |
| pair_address | Yes | DEX pair contract address. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, establishing safety. The description adds behavioral detail by specifying the six risk factors and the 0-100 scoring scale, which informs the agent what the tool will compute. No contradictions with 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?
Two concise sentences: the first states the purpose and output details, the second gives a practical example. No wasted words, and the most important information is 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?
With strong annotations, complete schema, and a description that names the six risk factors and scoring range, the tool is adequately specified for selection and invocation. The lack of an output schema is partially compensated by the description's reference to 0-100 scores, though exact return structure is not specified.
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%, with both 'chain' and 'pair_address' already described. The description's example ('new Solana pair') reinforces the parameters but does not add substantive new meaning beyond the schema, 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 clearly states the tool's purpose: 'Assess rug pull risk for a specific DEX trading pair.' It identifies the resource (DEX pair) and the specific action (rug risk assessment), and the list of six risk factors differentiates it from siblings like validate_trade or scan_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 provides clear context: it is used to evaluate a DEX pair's rug pull risk, with an explicit example ('check if a new Solana pair is a potential rug pull'). It does not name alternatives or state when not to use it, but the use case is sufficiently clear from the tool's focus.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan a single token for anomalous market behavior using WaveGuard physics-based anomaly detection. Compares the token to TIER-MATCHED peers (microcaps vs microcaps, large-caps vs large-caps). Returns anomaly scores, risk level, and explanations.
Example: scan 'solana' to check if its metrics are unusual.
| Name | Required | Description | Default |
|---|---|---|---|
| coin_id | Yes | CoinGecko coin ID (e.g., 'bitcoin', 'solana', 'pepe'). | |
| sensitivity | No | Anomaly sensitivity multiplier (default: 1.0). Higher = more sensitive. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and destructiveHint annotations, the description adds meaningful behavioral context: it performs peer-tier comparison and returns anomaly scores, risk level, and explanations. This goes beyond the safety profile already declared by annotations, giving the agent a clearer picture of the tool's operation and output.
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 highly concise, consisting of two sentences plus an example. Every sentence contributes meaningful information: the core functionality, the tier-matching behavior, and the output summary. No fluff or redundancy.
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 there is no output schema, the description compensates by listing the return types (anomaly scores, risk level, explanations). It adequately explains the input and the comparison logic, making the tool understandable for a read-only scan operation. It lacks details on rate limits or edge cases, but for this complexity level, it is 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?
The input schema already covers both parameters with clear descriptions (coin_id and sensitivity). The description enhances this by providing a concrete example ('scan solana') that demonstrates parameter usage in context, adding value beyond the schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans a single token for anomalous market behavior using a specific method (WaveGuard physics-based anomaly detection). It also distinguishes itself from sibling tools by specifying tier-matched peer comparison, making it unique among the listed siblings.
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 example 'scan solana' provides a concrete use case, making it clear when to use the tool. It implies the tool is for checking anomaly metrics on a single token, but it does not explicitly contrast with alternatives like rug_check or validate_trade, so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for a token's CoinGecko coin ID by name, symbol, or contract address. Use this first if you're unsure of the correct coin_id for scan_token or validate_trade.
Example: search 'pepe' to find the correct coin ID.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Token name, symbol, or contract address to search. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds useful behavioral context about returning CoinGecko coin IDs and demonstrates usage with an example, going 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 description is extremely concise, with purpose, usage guidance, and an example in just two sentences. No redundant information; every sentence serves a clear function.
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 simple search tool with a single parameter and no output schema, the description is fully adequate. It covers purpose, usage context, and example, while annotations cover safety. It's complete for an agent to select and invoke 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?
Schema description coverage is 100%, so the parameter is already well-documented. The description adds marginal value by clarifying the search purpose and providing an example, but doesn't uniquely enrich the parameter semantics beyond the 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's function: searching for a token's CoinGecko coin ID by name, symbol, or contract address. It also distinguishes itself from sibling tools by explicitly referencing scan_token and validate_trade as tools that require this coin_id.
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 when-to-use guidance: 'Use this first if you're unsure of the correct coin_id for scan_token or validate_trade.' This names alternatives and gives clear usage context, plus an example for added clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate a crypto trade BEFORE execution. Returns a verdict: PROCEED, CAUTION, or BLOCK. Runs 5 checks: peer anomaly scan via WaveGuard physics engine, self-history comparison, rug pull risk assessment, CEX/DEX price cross-check, and concentration risk analysis. Accepts token name, symbol, or contract address.
Example: validate buying $500 of PEPE before executing.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | Blockchain for contract address resolution (ethereum, solana, base, bsc, polygon, avalanche, arbitrum). | |
| token | Yes | Token to validate. Can be a name (bitcoin), symbol (BTC), or contract address (0x...). | |
| action | No | Trade action type (default: buy). | |
| amount_usd | No | Trade amount in USD for concentration risk analysis. | |
| pair_address | No | DEX pair address for rug pull check. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds a concrete behavioral model: five named checks and a tri-state verdict. It also implies no state mutation by focusing on pre-execution validation. No contradiction with annotations found.
