tnt-house-risk-data-api

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

With no annotations, the description must carry the full burden. It clearly describes what is returned, but it does not explicitly state whether the operation is strictly read-only, any permissions required, or potential delays. The verb 'Returns' implies safety but leaves some behavioral details unspecified.

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, front-loaded with the returned data, and ends with a clear usage instruction. Every word adds value with no redundancy or fluff.

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 single-parameter tool with no output schema or annotations, the description lists all key outputs and provides usage context. It doesn't describe return format or error behavior, but given the tool's simplicity, the description is largely 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?

Schema description coverage is 100%, meaning the only parameter 'mint' is fully described in the schema. The description adds no extra parameter semantics beyond what the schema already provides, 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 uses a specific verb ('Returns') and clearly identifies the resource (a single Solana token mint) while enumerating the exact outputs (safety score, insider clusters, authority status, holder concentration, price/liquidity/volume). This makes it distinct from siblings like batch or history 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?

It gives a clear usage context: 'Use before recommending or executing a trade on any Solana token.' It does not explicitly state when not to use it or name alternatives, but the wording implies single-token checks versus batch/history tools. Clear context with no exclusions.

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 discloses important operational behavior: per-mint billing (N mints as N calls), no bulk discount, and all-or-nothing rejection if quota/credit is insufficient. This goes beyond typical descriptions, though it doesn't cover other potential error conditions or return shape.

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 dense sentences deliver all essential information without excess. The front-loaded reference to check_token_risk establishes context immediately, and every phrase adds value.

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

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

Given the moderate complexity, the description covers purpose, usage, pricing, and failure mode. It relies on the sibling tool for return-format context, which is acceptable since that sibling context is provided; still, it could be slightly richer if no sibling reference existed.

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 documents the mints parameter (1-25 addresses). The description adds valuable economic semantics—N mints charged as N calls—clarifying how the array length impacts cost. This supplements the schema without contradicting it.

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 the tool checks risk for multiple Solana tokens at once, up to 25 mints, and explicitly frames it as 'Same as check_token_risk but for up to 25 mints', clearly distinguishing it from the single-token sibling.

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 conveys when to use this tool vs. check_token_risk via the 'Same as... but for up to 25 mints' phrasing, implying batch use cases. It also provides usage caveats like no bulk discount and all-or-nothing rejection, but does not explicitly name alternatives or exclusion criteria.

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

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

No annotations are provided, so the description carries the full burden. It discloses that this is a pure read from stored history, does not make a live upstream call, and does not count against quota. These are useful behavioral traits beyond a basic read operation.

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 key data fields and time range. Every clause adds value—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?

For a read-only tool with no output schema, the description lists the returned data points, the time range, and quota behavior. This is sufficient for an agent to decide whether to invoke it, though the return structure is not detailed.

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 parameters are well-documented. The description adds minimal extra meaning by listing the returned data fields and confirming the max days of 90, but it does not compensate beyond what the schema already provides.

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 ('Returns') and clearly identifies the resource ('hourly historical data points... for a mint over the last N days'). It distinguishes from siblings by emphasizing historical trend data versus current risk checks.

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 historical trend analysis and notes it does not count against quota, but it does not explicitly state when to use this tool versus check_token_risk or check_token_risk_batch. The alternative tools are not mentioned.

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