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 concise sentences plus a one-line example, with the core purpose stated first. It lists the five checks efficiently and the example illustrates usage without redundancy. Every word contributes informational value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with five parameters and no output schema, the description gives enough context to select and invoke it correctly: it explains the verdict output, the checks performed, and the accepted token formats from the schema. It could be slightly stronger by noting that certain parameters like pair_address are conditionally used for one of the five checks, but the overall picture is 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?
The input schema provides 100% parameter descriptions, so the baseline is 3. The description adds a concrete example ('buying $500 of PEPE') that maps token and amount_usd, but it doesn't explain parameter-specific formats or dependencies (e.g., that pair_address is needed for the rug pull check) beyond the 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 opens with a specific verb ('Validate') and resource ('a crypto trade'), immediately distinguishing this as a pre-execution validation tool. It enumerates the five checks and names the output verdict (PROCEED/CAUTION/BLOCK), clearly differentiating it from the rug_check sibling and signaling that this is the comprehensive trade validation tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The instruction to 'validate BEFORE execution' gives clear usage context, and the example ('before executing') reinforces the intended timing. However, it doesn't explicitly exclude alternatives like rug_check for specialized checks or mention validate_trade_plus for potentially deeper validation, so it stops short of a full when/when-not guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Premium stateless decision bundle for one-shot trade gating. Caller provides full context (training/test + optional what-if blocks) and receives verdict, risk score, instability/mechanism signals, and recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| test | Yes | Trade candidate sample to evaluate. | |
| sequence | No | Optional ordered sequence for trajectory/regime checks. | |
| training | Yes | Reference normal samples. | |
| field_level | No | 0=real field, 1=complex field (default: 1). | |
| sensitivity | No | Anomaly sensitivity (default: 1.0). | |
| intervention_tests | No | Optional intervention variants for mechanism probing. | |
| intervention_labels | No | Optional labels for intervention tests. | |
| counterfactual_tests | No | Optional what-if variants for sensitivity analysis. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, establishing a safe, non-mutating, idempotent profile. The description adds that the tool is 'stateless' and 'one-shot', and enumerates output signals (verdict, risk score, instability/mechanism signals, recommendation). However, it does not describe validation requirements, error handling, or limitations beyond what annotations cover.
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 consists of two sentences totaling about 28 words. It front-loads the core purpose and then succinctly lists inputs and outputs. Every clause contributes information, with no filler or redundancy.
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 complexity (8 parameters, no output schema), the description provides a useful high-level overview but omits detailed return structure and fails to position the tool relative to sibling 'cryptoguard_validate_trade'. The output components (verdict, risk score, etc.) are named but not elaborated, leaving interpretation 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?
Schema description coverage is 100%, with each of the 8 parameters having a description. The tool description groups inputs into 'training/test + optional what-if blocks', which is a high-level clustering but does not add new meaning beyond the schema. No additional parameter-level semantics are provided.
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's purpose: a 'stateless decision bundle for one-shot trade gating' that produces a verdict, risk score, signals, and recommendation. It is a specific and informative statement of function. However, it does not explicitly differentiate from the sibling 'cryptoguard_validate_trade', relying on the 'Premium' label and extended output signals to imply a 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 does not provide when-to-use or when-not-to-use guidance. It mentions 'one-shot' and 'full context' but never references alternative tools like 'cryptoguard_validate_trade' or 'cryptoguard_counterfactual_trade'. The 'Premium' adjective implies a more comprehensive option, but without explicit comparisons, the agent lacks direction on selecting this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Several tools overlap in purpose: validate_trade and validate_trade_plus both validate trades, while scan_token and rug_check are bundled into validate_trade's checks. This creates ambiguity about which tool to use for a given task, especially for agents trying to distinguish the premium versus standard validation flow.
All tool names share the 'cryptoguard_' prefix and snake_case, but the structure varies: some are verbs (search, scan_token, validate_trade), while others are nouns or adjective-noun combinations (health, rug_check, counterfactual_trade). The inconsistency in verb usage makes the set feel less predictable, though the prefix provides a unifying element.
Seven tools is within the typical well-scoped range for a crypto risk assessment server. The count is reasonable, but the overlap between validate_trade and validate_trade_plus suggests one could arguably be consolidated, making the set slightly redundant.
The server covers the core domain well: health checking, token search, rug pull assessment, anomaly scanning, trade validation, and counterfactual analysis. Minor gaps exist (e.g., no batch processing or explicit market data endpoints) but the main workflows for trade validation and risk assessment are represented.